Skip to main content

Advertisement

SpringerLink
Log in

Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review

  • Review Article
  • Published:
Graefe's Archive for Clinical and Experimental Ophthalmology Aims and scope Submit manuscript

Abstract

Purpose

This scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and their predictive accuracies. We also evaluate the integration of bioinformatics with AI tools.

Methods

This scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included.

Results

A total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data.

Conclusion

AI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Rajkomar A, Dean J, Kohane I (2019) Machine Learning in Medicine. N Engl J Med 380:1347–1358. https://doi.org/10.1056/NEJMra1814259

    Article  PubMed  Google Scholar 

  2. Yu K-H, Beam AL, Kohane IS (2018) Artificial intelligence in healthcare. Nat Biomed Eng 2:719–731. https://doi.org/10.1038/s41551-018-0305-z

    Article  PubMed  Google Scholar 

  3. Pucchio A, Del Papa J, de Moraes FY (2022) Artificial intelligence in the medical profession: ready or not, here AI comes. Clinics 77:100010. https://doi.org/10.1016/j.clinsp.2022.100010

    Article  PubMed  PubMed Central  Google Scholar 

  4. Tong Y, Lu W, Yu Y, Shen Y (2020) Application of machine learning in ophthalmic imaging modalities. Eye Vis 7:22. https://doi.org/10.1186/s40662-020-00183-6

    Article  Google Scholar 

  5. Du XL, Li WB, Hu BJ (2018) Application of artificial intelligence in ophthalmology. Int J Ophthalmol 11:1555–1561. https://doi.org/10.18240/ijo.2018.09.21

    Article  PubMed  PubMed Central  Google Scholar 

  6. Pomyen Y, Wanichthanarak K, Poungsombat P et al (2020) Deep metabolome: Applications of deep learning in metabolomics. Comput Struct Biotechnol J 18:2818–2825. https://doi.org/10.1016/j.csbj.2020.09.033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Csősz É, Tóth N, Deák E et al (2018) Wound-Healing Markers Revealed by Proximity Extension Assay in Tears of Patients following Glaucoma Surgery. Int J Mol Sci 19:4096. https://doi.org/10.3390/ijms19124096

    Article  PubMed  PubMed Central  Google Scholar 

  8. Buisset A, Gohier P, Leruez S et al (2019) Metabolomic Profiling of Aqueous Humor in Glaucoma Points to Taurine and Spermine Deficiency: Findings from the Eye-D Study. J Proteome Res 18:1307–1315. https://doi.org/10.1021/acs.jproteome.8b00915

    Article  CAS  PubMed  Google Scholar 

  9. Han G, Wei P, He M et al (2020) Metabolomic Profiling of the Aqueous Humor in Patients with Wet Age-Related Macular Degeneration Using UHPLC-MS/MS. J Proteome Res 19:2358–2366. https://doi.org/10.1021/acs.jproteome.0c00036

    Article  CAS  PubMed  Google Scholar 

  10. Beutgen VM, Perumal N, Pfeiffer N, Grus FH (2019) Autoantibody Biomarker Discovery in Primary Open Angle Glaucoma Using Serological Proteome Analysis (SERPA). Front Immunol 10. https://doi.org/10.3389/fimmu.2019.00381

  11. Sharma S, Bollinger KE, Kodeboyina SK et al (2018) Proteomic Alterations in Aqueous Humor From Patients With Primary Open Angle Glaucoma. Investig Opthalmology Vis Sci 59:2635. https://doi.org/10.1167/iovs.17-23434

    Article  CAS  Google Scholar 

  12. Gao Y, Teo YCK, Beuerman RW et al (2020) A serum metabolomics study of patients with nAMD in response to anti-VEGF therapy. Sci Rep 10:1341. https://doi.org/10.1038/s41598-020-58346-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Duong V-A, Ahn J, Han N-Y et al (2021) Proteomic Analysis of the Vitreous Body in Proliferative and Non-Proliferative Diabetic Retinopathy. Curr Proteomics 18:143–152. https://doi.org/10.2174/1570164617666200302101442

    Article  CAS  Google Scholar 

  14. Li M, Li H, Jiang P et al (2014) Investigating the pathological processes of rhegmatogenous retinal detachment and proliferative vitreoretinopathy with metabolomics analysis. Mol Biosyst 10:1055. https://doi.org/10.1039/c3mb70386j

    Article  CAS  PubMed  Google Scholar 

  15. Anton Apreutesei N, Tircoveanu F, Cantemir A et al (2018) Predictions of ocular changes caused by diabetes in glaucoma patients. Comput Methods Programs Biomed 154:183–190. https://doi.org/10.1016/j.cmpb.2017.11.013

    Article  PubMed  Google Scholar 

  16. Adav SS, Wei J, Qian J et al (2019) Aqueous humor protein dysregulation in primary angle-closure glaucoma. Int Ophthalmol 39:861–871. https://doi.org/10.1007/s10792-018-0885-3

    Article  PubMed  Google Scholar 

  17. Iomdina EN, Tikhomirova NK, Bessmertny AM et al (2020) Alterations in proteome of human sclera associated with primary open-angle glaucoma involve proteins participating in regulation of the extracellular matrix. Mol Vis 26:623–640

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Page MJ, McKenzie JE, Bossuyt PM et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. https://doi.org/10.1136/bmj.n71

    Article  PubMed  PubMed Central  Google Scholar 

  19. Tricco AC, Lillie E, Zarin W et al (2018) PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med 169:467–473. https://doi.org/10.7326/M18-0850

    Article  PubMed  Google Scholar 

  20. Grant MJ, Booth A (2009) A typology of reviews: an analysis of 14 review types and associated methodologies. Heal Inf Libr J 26:91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x

    Article  Google Scholar 

  21. Alabdulwahhab KM, Sami W, Mehmood T et al (2021) Automated detection of diabetic retinopathy using machine learning classifiers. Eur Rev Med Pharmacol Sci 25:583–590. https://doi.org/10.26355/eurrev_202101_24615

    Article  CAS  PubMed  Google Scholar 

  22. Cao B, Zhang N, Zhang Y et al (2021) Plasma cytokines for predicting diabetic retinopathy among type 2 diabetic patients via machine learning algorithms. Aging 13:1972–1988. https://doi.org/10.18632/aging.202168

    Article  CAS  Google Scholar 

  23. Barbosa Breda J, Croitor Sava A, Himmelreich U et al (2020) Metabolomic profiling of aqueous humor from glaucoma patients - The metabolomics in surgical ophthalmological patients (MISO) study. Exp Eye Res 201:108268. https://doi.org/10.1016/j.exer.2020.108268

    Article  CAS  PubMed  Google Scholar 

  24. Tang Y, Pan Y, Chen Y et al (2021) Metabolomic Profiling of Aqueous Humor and Plasma in Primary Open Angle Glaucoma Patients Points Towards Novel Diagnostic and Therapeutic Strategy. Front Pharmacol 12:1–12. https://doi.org/10.3389/fphar.2021.621146

    Article  CAS  Google Scholar 

  25. Curnow SJ, Falciani F, Durrani OM et al (2005) Multiplex Bead Immunoassay Analysis of Aqueous Humor Reveals Distinct Cytokine Profiles In Uveitis. Investig Opthalmology Vis Sci 46:4251. https://doi.org/10.1167/iovs.05-0444

    Article  Google Scholar 

  26. Igarashi N, Honjo M, Asaoka R et al (2021) Aqueous autotaxin and TGF-βs are promising diagnostic biomarkers for distinguishing open-angle glaucoma subtypes. Sci Rep 11:1408. https://doi.org/10.1038/s41598-021-81048-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Lynch AM, Wagner BD, Palestine AG et al (2020) Plasma Biomarkers of Reticular Pseudodrusen and the Risk of Progression to Advanced Age-Related Macular Degeneration. Transl Vis Sci Technol 9:12. https://doi.org/10.1167/tvst.9.10.12

    Article  PubMed  PubMed Central  Google Scholar 

  28. Testa MA, Puklin JE, Sherwin RS, Simonson DC (1985) Clinical Predictors of Retinopathy and Its Progression in Patients with Type I Diabetes During CSII or Conventional Insulin Treatment. Diabetes 34:61–68. https://doi.org/10.2337/diab.34.3.S61

    Article  PubMed  Google Scholar 

  29. de Almeida BD, Alborghetti MR, Franco Paes Leme A et al (2020) Tear proteomic profile in three distinct ocular surface diseases: keratoconus, pterygium, and dry eye related to graft-versus-host disease. Clin Proteomics 17:42. https://doi.org/10.1186/s12014-020-09307-5

    Article  CAS  Google Scholar 

  30. González N, Iloro I, Soria J et al (2014) Human tear peptide/protein profiling study of ocular surface diseases by SPE-MALDI-TOF mass spectrometry analyses. EuPA Open Proteom 3:206–215. https://doi.org/10.1016/j.euprot.2014.02.016

    Article  CAS  Google Scholar 

  31. Grus FH, Joachim SC, Sandmann S et al (2008) Transthyretin and complex protein pattern in aqueous humor of patients with primary open-angle glaucoma. Mol Vis 14:1437–1445

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Hysi PG, Khawaja AP, Menni C et al (2019) Ascorbic acid metabolites are involved in intraocular pressure control in the general population. Redox Biol 20:349–353. https://doi.org/10.1016/j.redox.2018.10.004

    Article  CAS  PubMed  Google Scholar 

  33. Ji DY, Park SH, Park SJ et al (2018) Comparative assessment of Graves’ disease and main extrathyroidal manifestation, Graves’ ophthalmopathy, by non-targeted metabolite profiling of blood and orbital tissue. Sci Rep 8:9262. https://doi.org/10.1038/s41598-018-27600-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Xu J, Su G, Huang X et al (2021) Metabolomic Analysis of Aqueous Humor Identifies Aberrant Amino Acid and Fatty Acid Metabolism in Vogt-Koyanagi-Harada and Behcet’s Disease. Front Immunol 12:1–10. https://doi.org/10.3389/fimmu.2021.587393

    Article  CAS  Google Scholar 

  35. Barba I, Garcia-Ramírez M, Hernández C et al (2010) Metabolic Fingerprints of Proliferative Diabetic Retinopathy: An 1 H-NMR–Based Metabonomic Approach Using Vitreous Humor. Investig Opthalmology Vis Sci 51:4416. https://doi.org/10.1167/iovs.10-5348

    Article  Google Scholar 

  36. Blighe K, Gurudas S, Lee Y, Sivaprasad S (2020) Diabetic Retinopathy Environment-Wide Association Study (EWAS) in NHANES 2005–2008. J Clin Med 9:3643. https://doi.org/10.3390/jcm9113643

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Bocca C, Kouassi Nzoughet J, Leruez S et al (2018) A Plasma Metabolomic Signature Involving Purine Metabolism in Human Optic Atrophy 1 ( OPA1)-Related Disorders. Investig Opthalmology Vis Sci 59:185. https://doi.org/10.1167/iovs.17-23027

    Article  CAS  Google Scholar 

  38. Bocca C, Le Paih V, Chao de la Barca JM et al (2021) A plasma metabolomic signature of Leber hereditary optic neuropathy showing taurine and nicotinamide deficiencies. Hum Mol Genet 30:21–29. https://doi.org/10.1093/hmg/ddab013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Han G, Wei P, He M et al (2020) Metabolomic Profiling of the Aqueous Humor in Patients with Wet Age-Related Macular Degeneration Using UHPLC–MS/MS. J Proteome Res 19:2358–2366. https://doi.org/10.1021/acs.jproteome.0c00036

    Article  CAS  PubMed  Google Scholar 

  40. Osborn MP, Park Y, Parks MB et al (2013) Metabolome-Wide Association Study of Neovascular Age-Related Macular Degeneration. PLoS One 8:e72737. https://doi.org/10.1371/journal.pone.0072737

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Shimizu H, Usui Y, Wakita R et al (2021) Differential Tissue Metabolic Signatures in IgG4-Related Ophthalmic Disease and Orbital Mucosa-Associated Lymphoid Tissue Lymphoma. Investig Opthalmology Vis Sci 62:15. https://doi.org/10.1167/iovs.62.1.15

    Article  CAS  Google Scholar 

  42. Soria J, Villarrubia A, Merayo-Lloves J et al (2015) Label-free LC-MS/MS quantitative analysis of aqueous humor from keratoconic and normal eyes. Mol Vis 21:451–460

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Verhagen FH, Stigter ECA, Pras-Raves ML et al (2019) Aqueous Humor Analysis Identifies Higher Branched Chain Amino Acid Metabolism as a Marker for Human Leukocyte Antigen-B27 Acute Anterior Uveitis and Disease Activity. Am J Ophthalmol 198:97–110. https://doi.org/10.1016/j.ajo.2018.10.004

    Article  CAS  PubMed  Google Scholar 

  44. Wojakowska A, Pietrowska M, Widlak P et al (2020) Metabolomic Signature Discriminates Normal Human Cornea from Keratoconus—A Pilot GC/MS Study. Molecules 25:2933. https://doi.org/10.3390/molecules25122933

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Mitchell SL, Uppal K, Williamson SM et al (2018) The Carnitine Shuttle Pathway is Altered in Patients With Neovascular Age-Related Macular Degeneration. Investig Opthalmology Vis Sci 59:4978. https://doi.org/10.1167/iovs.18-25137

    Article  CAS  Google Scholar 

  46. Luo D, Deng T, Yuan W et al (2017) Plasma metabolomic study in Chinese patients with wet age-related macular degeneration. BMC Ophthalmol 17:165. https://doi.org/10.1186/s12886-017-0555-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Zhavoronkov A, Izumchenko E, Kanherkar RR et al (2016) Pro-fibrotic pathway activation in trabecular meshwork and lamina cribrosa is the main driving force of glaucoma. Cell Cycle 15:1643–1652. https://doi.org/10.1080/15384101.2016.1170261

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Bonacini M, Soriano A, Cimino L et al (2020) Cytokine Profiling in Aqueous Humor Samples From Patients With Non-Infectious Uveitis Associated With Systemic Inflammatory Diseases. Front Immunol 11:1–12. https://doi.org/10.3389/fimmu.2020.00358

    Article  CAS  Google Scholar 

  49. Jiang L, Rong A, Wei R et al (2020) Tear proteomics of orbital decompression for disfiguring exophthalmos in inactive thyroid-associated ophthalmopathy. Exp Ther Med 20:1–1. https://doi.org/10.3892/etm.2020.9383

    Article  CAS  Google Scholar 

  50. Sembler-Møller ML, Belstrøm D, Locht H, Pedersen AML (2020) Proteomics of saliva, plasma, and salivary gland tissue in Sjögren’s syndrome and non-Sjögren patients identify novel biomarker candidates. J Proteomics 225:103877. https://doi.org/10.1016/j.jprot.2020.103877

    Article  CAS  PubMed  Google Scholar 

  51. Bancks MP, Casanova R, Gregg EW, Bertoni AG (2019) Epidemiology of diabetes phenotypes and prevalent cardiovascular risk factors and diabetes complications in the National Health and Nutrition Examination Survey 2003–2014. Diabetes Res Clin Pract 158:107915. https://doi.org/10.1016/j.diabres.2019.107915

    Article  CAS  PubMed  Google Scholar 

  52. Karpati T, Leventer-Roberts M, Feldman B et al (2018) Patient clusters based on HbA1c trajectories: A step toward individualized medicine in type 2 diabetes. PLoS One 13:e0207096. https://doi.org/10.1371/journal.pone.0207096

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Aretz S, Krohne TU, Kammerer K et al (2013) In-depth mass spectrometric mapping of the human vitreous proteome. Proteome Sci 11:22. https://doi.org/10.1186/1477-5956-11-22

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Dor M, Eperon S, Lalive PH et al (2019) Investigation of the global protein content from healthy human tears. Exp Eye Res 179:64–74. https://doi.org/10.1016/j.exer.2018.10.006

    Article  CAS  PubMed  Google Scholar 

  55. Velez G, Bassuk AG, Colgan D et al (2017) Therapeutic drug repositioning using personalized proteomics of liquid biopsies. JCI Insight 2. https://doi.org/10.1172/jci.insight.97818

  56. Lee S, Zhou J, Wong WT et al (2021) Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning. BMC Endocr Disord 21:94. https://doi.org/10.1186/s12902-021-00751-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Yao L, Zhong Y, Wu J et al (2019) Multivariable Logistic Regression And Back Propagation Artificial Neural Network To Predict Diabetic Retinopathy. Diabetes, Metab Syndr Obes Targets Ther 12:1943–1951. https://doi.org/10.2147/DMSO.S219842

    Article  Google Scholar 

  58. Marino APMP, dos Santos LI, Henriques PM et al (2020) Circulating inflammatory mediators as biomarkers of ocular toxoplasmosis in acute and in chronic infection. J Leukoc Biol 108:1253–1264. https://doi.org/10.1002/JLB.4MA0420-702R

    Article  CAS  PubMed  Google Scholar 

  59. Liu B, Zhang B, Hu Y et al (2021) Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning. Ann Transl Med 9:43–43. https://doi.org/10.21037/atm-20-1431

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Mazumder AG, Banerjee S, Zevictovich F et al (2018) Fourier-transform-infrared-spectroscopy based metabolomic spectral biomarker selection towards optimal diagnostic differentiation of diabetes with and without retinopathy. Spectrosc Lett 51:340–349. https://doi.org/10.1080/00387010.2018.1471510

    Article  CAS  Google Scholar 

  61. Schori C, Trachsel C, Grossmann J et al (2018) The proteomic landscape in the vitreous of patients with age-related and diabetic retinal disease. Investig Ophthalmol Vis Sci 59:AMD31–AMD40. https://doi.org/10.1167/iovs.18-24122

    Article  CAS  Google Scholar 

  62. Torok Z, Peto T, Csosz E et al (2013) Tear fluid proteomics multimarkers for diabetic retinopathy screening. BMC Ophthalmol 13:40. https://doi.org/10.1186/1471-2415-13-40

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Li M, Zhang X, Liao N et al (2016) Analysis of the Serum Lipid Profile in Polypoidal Choroidal Vasculopathy. Sci Rep 6:38342. https://doi.org/10.1038/srep38342

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Pucchio A, Krance SH, Pur DR et al (2022) Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review. Clin Ophthalmol 16:2463–2476. https://doi.org/10.2147/OPTH.S377262

    Article  PubMed  PubMed Central  Google Scholar 

  65. Pucchio A, Krance S, Pur DR et al (2022) The role of artificial intelligence in analysis of biofluid markers for diagnosis and management of glaucoma: A systematic review. Eur J Ophthalmol 112067212211409. https://doi.org/10.1177/11206721221140948

  66. Pur DR, Krance SH, Pucchio A et al (2022) Current uses of artificial intelligence in the analysis of biofluid markers involved in corneal and ocular surface diseases: a systematic review. Eye 1–3. https://doi.org/10.1038/s41433-022-02307-9

  67. Bassi A, Krance SH, Pucchio A et al (2022) The Application of Artificial Intelligence in the Analysis of Biomarkers for Diagnosis and Management of Uveitis and Uveal Melanoma: A Systematic Review. Clin Ophthalmol 16:2895–2908. https://doi.org/10.2147/OPTH.S377358

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Pur DR, Krance S, Pucchio A et al (2022) Emerging applications of bioinformatics and artificial intelligence in the analysis of biofluid markers involved in retinal occlusive diseases: a systematic review. Graefe’s Arch Clin Exp Ophthalmol. https://doi.org/10.1007/s00417-022-05769-5

    Article  Google Scholar 

  69. Maulud D, Abdulazeez AM (2020) A Review on Linear Regression Comprehensive in Machine Learning. J Appl Sci Technol Trends 1:140–147. https://doi.org/10.38094/jastt1457

    Article  Google Scholar 

  70. Ing EB, Miller NR, Nguyen A et al (2019) Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation. Clin Ophthalmol 13:421–430. https://doi.org/10.2147/OPTH.S193460

    Article  PubMed  PubMed Central  Google Scholar 

  71. Burgess LG, Uppal K, Walker DI et al (2015) Metabolome-Wide Association Study of Primary Open Angle Glaucoma. Investig Opthalmology Vis Sci 56:5020. https://doi.org/10.1167/iovs.15-16702

    Article  CAS  Google Scholar 

  72. Tokuda Y, Yagi T, Yoshii K et al (2012) An approach to predict the risk of glaucoma development by integrating different attribute data. Springerplus 1:41. https://doi.org/10.1186/2193-1801-1-41

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Aumüller M, Bernhardsson E, Faithfull A (2020) ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms. Inf Syst 87:101374. https://doi.org/10.1016/j.is.2019.02.006

    Article  Google Scholar 

  74. Pisner DA, Schnyer DM (2020) Support vector machine. In: Machine Learning. Elsevier, pp 101–121 More details here: https://www.sciencedirect.com/science/article/abs/pii/B9780128157398000067

  75. Zou J, Han Y, So S-S (2008) Overview of artificial neural networks. pp 14–22 More details here: https://pubmed.ncbi.nlm.nih.gov/19065803/

  76. Hengl T, Nussbaum M, Wright MN et al (2018) Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ 6:e5518. https://doi.org/10.7717/peerj.5518

    Article  PubMed  PubMed Central  Google Scholar 

  77. North Carolina State University (2022) PA 765: Discriminant Function Analysis. In: Web Arch. https://web.archive.org/web/20080312065328/http://www2.chass.ncsu.edu/garson/pA765/discrim.htm. Accessed 21 Sep 2022

  78. Ao Y, Li H, Zhu L et al (2019) The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. J Pet Sci Eng 174:776–789. https://doi.org/10.1016/j.petrol.2018.11.067

    Article  CAS  Google Scholar 

  79. Ayinla S, Kehinde Adekunle B. An Overview and Application of Discriminant Analysis in Data Analysis. IOSR J Math 11:2278–5728. https://doi.org/10.9790/5728-11151215

  80. Karamizadeh S, Abdullah SM, Manaf AA et al (2013) An Overview of Principal Component Analysis. J Signal Inf Process 04:173–175. https://doi.org/10.4236/jsip.2013.43B031

    Article  Google Scholar 

  81. Acar İE, Lores-Motta L, Colijn JM, et al (2020) Integrating metabolomics, genomics, and disease pathways in age-related macular degeneration: The EYE-RISK Consortium More details here: https://pubmed.ncbi.nlm.nih.gov/32553749/

  82. Agasing AM, Wu Q, Khatri B et al (2020) Transcriptomics and proteomics reveal a cooperation between interferon and T-helper 17 cells in neuromyelitis optica. Nat Commun 11:2856. https://doi.org/10.1038/s41467-020-16625-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Agrawal R, Grant R, Gupta B et al (2017) What does IGRA testing add to the diagnosis of ocular tuberculosis? A Bayesian latent class analysis. BMC Ophthalmol 17:245. https://doi.org/10.1186/s12886-017-0597-x

    Article  PubMed  PubMed Central  Google Scholar 

  84. Ahlqvist E, Storm P, Käräjämäki A et al (2018) Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol 6:361–369. https://doi.org/10.1016/S2213-8587(18)30051-2

    Article  PubMed  Google Scholar 

  85. Anjana RM, Baskar V, Nair ATN et al (2020) Novel subgroups of type 2 diabetes and their association with microvascular outcomes in an Asian Indian population: a data-driven cluster analysis: the INSPIRED study. BMJ open diabetes Res care 8:e001506. https://doi.org/10.1136/bmjdrc-2020-001506

    Article  PubMed  PubMed Central  Google Scholar 

  86. Aqrawi LA, Galtung HK, Vestad B et al (2017) Identification of potential saliva and tear biomarkers in primary Sjögren’s syndrome, utilising the extraction of extracellular vesicles and proteomics analysis. Arthritis Res Ther 19:14. https://doi.org/10.1186/s13075-017-1228-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Bai S, Liu S, Guo X et al (2010) Proteome analysis of haptoglobin in cerebrospinal fluid of neuromyelitis optica. Mol Biol Rep 37:1619–1625. https://doi.org/10.1007/s11033-009-9574-7

    Article  CAS  PubMed  Google Scholar 

  88. Bennett KL, Funk M, Tschernutter M et al (2011) Proteomic analysis of human cataract aqueous humour: Comparison of one-dimensional gel LCMS with two-dimensional LCMS of unlabelled and iTRAQ®-labelled specimens. J Proteomics 74:151–166. https://doi.org/10.1016/j.jprot.2010.10.002

    Article  CAS  PubMed  Google Scholar 

  89. Beutgen VM, Pfeiffer N, Grus FH (2021) Serological Levels of Anti-clathrin Antibodies Are Decreased in Patients With Pseudoexfoliation Glaucoma. Front Immunol 12:1–9. https://doi.org/10.3389/fimmu.2021.616421

    Article  CAS  Google Scholar 

  90. Breda JB, Sava AC, Himmelreich U et al (2020) Metabolomic profiling of aqueous humor from glaucoma patients - The metabolomics in surgical ophthalmological patients (MISO) study. Exp Eye Res 201:108268. https://doi.org/10.1016/j.exer.2020.108268

  91. Chiang SY, Tsai ML, Wang CY et al (2012) Proteomic analysis and identification of aqueous humor proteins with a pathophysiological role in diabetic retinopathy. J Proteomics 75:2950–2959. https://doi.org/10.1016/j.jprot.2011.12.006

    Article  CAS  PubMed  Google Scholar 

  92. Christakopoulos C, Cehofski LJ, Christensen SR et al (2019) Proteomics reveals a set of highly enriched proteins in epiretinal membrane compared with inner limiting membrane. Exp Eye Res 186:107722. https://doi.org/10.1016/j.exer.2019.107722

    Article  CAS  PubMed  Google Scholar 

  93. Curovic VR, Suvitaival T, Mattila I et al (2020) Circulating Metabolites and Lipids Are Associated to Diabetic Retinopathy in Individuals With Type 1 Diabetes. Diabetes 69:2217–2226. https://doi.org/10.2337/db20-0104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Dagliati A, Marini S, Sacchi L et al (2018) Machine Learning Methods to Predict Diabetes Complications. J Diabetes Sci Technol 12:295–302. https://doi.org/10.1177/1932296817706375

    Article  PubMed  Google Scholar 

  95. Fan Y, Long E, Cai L et al (2021) Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes. Front Pharmacol 12:1–11. https://doi.org/10.3389/fphar.2021.665951

    Article  CAS  Google Scholar 

  96. Fernández I, López-Miguel A, Enríquez-de-Salamanca A et al (2019) Response profiles to a controlled adverse desiccating environment based on clinical and tear molecule changes. Ocul Surf 17:502–515. https://doi.org/10.1016/j.jtos.2019.03.009

    Article  PubMed  Google Scholar 

  97. Fernández I, López-Miguel A, Martín-Montañez V et al (2020) Inflammatory status predicts contact lens discomfort under adverse environmental conditions. Ocul Surf 18:829–840. https://doi.org/10.1016/j.jtos.2020.07.015

    Article  PubMed  Google Scholar 

  98. Grus FH, Augustin AJ, Toth-Sagi K (1998) Diagnostic classification of autoantibody repertoires in endocrine ophthalmopathy using an artificial neural network. Ocul Immunol Inflamm 6:43–50. https://doi.org/10.1076/ocii.6.1.43.8082

    Article  CAS  PubMed  Google Scholar 

  99. Grus FH, Sabuncuo P, Augustin AJ (2001) Analysis of tear protein patterns of dry-eye patients using fluorescent staining dyes and two-dimensional quantification algorithms. Electrophoresis 22:1845–1850. https://doi.org/10.1002/1522-2683(200105)22:9%3c1845::AID-ELPS1845%3e3.0.CO;2-N

    Article  CAS  PubMed  Google Scholar 

  100. Grus FH, Kramann C, Bozkurt N et al (2005) Effects of multipurpose contact lens solutions on the protein composition of the tear film. Contact Lens Anterior Eye 28:103–112. https://doi.org/10.1016/j.clae.2005.06.004

    Article  PubMed  Google Scholar 

  101. Grus FH, Podust VN, Bruns K et al (2005) SELDI-TOF-MS ProteinChip Array Profiling of Tears from Patients with Dry Eye. Investig Opthalmology Vis Sci 46:863. https://doi.org/10.1167/iovs.04-0448

    Article  Google Scholar 

  102. Guha Mazumder A, Chatterjee S, Chatterjee S et al (2017) Spectropathology-corroborated multimodal quantitative imaging biomarkers for neuroretinal degeneration in diabetic retinopathy. Clin Ophthalmol 11:2073–2089. https://doi.org/10.2147/OPTH.S140110

    Article  PubMed  PubMed Central  Google Scholar 

  103. Guo J, Yan T, Bi H et al (2014) Plasma metabonomics study of the patients with acute anterior uveitis based on ultra-performance liquid chromatography–mass spectrometry. Graefe’s Arch Clin Exp Ophthalmol 252:925–934. https://doi.org/10.1007/s00417-014-2619-1

    Article  CAS  Google Scholar 

  104. Hall JC, Baer AN, Shah AA et al (2015) Molecular Subsetting of Interferon Pathways in Sjögren’s Syndrome. Arthritis Rheumatol 67:2437–2446. https://doi.org/10.1002/art.39204

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Huang Z, Du C-X, Pan X-D (2018) The use of in-strip digestion for fast proteomic analysis on tear fluid from dry eye patients. PLoS One 13:e0200702. https://doi.org/10.1371/journal.pone.0200702

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Indini A, Di Guardo L, Cimminiello C et al (2019) Artificial Intelligence Estimates the Importance of Baseline Factors in Predicting Response to Anti-PD1 in Metastatic Melanoma. Am J Clin Oncol 42:643–648. https://doi.org/10.1097/COC.0000000000000566

    Article  CAS  PubMed  Google Scholar 

  107. Ji Y, Rong X, Ye H et al (2015) Proteomic analysis of aqueous humor proteins associated with cataract development. Clin Biochem 48:1304–1309. https://doi.org/10.1016/j.clinbiochem.2015.08.006

    Article  CAS  PubMed  Google Scholar 

  108. Ji YW, Kim HM, Ryu SY et al (2019) Changes in Human Tear Proteome Following Topical Treatment of Dry Eye Disease: Cyclosporine A Versus Diquafosol Tetrasodium. Investig Opthalmology Vis Sci 60:5035. https://doi.org/10.1167/iovs.19-27872

    Article  CAS  Google Scholar 

  109. Jin H, Zhu B, Liu X et al (2019) Metabolic characterization of diabetic retinopathy: An 1H-NMR-based metabolomic approach using human aqueous humor. J Pharm Biomed Anal 174:414–421. https://doi.org/10.1016/j.jpba.2019.06.013

    Article  CAS  PubMed  Google Scholar 

  110. Joachim SC, Pfeiffer N, Grus FH (2005) Autoantibodies in patients with glaucoma: a comparison of IgG serum antibodies against retinal, optic nerve, and optic nerve head antigens. Graefe’s Arch Clin Exp Ophthalmol 243:817–823. https://doi.org/10.1007/s00417-004-1094-5

    Article  CAS  Google Scholar 

  111. Joachim SC, Wuenschig D, Pfeiffer N, Grus FH (2007) IgG antibody patterns in aqueous humor of patients with primary open angle glaucoma and pseudoexfoliation glaucoma. Mol Vis 13:1573–1579

    CAS  PubMed  Google Scholar 

  112. Jurynczyk M, Probert F, Yeo T et al (2017) Metabolomics reveals distinct, antibody-independent, molecular signatures of MS, AQP4-antibody and MOG-antibody disease. Acta Neuropathol Commun 5:95. https://doi.org/10.1186/s40478-017-0495-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Kahkoska AR, Nguyen CT, Adair LA et al (2019) Longitudinal Phenotypes of Type 1 Diabetes in Youth Based on Weight and Glycemia and Their Association With Complications. J Clin Endocrinol Metab 104:6003–6016. https://doi.org/10.1210/jc.2019-00734

    Article  PubMed  PubMed Central  Google Scholar 

  114. Ke C, Xu H, Chen Q et al (2021) Serum metabolic signatures of high myopia among older Chinese adults. Eye 35:817–824. https://doi.org/10.1038/s41433-020-0968-z

    Article  CAS  PubMed  Google Scholar 

  115. Kersten E, Dammeier S, Ajana S et al (2019) Metabolomics in serum of patients with non-advanced age-related macular degeneration reveals aberrations in the glutamine pathway. PLoS One 14:e0218457. https://doi.org/10.1371/journal.pone.0218457

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Kim WB, Chung HK, Park YJ et al (2001) Clinical Significance of Classification of Graves` Disease According to the Characteristics of TSH receptor Antibodies. Korean J Intern Med 16:187–200. https://doi.org/10.3904/kjim.2001.16.3.187

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Kim SW, Lee J, Lee B, Rhim T (2014) Proteomic analysis in pterygium; upregulated protein expression of ALDH3A1, PDIA3, and PRDX2. Mol Vis 20:1192–1202

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Kouassi Nzoughet J, Guehlouz K, Leruez S et al (2020) A Data Mining Metabolomics Exploration of Glaucoma. Metabolites 10:49. https://doi.org/10.3390/metabo10020049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Kowalczuk L, Matet A, Dor M et al (2018) Proteome and Metabolome of Subretinal Fluid in Central Serous Chorioretinopathy and Rhegmatogenous Retinal Detachment: A Pilot Case Study. Transl Vis Sci Technol 7:3. https://doi.org/10.1167/tvst.7.1.3

    Article  PubMed  PubMed Central  Google Scholar 

  120. Kramann C, Boehm N, Lorenz K et al (2011) Effect of contact lenses on the protein composition in tear film: a ProteinChip study. Graefe’s Arch Clin Exp Ophthalmol 249:233–243. https://doi.org/10.1007/s00417-010-1456-0

    Article  Google Scholar 

  121. Kuiper JJW, Beretta L, Nierkens S et al (2017) An Ocular Protein Triad Can Classify Four Complex Retinal Diseases. Sci Rep 7:1–9. https://doi.org/10.1038/srep41595

    Article  CAS  Google Scholar 

  122. Laíns I, Kelly RS, Miller JB et al (2018) Human Plasma Metabolomics Study across All Stages of Age-Related Macular Degeneration Identifies Potential Lipid Biomarkers. Ophthalmology 125:245–254. https://doi.org/10.1016/j.ophtha.2017.08.008

    Article  PubMed  Google Scholar 

  123. Laíns I, Chung W, Kelly RS et al (2019) Human Plasma Metabolomics in Age-Related Macular Degeneration: Meta-Analysis of Two Cohorts. Metabolites 9:127. https://doi.org/10.3390/metabo9070127

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Li H, Ma C, Liu W et al (2020) Gypenosides Protect Orbital Fibroblasts in Graves Ophthalmopathy via Anti-Inflammation and Anti-Fibrosis Effects. Investig Opthalmology Vis Sci 61:64. https://doi.org/10.1167/iovs.61.5.64

    Article  CAS  Google Scholar 

  125. Li J, Wang T, Zuo J et al (2020) Association of n-6 PUFAs with the risk of diabetic retinopathy in diabetic patients. Endocr Connect 9:1191–1201. https://doi.org/10.1530/EC-20-0370

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Linghu D, Guo L, Zhao Y et al (2017) iTRAQ-based quantitative proteomic analysis and bioinformatics study of proteins in pterygia. PROTEOMICS - Clin Appl 11:1600094. https://doi.org/10.1002/prca.201600094

    Article  CAS  Google Scholar 

  127. Liu H, Zhang J, Zhou P et al (2019) Exploration of vascular adhesion protein-1 expression in patients with conjunctivitis associated systemic lupus erythematosus using 2D-DIGE. Exp Ther Med 5072–5077. https://doi.org/10.3892/etm.2019.8009

  128. Liu A, Wang L, Feng Q et al (2021) Low expression of GSTP1 in the aqueous humour of patients with primary open-angle glaucoma. J Cell Mol Med 25:3063–3079. https://doi.org/10.1111/jcmm.16361

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Liu Y-C, Yam GH-F, Lin MT-Y et al (2021) Comparison of tear proteomic and neuromediator profiles changes between small incision lenticule extraction (SMILE) and femtosecond laser-assisted in-situ keratomileusis (LASIK). J Adv Res 29:67–81. https://doi.org/10.1016/j.jare.2020.11.001

    Article  CAS  PubMed  Google Scholar 

  130. Lynch AM, Wagner BD, Weiss SJ et al (2019) Proteomic profiles in advanced age-related macular degeneration using an aptamer-based proteomic technology. Transl Vis Sci Technol 8. https://doi.org/10.1167/tvst.8.1.14

  131. Lynch AM, Wagner BD, Palestine AG et al (2020) Plasma biomarkers of reticular pseudodrusen and the risk of progression to advanced age-related macular degeneration. Transl Vis Sci Technol 9:1–13. https://doi.org/10.1167/tvst.9.10.12

    Article  Google Scholar 

  132. Maier P, Heizmann U, Böhringer D et al (2011) Predicting the risk for corneal graft rejection by aqueous humor analysis. Mol Vis 17:1016–1023

    CAS  PubMed  PubMed Central  Google Scholar 

  133. Menegay M, Lee D, Tabbara KF et al (2008) Proteomic Analysis of Climatic Keratopathy Droplets. Investig Opthalmology Vis Sci 49:2829. https://doi.org/10.1167/iovs.07-1438

    Article  Google Scholar 

  134. Mirić D, Kisić B, Žorić L et al (2012) The Impact of Senile Cataract Maturity on Blood Oxidative Stress Markers and Glutathione-Dependent Antioxidants: Relations with Lens Variables. J Med Biochem 31:184–192. https://doi.org/10.2478/v10011-012-0003-4

    Article  CAS  Google Scholar 

  135. Moussallieh F-M, Elbayed K, Chanson J et al (2014) Serum analysis by 1 H Nuclear Magnetic Resonance spectroscopy: a new tool for distinguishing neuromyelitis optica from multiple sclerosis. Mult Scler J 20:558–565. https://doi.org/10.1177/1352458513504638

    Article  Google Scholar 

  136. Myer C, Abdelrahman L, Banerjee S et al (2020) Aqueous humor metabolite profile of pseudoexfoliation glaucoma is distinctive. Mol Omi 16:425–435. https://doi.org/10.1039/C9MO00192A

    Article  CAS  Google Scholar 

  137. Nättinen J, Mäkinen P, Aapola U et al (2020) Early changes in tear film protein profiles after femtosecond LASIK surgery. Clin Proteomics 17:36. https://doi.org/10.1186/s12014-020-09303-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Nezu N, Usui Y, Saito A et al (2021) Machine Learning Approach for Intraocular Disease Prediction Based on Aqueous Humor Immune Mediator Profiles. Ophthalmology 128:1197–1208. https://doi.org/10.1016/j.ophtha.2021.01.019

    Article  PubMed  Google Scholar 

  139. Nielsen HH, Beck HC, Kristensen LP et al (2015) The Urine Proteome Profile Is Different in Neuromyelitis Optica Compared to Multiple Sclerosis: A Clinical Proteome Study. PLoS One 10:e0139659. https://doi.org/10.1371/journal.pone.0139659

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Nusinovici S, Zhang L, Chai X, et al (2020) Machine learning to determine relative contribution of modifiable and non-modifiable risk factors of major eye diseases. Br J Ophthalmol 2020–317454. https://doi.org/10.1136/bjophthalmol-2020-317454

  141. Ogunyemi OI, Gandhi M, Tayek C (2019) Predictive Models for Diabetic Retinopathy from Non-Image Teleretinal Screening Data. AMIA Jt Summits Transl Sci proceedings AMIA Jt Summits Transl Sci 2019:472–477

    Google Scholar 

  142. Okrojek R, Grus FH, Matheis N, Kahaly GJ (2009) Proteomics in Autoimmune Thyroid Eye Disease. Horm Metab Res 41:465–470. https://doi.org/10.1055/s-0029-1214413

    Article  CAS  PubMed  Google Scholar 

  143. O’Leary OE, Schoetzau A, Amruthalingam L et al (2020) Tear Proteomic Predictive Biomarker Model for Ocular Graft Versus Host Disease Classification. Transl Vis Sci Technol 9:3. https://doi.org/10.1167/tvst.9.9.3

    Article  PubMed  PubMed Central  Google Scholar 

  144. Pan C-W, Ke C, Chen Q et al (2020) Differential metabolic markers associated with primary open-angle glaucoma and cataract in human aqueous humor. BMC Ophthalmol 20:183. https://doi.org/10.1186/s12886-020-01452-7

    Article  PubMed  PubMed Central  Google Scholar 

  145. Pavan J, Štambuk N, Pokrić B et al (2000) Quantification of Intraocular Interferon-γ and IgG in Cataract and Diabetes. Croat Chem Acta 73:1099–1110

    CAS  Google Scholar 

  146. Prior MJ, Prout T, Miller D et al (1993) C-peptide and the classification of diabetes mellitus patients in the Early treatment diabetic retinopathy study report number 6. Ann Epidemiol 3:9–17. https://doi.org/10.1016/1047-2797(93)90004-N

    Article  CAS  PubMed  Google Scholar 

  147. Qin Y, Feng X, Luo H et al (2022) Association between plasma free fatty acid levels and primary angle-closure glaucoma based on a mass spectrometry metabolomics analysis. Acta Ophthalmol 100:e204–e212. https://doi.org/10.1111/aos.14874

    Article  CAS  PubMed  Google Scholar 

  148. Romero P, Salvat M, Fernández J et al (2007) Renal and retinal microangiopathy after 15 years of follow-up study in a sample of Type 1 diabetes mellitus patients. J Diabetes Complications 21:93–100. https://doi.org/10.1016/j.jdiacomp.2006.04.001

    Article  PubMed  Google Scholar 

  149. Romero-Aroca P, Baget-Bernaldiz M, Reyes-Torres J et al (2012) Relationship between diabetic retinopathy, microalbuminuria and overt nephropathy, and twenty-year incidence follow-up of a sample of type 1 diabetic patients. J Diabetes Complications 26:506–512. https://doi.org/10.1016/j.jdiacomp.2012.06.010

    Article  PubMed  Google Scholar 

  150. Romero-Aroca P, Valls A, Moreno A et al (2019) A Clinical Decision Support System for Diabetic Retinopathy Screening: Creating a Clinical Support Application. Telemed e-Health 25:31–40. https://doi.org/10.1089/tmj.2017.0282

    Article  Google Scholar 

  151. Romero-Aroca P, Verges-Pujol R, Santos-Blanco E et al (2021) Validation of a Diagnostic Support System for Diabetic Retinopathy Based on Clinical Parameters. Transl Vis Sci Technol 10:17. https://doi.org/10.1167/tvst.10.3.17

    Article  PubMed  PubMed Central  Google Scholar 

  152. Roybal CN, Velez G, Toral MA et al (2018) Personalized Proteomics in Proliferative Vitreoretinopathy Implicate Hematopoietic Cell Recruitment and mTOR as a Therapeutic Target. Am J Ophthalmol 186:152–163. https://doi.org/10.1016/j.ajo.2017.11.025

    Article  CAS  PubMed  Google Scholar 

  153. Safai N, Ali A, Rossing P, Ridderstråle M (2018) Stratification of type 2 diabetes based on routine clinical markers. Diabetes Res Clin Pract 141:275–283. https://doi.org/10.1016/j.diabres.2018.05.014

    Article  CAS  PubMed  Google Scholar 

  154. Sandhu HS, Elmogy M, Taher Sharafeldeen A et al (2020) Automated Diagnosis of Diabetic Retinopathy Using Clinical Biomarkers, Optical Coherence Tomography, and Optical Coherence Tomography Angiography. Am J Ophthalmol 216:201–206. https://doi.org/10.1016/j.ajo.2020.01.016

    Article  PubMed  Google Scholar 

  155. Sato T, Takeuchi M, Karasawa Y et al (2019) Comprehensive expression patterns of inflammatory cytokines in aqueous humor of patients with neovascular age-related macular degeneration. Sci Rep 9:19447. https://doi.org/10.1038/s41598-019-55191-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  156. Seghieri G, Bartolomei G, De Giorgio LA (1986) Plasma fibronectin in diabetic retinopathy and macroangiopathy. Diabete Metab 12:186–90

    CAS  PubMed  Google Scholar 

  157. Semeraro F, Parrinello G, Cancarini A et al (2011) Predicting the risk of diabetic retinopathy in type 2 diabetic patients. J Diabetes Complications 25:292–297. https://doi.org/10.1016/j.jdiacomp.2010.12.002

    Article  PubMed  Google Scholar 

  158. Shi XY, Li Q, Bin Wei W, Tao LM (2017) Peptidome profiling of human serum of uveal melanoma patients based on magnetic bead fractionation and mass spectrometry. Int J Ophthalmol 10:939–947. https://doi.org/10.18240/ijo.2017.06.17

    Article  PubMed  PubMed Central  Google Scholar 

  159. Shimizu H, Usui Y, Asakage M et al (2020) Serum metabolomic profiling of patients with non-infectious uveitis. J Clin Med 9:1–10. https://doi.org/10.3390/jcm9123955

    Article  CAS  Google Scholar 

  160. Sivagurunathan S, Selvan LN, Khan A et al (2021) Proteomics-based approach for differentiation of age-related macular degeneration sub-types. Indian J Ophthalmol 69:647. https://doi.org/10.4103/ijo.IJO_470_20

    Article  PubMed  PubMed Central  Google Scholar 

  161. Skeie JM, Roybal CN, Mahajan VB (2015) Proteomic Insight into the Molecular Function of the Vitreous. PLoS One 10:e0127567. https://doi.org/10.1371/journal.pone.0127567

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Sommer F, Spörl E, Herber R et al (2019) Predictive value of positive temporal artery biopsies in patients with clinically suspected giant cell arteritis considering temporal artery ultrasound findings. Graefe’s Arch Clin Exp Ophthalmol 257:2279–2284. https://doi.org/10.1007/s00417-019-04430-y

    Article  CAS  Google Scholar 

  163. Srinivasan S, Thangavelu M, Zhang L et al (2012) iTRAQ Quantitative Proteomics in the Analysis of Tears in Dry Eye Patients. Investig Opthalmology Vis Sci 53:5052. https://doi.org/10.1167/iovs.11-9022

    Article  CAS  Google Scholar 

  164. Sudha D, Kohansal-Nodehi M, Kovuri P et al (2017) Proteomic profiling of human intraschisis cavity fluid. Clin Proteomics 14:13. https://doi.org/10.1186/s12014-017-9148-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. Tebani A, Mauhin W, Abily-Donval L et al (2020) A Proteomics-Based Analysis Reveals Predictive Biological Patterns in Fabry Disease. J Clin Med 9:1325. https://doi.org/10.3390/jcm9051325

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  166. Ting DSW, Cheung CY, Nguyen Q et al (2019) Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi-ethnic study. NPJ Digit Med 2:24. https://doi.org/10.1038/s41746-019-0097-x

    Article  PubMed  PubMed Central  Google Scholar 

  167. Tong L, Zhou L, Beuerman R et al (2017) Effects of punctal occlusion on global tear proteins in patients with dry eye. Ocul Surf 15:736–741. https://doi.org/10.1016/j.jtos.2017.04.002

    Article  PubMed  Google Scholar 

  168. Torok Z, Peto T, Csosz E et al (2015) Combined Methods for Diabetic Retinopathy Screening, Using Retina Photographs and Tear Fluid Proteomics Biomarkers. J Diabetes Res 2015:1–8. https://doi.org/10.1155/2015/623619

    Article  Google Scholar 

  169. Tsubota K, Usui Y, Goto H (2020) Identification of Prognostic Markers in Patients with Primary Vitreoretinal Lymphoma by Clustering Analysis Using Clinical Data. J Clin Med 9:2298. https://doi.org/10.3390/jcm9072298

    Article  PubMed  PubMed Central  Google Scholar 

  170. Tsubota K, Usui Y, Nemoto R, Goto H (2020) Identification of Markers Predicting Clinical Course in Patients with IgG4-Related Ophthalmic Disease by Unbiased Clustering Analysis. J Clin Med 9:4084. https://doi.org/10.3390/jcm9124084

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Varghese JS, Ho JC, Anjana RM et al (2021) Profiles of Intraday Glucose in Type 2 Diabetes and Their Association with Complications: An Analysis of Continuous Glucose Monitoring Data. Diabetes Technol Ther 23:555–564. https://doi.org/10.1089/dia.2020.0672

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  172. Velez G, Roybal CN, Colgan D et al (2016) Precision Medicine: Personalized Proteomics for the Diagnosis and Treatment of Idiopathic Inflammatory Disease. JAMA Ophthalmol 134:444–448. https://doi.org/10.1001/jamaophthalmol.2015.5934

    Article  PubMed  PubMed Central  Google Scholar 

  173. Velez G, Roybal CN, Binkley E et al (2017) Proteomic analysis of elevated intraocular pressure with retinal detachment. Am J Ophthalmol Case Rep 5:107–110. https://doi.org/10.1016/j.ajoc.2016.12.023

    Article  PubMed  PubMed Central  Google Scholar 

  174. Velez G, Yang J, Li AS et al (2019) Proteomic insight into the pathogenesis of CAPN5-vitreoretinopathy. Sci Rep 9:7608. https://doi.org/10.1038/s41598-019-44031-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Wang H, Zhai R, Sun Q et al (2019) Metabolomic Profile of Posner-Schlossman Syndrome: A Gas Chromatography Time-of-Flight Mass Spectrometry-Based Approach Using Aqueous Humor. Front Pharmacol 10:1–11. https://doi.org/10.3389/fphar.2019.01322

    Article  CAS  Google Scholar 

  176. Wang H, Fang J, Chen F et al (2020) Metabolomic profile of diabetic retinopathy: a GC-TOFMS-based approach using vitreous and aqueous humor. Acta Diabetol 57:41–51. https://doi.org/10.1007/s00592-019-01363-0

    Article  PubMed  Google Scholar 

  177. Wang W, Pei X, Zhang L et al (2021) Application of new international classification of adult-onset diabetes in Chinese inpatients with diabetes mellitus. Diabetes Metab Res Rev 37:1–8. https://doi.org/10.1002/dmrr.3427

    Article  CAS  Google Scholar 

  178. Wei Q, Jiang C, Ye X et al (2019) Vitreous Proteomics Provides New Insights into Antivascular Endothelial Growth Factor Therapy for Pathologic Myopia Choroid Neovascularization. J Interf Cytokine Res 39:786–796. https://doi.org/10.1089/jir.2019.0030

    Article  CAS  Google Scholar 

  179. Wei P, He M, Teng H, Han G (2020) Metabolomic analysis of the aqueous humor from patients with central retinal vein occlusion using UHPLC-MS/MS. J Pharm Biomed Anal 188:113448. https://doi.org/10.1016/j.jpba.2020.113448

    Article  CAS  PubMed  Google Scholar 

  180. Wen K, Shao X, Li Y et al (2021) The plasminogen protein is associated with high myopia as revealed by the iTRAQ-based proteomic analysis of the aqueous humor. Sci Rep 11:8789. https://doi.org/10.1038/s41598-021-88220-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Wu Z, Ding N, Yu M et al (2016) Identification of Potential Biomarkers for Rhegmatogenous Retinal Detachment Associated with Choroidal Detachment by Vitreous iTRAQ-Based Proteomic Profiling. Int J Mol Sci 17:2052. https://doi.org/10.3390/ijms17122052

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  182. Wu X, Liu Z, Zhang X et al (2017) Proteomics analysis and proteogenomic characterization of different physiopathological human lenses. BMC Ophthalmol 17:253. https://doi.org/10.1186/s12886-017-0642-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  183. Xiao H, Xin W, Sun LM et al (2021) Comprehensive proteomic profiling of aqueous humor proteins in proliferative diabetic retinopathy. Transl Vis Sci Technol 10:0–7. https://doi.org/10.1167/tvst.10.6.3

  184. Xiong X, Yang Y, Wei L et al (2021) Identification of two novel subgroups in patients with diabetes mellitus and their association with clinical outcomes: A two-step cluster analysis. J Diabetes Investig 12:1346–1358. https://doi.org/10.1111/jdi.13494

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  185. Xuan Q, Zheng F, Yu D et al (2020) Rapid lipidomic profiling based on ultra-high performance liquid chromatography–mass spectrometry and its application in diabetic retinopathy. Anal Bioanal Chem 412:3585–3594. https://doi.org/10.1007/s00216-020-02632-6

    Article  CAS  PubMed  Google Scholar 

  186. Yam GH-F, Fuest M, Zhou L et al (2019) Differential epithelial and stromal protein profiles in cone and non-cone regions of keratoconus corneas. Sci Rep 9:2965. https://doi.org/10.1038/s41598-019-39182-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  187. Yamamoto Y, Morozumi T, Hirata T et al (2020) Effect of Periodontal Disease on Diabetic Retinopathy in Type 2 Diabetic Patients: A Cross-Sectional Pilot Study. J Clin Med 9:3234. https://doi.org/10.3390/jcm9103234

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  188. Yang H, Yang X, Wang Y et al (2020) Comparative analysis of the tear protein profile in herpes simplex virus type 1 epithelial keratitis. BMC Ophthalmol 20:355. https://doi.org/10.1186/s12886-020-01626-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  189. Yao J, Chen Z, Yang Q et al (2013) Proteomic analysis of aqueous humor from patients with branch retinal vein occlusion-induced macular edema. Int J Mol Med 32:1421–1434. https://doi.org/10.3892/ijmm.2013.1509

    Article  CAS  PubMed  Google Scholar 

  190. Yao J, Liu X, Yang Q et al (2013) Proteomic analysis of the aqueous humor in patients with wet age-related macular degeneration. PROTEOMICS - Clin Appl 7:550–560. https://doi.org/10.1002/prca.201200012

    Article  CAS  PubMed  Google Scholar 

  191. Yawata N, Awate S, Liu Y-C et al (2019) Kinetics of Tear Fluid Proteins after Endothelial Keratoplasty and Predictive Factors for Recovery from Corneal Haze. J Clin Med 9:63. https://doi.org/10.3390/jcm9010063

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  192. Young SP, Nessim M, Falciani F et al (2009) Metabolomic analysis of human vitreous humor differentiates ocular inflammatory disease. Mol Vis 15:1210–1217

    CAS  PubMed  PubMed Central  Google Scholar 

  193. Yu J, Feng L, Wu Y et al (2014) Vitreous proteomic analysis of idiopathic epiretinal membranes. Mol BioSyst 10:2558–2566. https://doi.org/10.1039/C4MB00240G

    Article  CAS  PubMed  Google Scholar 

  194. Yu M, Wu Z, Zhang Z et al (2015) Metabolomic Analysis of Human Vitreous in Rhegmatogenous Retinal Detachment Associated With Choroidal Detachment. Investig Opthalmology Vis Sci 56:5706. https://doi.org/10.1167/iovs.14-16338

    Article  Google Scholar 

  195. Zhang L, Masetti G, Colucci G et al (2018) Combining micro-RNA and protein sequencing to detect robust biomarkers for Graves’ disease and orbitopathy. Sci Rep 8:8386. https://doi.org/10.1038/s41598-018-26700-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  196. Zhang D, Zhao Y, Yang Y-T et al (2020) A Mechanism Study of Electroacupuncture for Dry Eye Syndrome by Targeting Conjunctival Cytokine Expressions. Curr Eye Res 45:419–427. https://doi.org/10.1080/02713683.2019.1666997

    Article  CAS  PubMed  Google Scholar 

  197. Zhang S, Zhang K, He W et al (2021) Quantitative Phosphoproteomic Comparison of Lens Proteins in Highly Myopic Cataract and Age-Related Cataract. Biomed Res Int 2021:1–18. https://doi.org/10.1155/2021/6668845

    Article  CAS  Google Scholar 

  198. Zhavoronkov A, Kanherkar RR, Izumchenko E et al (2016) Pro-fibrotic pathway activation in trabecular meshwork and lamina cribrosa is the main driving force of glaucoma. Cell Cycle 15:1643–1652. https://doi.org/10.1080/15384101.2016.1170261

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  199. Zhou L, Zhao SZ, Koh SK et al (2012) In-depth analysis of the human tear proteome. J Proteomics 75:3877–3885. https://doi.org/10.1016/j.jprot.2012.04.053

    Article  CAS  PubMed  Google Scholar 

  200. Zhu X, Du Y, Truscott RJW et al (2020) Profiling and Bioinformatic Analysis of Differentially Expressed Cytokines in Aqueous Humor of High Myopic Eyes – Clues for Anti-VEGF Injections. Curr Eye Res 45:97–103. https://doi.org/10.1080/02713683.2019.1648833

    Article  CAS  PubMed  Google Scholar 

  201. Zou C, Zhao M, Yu J et al (2018) Difference in the Vitreal Protein Profiles of Patients with Proliferative Diabetic Retinopathy with and without Intravitreal Conbercept Injection. J Ophthalmol 2018:1–11. https://doi.org/10.1155/2018/7397610

    Article  CAS  Google Scholar 

  202. Zou X, Wang S, Zhang P et al (2020) Quantitative Proteomics and Weighted Correlation Network Analysis of Tear Samples in Adults and Children With Diabetes and Dry Eye. Transl Vis Sci Technol 9:8. https://doi.org/10.1167/tvst.9.13.8

    Article  PubMed  PubMed Central  Google Scholar 

  203. Zuo J, Lan Y, Hu H et al (2021) Metabolomics-based multidimensional network biomarkers for diabetic retinopathy identification in patients with type 2 diabetes mellitus. BMJ Open Diabetes Res Care 9:e001443. https://doi.org/10.1136/bmjdrc-2020-001443

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This study was in part supported by the Fighting Blindness Canada’s (FBC’s) Clinician Scientist Emerging Leader Award granted to Dr. Tina Felfeli. 

Author information

Authors and Affiliations

  1. Department of Ophthalmology, Queen’s University, Kingston, ON, Canada

    Aidan Pucchio

  2. Queens School of Medicine, Kingston, ON, Canada

    Aidan Pucchio, Karthik Manichavagan & Ishita Aggarwal

  3. Schulich School of Medicine and Dentistry, Western University, London, ON, Canada

    Saffire H. Krance, Daiana R. Pur, Jasmine Bhatti, Arshpreet Bassi, Shaily Brahmbhatt, Priyanka Singh & Aleena Virani

  4. Western University, London, ON, Canada

    Meagan Stanley

  5. The Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada

    Rafael N. Miranda & Tina Felfeli

  6. Department of Ophthalmology and Vision Sciences, University of Toronto, 340 College Street, Suite 400, Toronto, ON, M5T 3A9, Canada

    Tina Felfeli

Authors
  1. Aidan Pucchio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saffire H. Krance
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daiana R. Pur
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jasmine Bhatti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Arshpreet Bassi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Karthik Manichavagan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shaily Brahmbhatt
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ishita Aggarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Priyanka Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Aleena Virani
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Meagan Stanley
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Rafael N. Miranda
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Tina Felfeli
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: Saffire H Krance, Karthik Manichavagan, Rafael N Miranda, Tina Felfeli; Methodology: Saffire H Krance, Aidan Pucchio, Karthik Manichavagan, Rafael N Miranda, Tina Felfeli; Formal analysis and investigation: Saffire H Krance, Aidan Pucchio, Daiana R Pur, Arshpreet Bassi, Jasmine Bhatti, Karthik Manichavagan, Shaily Brahmbhatt, Ishita Aggarwal, Priyanka Singh, Aleena Virani, Meagan Stanley, Rafael N Miranda, Tina Felfeli; Writing—original draft preparation: Saffire H Krance, Aidan Pucchio, Daiana R Pur, Arshpreet Bassi, Rafael N Miranda, Tina Felfeli; Writing—review and editing: Saffire H Krance, Aidan Pucchio, Rafael N Miranda, Tina Felfeli; Funding acquisition: Tina Felfeli; Supervision: Rafael N Miranda, Tina Felfeli.

Corresponding author

Correspondence to Tina Felfeli.

Ethics declarations

Ethics approval

As this is a scoping review, no human participants were involved, and IRB approval was not required.

Conflict of interest

No conflicting relationship exists for any author. The authors do not have any proprietary interests in the materials described in the article.

Presentations

The contents of this manuscript have not been presented at any conferences priorto submission.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Aidan Pucchio and Saffire H Krance are co-first authors.

Appendices

Appendix A

Search strategy utilized for five electronic databases (EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Web of Science).

Embase

  1. 1.

    (ophth* or ocular or intraocular or eye* or retina* or macula* or fovea* or uvea* or sclera* or cornea* or conjunctiva* or iris or "vitreous body" or "vitreous humo?r" or "vitreous fluid" or vitreo* or "aqueous humo?r" or "aqueous fluid" or tears or ((tear or lacrimal) adj fluid) or glaucoma or retinop* or retinoblastoma or uveitis or iritis or choroiditis or retinitis or chorioretinitis or conjunctivitis or endophthalmitis or cataract* or ?????????opia or "optic atrophy" or "optic neuropathy" or vitrectomy or phacoemulsification or trabeculotomy or (paracentesis adj3 "anterior chamber")).tw.

  2. 2.

    ("precision medicine" or "precision health" or "personalized medicine" or "personalized proteomics" or thera?nostic? or "tailored medicine" or "artificial intelligence" or "machine learning" or "deep learning" or algorithm? or ((supervised or unsupervised or biased or unbiased or bayesian or hierarchical or neur??al) adj (cluster* or learning or learner? or classifi* or network?)) or "k-nearest neighbo?r?" or "naive bayes" or (decision adj (tree? or forest? or jungle?)) or "random forest?" or "gradient-boost*" or "support vector machine" or "k-means" or "association rules" or "recursive partitioning" or "discriminant analysis" or "feature selection" or ((linear or nonlinear or "non-linear" or logistic or ordinal or poisson or quantile or analysis) adj1 (regression? or model?)) or bioinformatic? or "gene ontology" or "Kyoto Encyclopedia of Genes and Genomes" or "KEGG" or ((progress* or regress* or recover* or respond* or response*) and (predict* or stratif*))).tw.

  3. 3.

    (Proteomic? or proteome? or metabolomic? or metabolome? or lipidomic? or lipidome? or "?????inflammatory protein?" or "?????inflammatory marker" or cytokine? or interleukin? or lymphokine? or monokine? or interferon? or "colony stimulating factor?" or chemokine? or "growth factor?" or "necrosis factor?" or "chemotactic protein?" or "adhesion molecule?" or "adhesion protein?" or "matrix metalloproteinase-2" or myeloperoxidase? or "tissue inhibitor of metalloproteinase-2" or "macrophage inflammatory protein-1" or "brain-derived neurotrophic factor" or angiopoietin? or ((hemoglobin or haemoglobin) adj1 (a1c or glycated)) or hba1c or "c reactive protein" or "c-reactive protein" or crp or hscrp or "hs-crp" or ((protein or biomarker) and (concentration? or level? or quantif* or quantit* or mass spectrometry or iTRAQ or MALDI or SELDI or assay))).tw.

  4. 4.

    ophthalmology/

  5. 5.

    eye/ or anterior eye chamber/ or anterior eye segment/ or aqueous humor/ or exp conjunctiva/ or exp cornea/ or eye fundus/ or eyeball/ or exp lens/ or ocular blood vessel/ or ophthalmic artery/ or optic disk/ or palpebral fissure/ or posterior eye chamber/ or posterior eye segment/ or exp retina/ or exp sclera/ or sphincter pupillae muscle/ or tenon capsule/ or trabecular meshwork/ or exp uvea/ or vitreous body/

  6. 6.

    lacrimal fluid/

  7. 7.

    eye disease/ or exp accommodation disorder/ or exp conjunctiva disease/ or exp cornea disease/ or exp dry eye/ or exp eye burning/ or exp eye chamber disease/ or exp eye discharge/ or exp eye discomfort/ or exp eye edema/ or exp eye infection/ or exp eye inflammation/ or exp eye injury/ or exp eye irritation/ or exp eye jaundice/ or exp eye malformation/ or exp eye pain/ or exp eye redness/ or exp eye swelling/ or exp eye toxicity/ or exp eye tumor/ or exp glaucoma/ or exp intraocular hemorrhage/ or exp intraocular pressure abnormality/ or exp lens disease/ or exp ocular albinism/ or exp ocular fibrosis/ or exp ocular pruritus/ or exp ocular surface disease/ or exp optic nerve disease/ or exp photophobia/ or exp pupil disease/ or exp retina disease/ or exp sclera disease/ or exp uvea disease/ or exp visual disorder/ or exp vitreous disease/

  8. 8.

    exp vitrectomy/

  9. 9.

    exp phacoemulsification/

  10. 10.

    exp trabeculectomy/

  11. 11.

    personalized medicine/

  12. 12.

    theranostic nanomedicine/

  13. 13.

    algorithm/

  14. 14.

    exp clustering algorithm/

  15. 15.

    artificial intelligence/

  16. 16.

    exp machine learning/

  17. 17.

    "decision tree"/

  18. 18.

    bioinformatics/

  19. 19.

    exp regression analysis/

  20. 20.

    discriminant analysis/

  21. 21.

    gene ontology/

  22. 22.

    proteomics/ or comparative proteomics/ or immunoproteomics/ or exp pharmacoproteomics/ or phosphoproteomics/ or proteogenomics/ or secretomics/

  23. 23.

    proteome/

  24. 24.

    metabolomics/

  25. 25.

    metabolome/

  26. 26.

    lipidomics/

  27. 27.

    lipidome/

  28. 28.

    exp cytokine/

  29. 29.

    exp cell adhesion molecule/

  30. 30.

    myeloperoxidase/

  31. 31.

    "tissue inhibitor of metalloproteinase 1"/

  32. 32.

    "tissue inhibitor of metalloproteinase 2"/

  33. 33.

    brain derived neurotrophic factor/

  34. 34.

    exp angiopoietin/

  35. 35.

    exp "peptides and proteins"/ec [Endogenous Compound]

  36. 36.

    biological marker/ec [Endogenous Compound]

  37. 37.

    1 or 4 or 5 or 6 or 7 or 8 or 9 or 10

  38. 38.

    2 or 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 or 19 or 20 or 21

  39. 39.

    (concentration? or level? or quantif* or quantit* or mass spectrometry or iTRAQ or MALDI or SELDI or assay).tw.

  40. 40.

    (35 or 36) and 39

  41. 41.

    3 or 22 or 23 or 24 or 25 or 26 or 27 or 28 or 29 or 30 or 31 or 32 or 33 or 34 or 40

  42. 42.

    37 and 38 and 41

  43. 43.

    limit 42 to conference abstracts

  44. 44.

    limit 42 to animal studies

  45. 45.

    limit 44 to human

  46. 46.

    limit 42 to "review"

  47. 47.

    44 not 45

  48. 48.

    42 not (43 or 46 or 47)

Medline

  1. 1.

    (ophth* or ocular or intraocular or eye* or retina* or macula* or fovea* or uvea* or sclera* or cornea* or conjunctiva* or iris or "vitreous body" or "vitreous humo?r" or "vitreous fluid" or vitreo* or "aqueous humo?r" or "aqueous fluid" or tears or ((tear or lacrimal) adj fluid) or glaucoma or retinop* or retinoblastoma or uveitis or iritis or choroiditis or retinitis or chorioretinitis or conjunctivitis or endophthalmitis or cataract* or ?????????opia or "optic atrophy" or "optic neuropathy" or vitrectomy or phacoemulsification or trabeculotomy or (paracentesis adj3 "anterior chamber")).tw.

  2. 2.

    ("precision medicine" or "precision health" or "personalized medicine" or "personalized proteomics" or thera?nostic? or "tailored medicine" or "artificial intelligence" or "machine learning" or "deep learning" or algorithm? or ((supervised or unsupervised or biased or unbiased or bayesian or hierarchical or neur??al) adj (cluster* or learning or learner? or classifi* or network?)) or "k-nearest neighbo?r?" or "naive bayes" or (decision adj (tree? or forest? or jungle?)) or "random forest?" or "gradient-boost*" or "support vector machine" or "k-means" or "association rules" or "recursive partitioning" or "discriminant analysis" or "feature selection" or ((linear or nonlinear or "non-linear" or logistic or ordinal or poisson or quantile or analysis) adj1 (regression? or model?)) or bioinformatic? or "gene ontology" or "Kyoto Encyclopedia of Genes and Genomes" or "KEGG" or ((progress* or regress* or recover* or respond* or response*) and (predict* or stratif*))).tw.

  3. 3.

    (Proteomic? or proteome? or metabolomic? or metabolome? or lipidomic? or lipidome? or "?????inflammatory protein?" or "?????inflammatory marker" or cytokine? or interleukin? or lymphokine? or monokine? or interferon? or "colony stimulating factor?" or chemokine? or "growth factor?" or "necrosis factor?" or "chemotactic protein?" or "adhesion molecule?" or "adhesion protein?" or "matrix metalloproteinase-2" or myeloperoxidase? or "tissue inhibitor of metalloproteinase-2" or "macrophage inflammatory protein-1" or "brain-derived neurotrophic factor" or angiopoietin? or ((hemoglobin or haemoglobin) adj1 (a1c or glycated)) or hba1c or "c reactive protein" or "c-reactive protein" or crp or hscrp or "hs-crp" or ((protein or biomarker) and (concentration? or level? or quantif* or quantit* or mass spectrometry or iTRAQ or MALDI or SELDI or assay))).tw.

  4. 4.

    exp Ophthalmology/cl, di, dg, ec, pd, px, sn, sd, su, th, td, ed, es, hi, is, mt, og, rt, st [Classification, Diagnosis, Diagnostic Imaging, Economics, Pharmacology, Psychology, Statistics & Numerical Data, Supply & Distribution, Surgery, Therapy, Trends, Education, Ethics, History, Instrumentation, Methods, Organization & Administration, Radiotherapy, Standards]

  5. 5.

    eye/ or exp anterior eye segment/ or "anterior capsule of the lens"/ or conjunctiva/ or meibomian glands/ or exp "pigment epithelium of eye"/ or exp posterior eye segment/ or exp retina/ or sclera/ or tenon capsule/ or exp uvea/

  6. 6.

    Tears/

  7. 7.

    eye diseases/ or cogan syndrome/ or exp conjunctival diseases/ or exp corneal diseases/ or exp eye abnormalities/ or exp eye diseases, hereditary/ or exp eye hemorrhage/ or exp eye infections/ or exp eye injuries/ or exp eye manifestations/ or exp eye neoplasms/ or exp lens diseases/ or exp ocular hypertension/ or ocular hypotension/ or exp optic nerve diseases/ or exp pupil disorders/ or exp refractive errors/ or exp retinal diseases/ or exp scleral diseases/ or exp uveal diseases/ or exp vision disorders/ or vitreous detachment/

  8. 8.

    Vitrectomy/ae, ec, ed, es, hi, is, mt, mo, nu, px, rh, st, sn, td [Adverse Effects, Economics, Education, Ethics, History, Instrumentation, Methods, Mortality, Nursing, Psychology, Rehabilitation, Standards, Statistics & Numerical Data, Trends]

  9. 9.

    Phacoemulsification/ae, cl, ec, ed, hi, is, mt, mo, nu, px, rh, st, sn, td [Adverse Effects, Classification, Economics, Education, History, Instrumentation, Methods, Mortality, Nursing, Psychology, Rehabilitation, Standards, Statistics & Numerical Data, Trends]

  10. 10.

    Trabeculectomy/nu, px, rh, st, sn, td, ae, cl, ec, ed, hi, is, mt, mo [Nursing, Psychology, Rehabilitation, Standards, Statistics & Numerical Data, Trends, Adverse Effects, Classification, Economics, Education, History, Instrumentation, Methods, Mortality]

  11. 11.

    Precision Medicine/ae, cl, ec, es, hi, is, mt, mo, nu, px, st, sn, td [Adverse Effects, Classification, Economics, Ethics, History, Instrumentation, Methods, Mortality, Nursing, Psychology, Standards, Statistics & Numerical Data, Trends]

  12. 12.

    Theranostic Nanomedicine/

  13. 13.

    exp algorithms/

  14. 14.

    Neural Networks, Computer/

  15. 15.

    Decision Trees/

  16. 16.

    exp Regression Analysis/

  17. 17.

    Discriminant Analysis/

  18. 18.

    exp Proteomics/cl, ec, ed, es, hi, is, mt, og, st, sn, td [Classification, Economics, Education, Ethics, History, Instrumentation, Methods, Organization & Administration, Standards, Statistics & Numerical Data, Trends]

  19. 19.

    Proteome/

  20. 20.

    exp Metabolomics/cl, ec, ed, es, hi, is, mt, og, st, sn, td [Classification, Economics, Education, Ethics, History, Instrumentation, Methods, Organization & Administration, Standards, Statistics & Numerical Data, Trends]

  21. 21.

    Metabolome/

  22. 22.

    exp Cytokines/

  23. 23.

    exp Cell Adhesion Molecules/

  24. 24.

    Matrix Metalloproteinase 2/

  25. 25.

    Peroxidase/

  26. 26.

    "Tissue Inhibitor of Metalloproteinase-1"/

  27. 27.

    "Tissue Inhibitor of Metalloproteinase-2"/Brain-Derived Neurotrophic Factor/

  28. 28.

    exp Angiopoietins/

  29. 29.

    Gene Ontology/

  30. 30.

    exp Proteins/

  31. 31.

    exp Peptides/

  32. 32.

    Biomarkers/

  33. 33.

    (concentration? or level? or quantif* or quantit* or mass spectrometry or iTRAQ or MALDI or SELDI or assay).tw.

  34. 34.

    (31 or 32 or 33) and 3

  35. 35.

    1 or 4 or 5 or 6 or 7 or 8 or 9 or 10

  36. 36.

    2 or 11 or 12 or 13 or 14 or 15 or 16 or 17 or 30

  37. 37.

    3 or 18 or 19 or 20 or 21 or 22 or 23 or 24 or 25 or 26 or 27 or 28 or 29 or 35

  38. 38.

    36 and 37 and 38

  39. 39.

    limit 39 to animals

  40. 40.

    limit 40 to humans

  41. 41.

    40 not 41

  42. 42.

    limit 39 to "review articles"

  43. 43.

    39 not (42 or 43)

Web of science

  1. 1.

    (TI = (ophth* or ocular or intraocular or eye* or retina* or macula* or fovea* or uvea* or sclera* or cornea* or conjunctiva* or iris or "vitreous body" or "vitreous humo$r" or "vitreous fluid" or vitreo* or "aqueous humo$r" or "aqueous fluid" or tears or ((tear or lacrimal) NEAR/1 fluid) or glaucoma or retinop* or retinoblastoma or uveitis or iritis or choroiditis or retinitis or chorioretinitis or conjunctivitis or endophthalmitis or cataract* or *opia or "optic atrophy" or "optic neuropathy" or vitrectomy or phacoemulsification or trabeculotomy.tw. or (paracentesis NEAR/3 "anterior chamber")) or AB = (ophth* or ocular or intraocular or eye* or retina* or macula* or fovea* or uvea* or sclera* or cornea* or conjunctiva* or iris.tw. or "vitreous body" or "vitreous humo$r" or "vitreous fluid" or vitreo* or "aqueous humo$r" or "aqueous fluid" or tears or ((tear or lacrimal) NEAR/1 fluid) or glaucoma or retinop* or retinoblastoma or uveitis or iritis or choroiditis or retinitis or chorioretinitis or conjunctivitis or endophthalmitis or cataract* or "optic atrophy" or "optic neuropathy" or vitrectomy or phacoemulsification or trabeculotomy or (paracentesis NEAR/3 "anterior chamber"))) AND

  2. 2.

    (TI = ("precision medicine" or "precision health" or "personalized medicine" or "personalized proteomics" or thera$nostic* or "tailored medicine" or "artificial intelligence" or "machine learning" or "deep learning" or algorithm$ or ((supervised or unsupervised or biased or unbiased or bayesian or hierarchical or neural or neuronal) NEAR/1 (cluster* or learning or learner$ or classifi* or network$)) or "k-nearest neighbo$r*" or "naive bayes" or (decision NEAR/1 (tree$ or forest$ or jungle$)) or "random forest$" or "gradient-boost*" or "support vector machine" or "k-means" or "association rules" or "recursive partitioning" or "discriminant analysis" or "feature selection" or ((linear or nonlinear or "non-linear" or logistic or ordinal or poisson or quantile or analysis) NEAR/1 (regression$ or model$)) or bioinformatic$ or ((progress* or regress* or recover* or respond* or response*) and (predict* or stratif*))) OR AB = ("precision medicine" or "precision health" or "personalized medicine" or "personalized proteomics" or thera$nostic* or "tailored medicine" or "artificial intelligence" or "machine learning" or "deep learning" or algorithm$ or ((supervised or unsupervised or biased or unbiased or bayesian or hierarchical or neural or neuronal) NEAR/1 (cluster* or learning or learner$ or classifi* or network$)) or "k-nearest neighbo$r*" or "naive bayes" or (decision NEAR/1 (tree$ or forest$ or jungle$)) or "random forest$" or "gradient-boost*" or "support vector machine" or "k-means" or "association rules" or "recursive partitioning" or "discriminant analysis" or "feature selection" or ((linear or nonlinear or "non-linear" or logistic or ordinal or poisson or quantile or analysis) NEAR/1 (regression$ or model$)) or bioinformatic$ or ((progress* or regress* or recover* or respond* or response*) and (predict* or stratif*)))) AND

  3. 3.

    (TI = (Proteomic$ or proteome$ or metabolomic$ or metabolome$ or lipidomic$ or lipidome$ or "*inflammatory protein$" or "*inflammatory marker" or cytokine$ or interleukin$ or lymphokine$ or monokine$ or interferon$ or "colony stimulating factor$" or chemokine$ or "growth factor$" or "necrosis factor$" or "chemotactic protein$" or "adhesion molecule$" or "adhesion protein$" or "matrix metalloproteinase-2" or myeloperoxidase$ or "tissue inhibitor of metalloproteinase-2" or "macrophage inflammatory protein-1" or "brain-derived neurotrophic factor" or angiopoietin$ or ((hemoglobin or haemoglobin) NEAR/1 (a1c or glycated or glycosylated)) or hba1c or "c reactive protein" or "c-reactive protein" or crp or hscrp or "hs-crp" or ((protein or biomarker) and (concentration$ or level$ or quantif* or quantit* or mass spectrometry or iTRAQ or MALDI or SELDI or assay))) OR AB = (Proteomic$ or proteome$ or metabolomic$ or metabolome$ or lipidomic$ or lipidome$ or "inflammatory protein$" or "inflammatory marker" or cytokine$ or interleukin$ or lymphokine$ or monokine$ or interferon$ or "colony stimulating factor$" or chemokine$ or "growth factor$" or "necrosis factor$" or "chemotactic protein$" or "adhesion molecule$" or "adhesion protein$" or "matrix metalloproteinase-2" or myeloperoxidase$ or "tissue inhibitor of metalloproteinase-2" or "macrophage inflammatory protein-1" or "brain-derived neurotrophic factor" or angiopoietin$ or ((hemoglobin or haemoglobin) NEAR/1 (a1c or glycated or glycosylated)) or hba1c or "c reactive protein" or "c-reactive protein" or crp or hscrp or "hs-crp" or ((protein or biomarker) and (concentration$ or level$ or quantif* or quantit* or mass spectrometry or iTRAQ or MALDI or SELDI or assay))))

Cochrane central register of controlled trials (CONTROL), cochrane database of systematic reviews

  1. 1.

    ophth* or ocular or intraocular or eye* or retina* or macula* or fovea* or uvea* or sclera* or cornea* or conjunctiva* or iris or "vitreous body" or "vitreous humo*r" or "vitreous fluid" or vitreo* or "aqueous humo*r" or "aqueous fluid" or tears or ((tear or lacrimal) NEXT fluid) or glaucoma or retinop* or retinoblastoma or uveitis or iritis or choroiditis or retinitis or chorioretinitis or conjunctivitis or endophthalmitis or cataract* or *opia or "optic atrophy" or "optic neuropathy" or vitrectomy or phacoemulsification or trabeculotomy or (paracentesis NEAR/3 "anterior chamber")

  2. 2.

    "precision medicine" or "precision health" or "personalized medicine" or "personalized proteomics" or thera*nostic* or "tailored medicine" or "artificial intelligence" or "machine learning" or "deep learning" or algorithm* or ((supervised or unsupervised or biased or unbiased or bayesian or hierarchical or neural or neuronal) NEXT (cluster* or learning or learner* or classifi* or network*)) or "k-nearest neighbo*r*" or "naive bayes" or (decision NEXT (tree* or forest* or jungle*)) or "random forest*" or "gradient-boost*" or "support vector machine" or "k-means" or "association rules" or "recursive partitioning" or "discriminant analysis" or "feature selection" or ((linear or nonlinear or "non-linear" or logistic or ordinal or poisson or quantile or analysis) NEXT (regression* or model*)) or bioinformatic* or ((progress* or regress* or recover* or respond* or response*) and (predict* or stratif*))

  3. 3.

    Proteomic* or proteome* or metabolomic* or metabolome* or lipidomic* or lipidome* or "*inflammatory protein*" or "*inflammatory marker*" or cytokine* or interleukin* or lymphokine* or monokine$ or interferon* or "colony stimulating factor*" or chemokine* or "growth factor*" or "necrosis factor*" or "chemotactic protein*" or "adhesion molecule*" or "adhesion protein*" or "matrix metalloproteinase-2" or myeloperoxidase* or "tissue inhibitor of metalloproteinase-2" or "macrophage inflammatory protein-1" or "brain-derived neurotrophic factor" or angiopoietin* or ((hemoglobin or haemoglobin) NEXT (a1c or glycated or glycosylated)) or hba1c or "c reactive protein" or "c-reactive protein" or crp or hscrp or "hs-crp" or ((protein or biomarker) and (concentration* or level* or quantif* or quantit* or mass spectrometry or iTRAQ or MALDI or SELDI or assay))

  4. 4.

    MeSH descriptor: [Ophthalmology]

  5. 5.

    MeSH descriptor: [Eye]

  6. 6.

    MeSH descriptor: [Tears]

  7. 7.

    MeSH descriptor: [Eye Diseases]

  8. 8.

    MeSH descriptor: [Vitrectomy]

  9. 9.

    MeSH descriptor: [Phacoemulsification]

  10. 10.

    MeSH descriptor: [Trabeculectomy]

  11. 11.

    MeSH descriptor: [Precision Medicine]

  12. 12.

    MeSH descriptor: [Theranostic Nanomedicine]

  13. 13.

    MeSH descriptor: [Artificial Intelligence]

  14. 14.

    MeSH descriptor: [Algorithms]

  15. 15.

    MeSH descriptor: [Neural Networks, Computer]

  16. 16.

    MeSH descriptor: [Decision Trees]

  17. 17.

    MeSH descriptor: [Regression Analysis]

  18. 18.

    MeSH descriptor: [Discriminant Analysis]

  19. 19.

    MeSH descriptor: [Proteomics]

  20. 20.

    MeSH descriptor: [Metabolomics]

  21. 21.

    MeSH descriptor: [Proteins] explode

  22. 22.

    MeSH descriptor: [Peptides]

  23. 23.

    MeSH descriptor: [Biomarkers]

  24. 24.

    concentration* or level* or quantif* or quantit* or mass spectrometry or iTRAQ or MALDI or SELDI or assay

  25. 25.

    (#21 or #22 or #23) and #24

  26. 26.

    MeSH descriptor: [Cytokines]

  27. 27.

    MeSH descriptor: [Cytokines]

  28. 28.

    MeSH descriptor: [Matrix Metalloproteinase 2]

  29. 29.

    MeSH descriptor: [Peroxidase]

  30. 30.

    MeSH descriptor: [Tissue Inhibitor of Metalloproteinase-1]

  31. 31.

    MeSH descriptor: [Tissue Inhibitor of Metalloproteinase-2]

  32. 32.

    MeSH descriptor: [Brain-Derived Neurotrophic Factor

  33. 33.

    MeSH descriptor: [Angiopoietins]

  34. 34.

    MeSH descriptor: [Anterior Eye Segment]

  35. 35.

    MeSH descriptor: [Anterior Capsule of the Lens]

  36. 36.

    MeSH descriptor: [Axial Length, Eye]

  37. 37.

    MeSH descriptor: [Pigment Epithelium of Eye]

  38. 38.

    MeSH descriptor: [Posterior Eye Segment]

  39. 39.

    MeSH descriptor: [Retina]

  40. 40.

    MeSH descriptor: [Sclera]

  41. 41.

    MeSH descriptor: [Tenon Capsule]

  42. 42.

    MeSH descriptor: [Uvea]

  43. 43.

    MeSH descriptor: [Asthenopia]

  44. 44.

    MeSH descriptor: [Cogan Syndrome]

  45. 45.

    MeSH descriptor: [Conjunctival Diseases]

  46. 46.

    MeSH descriptor: [Corneal Diseases]

  47. 47.

    MeSH descriptor: [Eye Abnormalities]

  48. 48.

    MeSH descriptor: [Eye Diseases, Hereditary]

  49. 49.

    MeSH descriptor: [Eye Hemorrhage]

  50. 50.

    MeSH descriptor: [Eye Infections]

  51. 51.

    MeSH descriptor: [Eye Injuries]

  52. 52.

    MeSH descriptor: [Eye Manifestations]

  53. 53.

    MeSH descriptor: [Eye Neoplasms]

  54. 54.

    MeSH descriptor: [Lens Diseases]

  55. 55.

    MeSH descriptor: [Ocular Hypertension]

  56. 56.

    MeSH descriptor: [Ocular Hypotension]

  57. 57.

    MeSH descriptor: [Optic Nerve Diseases]

  58. 58.

    MeSH descriptor: [Pupil Disorders]

  59. 59.

    MeSH descriptor: [Refractive Errors]

  60. 60.

    MeSH descriptor: [Retinal Diseases]

  61. 61.

    MeSH descriptor: [Scleral Diseases]

  62. 62.

    MeSH descriptor: [Uveal Diseases]

  63. 63.

    MeSH descriptor: [Vision Disorders]

  64. 64.

    MeSH descriptor: [Vitreous Detachment]

  65. 65.

    #1 or #4 or #5 or #6 or #7 or #8 or #9 or #10 or #34 or #35 or #36 or #37 or #38 or #39 or #40 or #41 or #42 or #43 or #44 or #45 or #46 or #47 or #48 or #49 or #50 or #51 or #52 or #53 or #54 or #55 or #56 or #57 or #58 or #59 or #60 or #61 or #62 or #63 or #64

  66. 66.

    #2 or #11 or #12 or #13 or #14 or #15 or #16 or #17 or #18

  67. 67.

    #3 or #19 or #20 or #25 or #26 or #27 or #28 or #29 or #30 or #31 or #32 or #33

  68. 68.

    #65 and #66 and #67

Appendix B

Table

Table 4 Characteristics of all studies identified
Full size table

4

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pucchio, A., Krance, S.H., Pur, D.R. et al. Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review. Graefes Arch Clin Exp Ophthalmol 262, 1041–1091 (2024). https://doi.org/10.1007/s00417-023-06100-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00417-023-06100-6

Keywords

Search

Navigation