Skip to main content
Log in

A Systematic Review of Artificial Intelligence (AI) Based Approaches for the Diagnosis of Parkinson’s Disease

  • Review article
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder that primarily affects the elderly for over 55 years. PD can be characterised by patients exhibiting various non-motor and motor symptoms. It is significant to note that even though modern-day medical technology has grown exponentially over the years, there is still no cure for Parkinson’s disease. Hence, it is a scientifically exciting proposal to develop technologies that diagnose Parkinson’s disease earlier. Early diagnosis of PD can enhance the patient’s quality of life to a reasonable extent, as the disease’s nature is progressive, and it may take years to cripple the patient. It is also essential to observe that the symptoms will get intensified over time. Early diagnosis can also predict other types of neurodegenerative diseases, as the symptoms are pretty similar. The idea of Artificial Intelligence (AI) techniques is recently getting significant medical diagnosis attention, as these technologies can process massive data and come up with good statistical predictions. This study presents a detailed review of various machine learning and deep learning-based AI techniques applied to PD diagnosis and their impact in opening up newer research avenues. Furthermore, this paper explores the possible opportunities of data-driven AI technologies in PD diagnosis and its current status.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. DeMaagd G, PharmD BCPS, Ashok Philip P (2015) Parkinson’s disease and its management. BMJ 308:281. https://doi.org/10.1136/bmj.308.6923.281

    Article  Google Scholar 

  2. Nawar A, Rahman F, Krishnamurthi N, Som A, Turaga P (2020) Topological descriptors for parkinson’s disease classification and regression analysis. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS pp 793–797 . https://doi.org/10.1109/EMBC44109.2020.9176285

  3. Mischley LK, Lau RC, Weiss NS (2017) Use of a self-rating scale of the nature and severity of symptoms in Parkinson’s disease (PRO-PD): correlation with quality of life and existing scales of disease severity. npj Park Dis 3:1–6. https://doi.org/10.1038/s41531-017-0021-5

    Article  Google Scholar 

  4. Bougea A (2020) New markers in Parkinson’s disease. In: Advances in clinical chemistry. Academic Press Inc., Cambridge

  5. Tang Y, Meng L, Wan CM, Liu ZH, Liao WH, Yan XX, Wang XY, Tang BS, Guo JF (2017) Identifying the presence of Parkinson’s disease using low-frequency fluctuations in BOLD signals. Neurosci Lett 645:1–6. https://doi.org/10.1016/j.neulet.2017.02.056

    Article  Google Scholar 

  6. Zhang H, Song C, Rathore AS, Huang M, Zhang Y, Xu W (2020) mHealth technologies towards Parkinson’s disease detection and monitoring in daily life: a comprehensive review. IEEE Rev Biomed Eng 3333:2–12. https://doi.org/10.1109/RBME.2020.2991813

    Article  Google Scholar 

  7. Richens JG, Lee CM, Johri S (2020) Improving the accuracy of medical diagnosis with causal machine learning. Nat Commun 11:1–9. https://doi.org/10.1038/s41467-020-17419-7

    Article  Google Scholar 

  8. Ray Dorsey E, Elbaz A (2018) Global, regional, and national burden of Parkinson’s disease, 1990–2016: a systematic analysis for the global burden of disease study 2016. Lancet Neurol 17:939–953. https://doi.org/10.1016/S1474-4422(18)30295-3

    Article  Google Scholar 

  9. Yang W, Hamilton JL, Kopil C, Beck JC, Tanner CM, Albin RL, Ray Dorsey E, Dahodwala N, Cintina I, Hogan P, Thompson T (2020) Current and projected future economic burden of Parkinson’s disease in the US. npj Park Dis 6:1–9. https://doi.org/10.1038/s41531-020-0117-1

    Article  Google Scholar 

  10. Surathi P, Jhunjhunwala K, Yadav R, Pal PK (2016) Research in Parkinson’s disease in India: a review. Ann Indian Acad Neurol 19:9–20

    Google Scholar 

  11. Ker J, Wang L, Rao J, Lim T (2017) Deep learning applications in medical image analysis. IEEE Access 6:9375–9379. https://doi.org/10.1109/ACCESS.2017.2788044

    Article  Google Scholar 

  12. Singh P, Singh SP, Singh DS (2019) An introduction and review on machine learning applications in medicine and healthcare. 2019 IEEE Conf Inf Commun Technol CICT 2019. https://doi.org/10.1109/CICT48419.2019.9066250

  13. Kononenko I (2001) Machine learning for medical diagnosis: History, state of the art and perspective. Artif Intell Med 23:89–109. https://doi.org/10.1016/S0933-3657(01)00077-X

    Article  Google Scholar 

  14. Gagliano M, Van Pham J, Tang B, Kashif H and Ban J (2017) Applications of machine learning in medical diagnosis. [online] Available: https://www.researchgate.net/publication/321151498_Applications_of_Machine_Learning_in_Medical_Diagnosis.

  15. Zhou LQ, Wang JY, Yu SY, Wu GG, Wei Q, Bin DY, Wu XL, Cui XW, Dietrich CF (2019) Artificial intelligence in medical imaging of the liver. World J Gastroenterol 25:672–682. https://doi.org/10.3748/wjg.v25.i6.672

    Article  Google Scholar 

  16. Pereira CR, Pereira DR, Weber SAT, Hook C, de Albuquerque VHC, Papa JP (2019) A survey on computer-assisted Parkinson’s disease diagnosis. Artif Intell Med 95:48–63. https://doi.org/10.1016/j.artmed.2018.08.007

    Article  Google Scholar 

  17. Sharma P, Sundaram S, Sharma M, Sharma A, Gupta D (2019) Diagnosis of Parkinson’s disease using modified grey wolf optimisation. Cogn Syst Res 54:100–115. https://doi.org/10.1016/j.cogsys.2018.12.002

    Article  Google Scholar 

  18. Gupta D, Julka A, Jain S, Aggarwal T, Khanna A, Arunkumar N, de Albuquerque VHC (2018) Optimised cuttlefish algorithm for diagnosis of Parkinson’s disease. Cogn Syst Res 52:36–48. https://doi.org/10.1016/j.cogsys.2018.06.006

    Article  Google Scholar 

  19. Karapinar Senturk Z (2020) Early diagnosis of Parkinson’s disease using machine learning algorithms. Med Hypotheses 138:109603. https://doi.org/10.1016/j.mehy.2020.109603

    Article  Google Scholar 

  20. Sharma V, Kaur S, Kumar J, Singh AK (2019) A fast parkinson’s disease prediction technique using PCA and artificial neural network. Int Conf Intell Comput Control Syst ICCS 2019:1491–1496. https://doi.org/10.1109/ICCS45141.2019.9065876

    Article  Google Scholar 

  21. Cai Z, Gu J, Chen HL (2017) A new hybrid intelligent framework for predicting Parkinson’s disease. IEEE Access 5:17188–17200. https://doi.org/10.1109/ACCESS.2017.2741521

    Article  Google Scholar 

  22. Wang M, Ge W, Apthorp D, Suominen H (2020) Robust feature engineering for Parkinson disease diagnosis: new machine learning techniques. JMIR Biomed Eng 5:e13611. https://doi.org/10.2196/13611

    Article  Google Scholar 

  23. Tuncer T, Dogan S (2019) A novel octopus based Parkinson’s disease and gender recognition method using vowels. Appl Acoust 155:75–83. https://doi.org/10.1016/j.apacoust.2019.05.019

    Article  Google Scholar 

  24. Younis Thanoun M, Yaseen MT (2020) A comparative study of Parkinson disease diagnosis in machine learning. ACM Int Conf Proceed Ser. https://doi.org/10.1145/3441417.3441425

    Article  Google Scholar 

  25. Bhurane AA, Dhok S, Sharma M, Yuvaraj R, Murugappan M, Acharya UR (2019) Diagnosis of Parkinson’s disease from electroencephalography signals using linear and self similarity features. Expert Syst. https://doi.org/10.1111/exsy.12472

    Article  Google Scholar 

  26. Yuvaraj R, Rajendra Acharya U, Hagiwara Y (2018) A novel Parkinson’s disease diagnosis index using higher-order spectra features in EEG signals. Neural Comput Appl 30:1225–1235. https://doi.org/10.1007/s00521-016-2756-z

    Article  Google Scholar 

  27. Jebakumari VS, Shanthi D, Sridevi S, Meha P (2018) Performance evaluation of various classification algorithms for the diagnosis of Parkinson’s disease. Proc 2017 IEEE Int Conf Intell Tech Control Optim Signal Process INCOS 2017, pp 1–7 https://doi.org/10.1109/ITCOSP.2017.8303089

  28. Georgiopoulos C, Witt ST, Haller S, Dizdar N, Zachrisson H, Engström M, Larsson, (2019) A study of neural activity and functional connectivity within the olfactory brain network in Parkinson’s disease. NeuroImage Clin 23:1491–1496. https://doi.org/10.1016/j.nicl.2019.101946

    Article  Google Scholar 

  29. Singh G, Vadera M, Samavedham L, Lim ECH (2019) Multiclass diagnosis of neurodegenerative diseases: a neuroimaging machine-learning-based approach †. NeuroImage Clin 7:21710–21745. https://doi.org/10.1016/j.jneumeth.2013.11.016

    Article  Google Scholar 

  30. Kazeminejad A, Golbabaei S, Soltanian-Zadeh H (2018) Graph theoretical metrics and machine learning for diagnosis of Parkinson’s disease using rs-fMRI. 19th CSI Int Symp Artif Intell Signal Process AISP 2017, pp 134–139 https://doi.org/10.1109/AISP.2017.8324124

  31. Wabnegger A, Ille R, Schwingenschuh P, Katschnig-winter P (2015) Facial emotion recognition in Parkinson’s disease : an fmri investigation. PLoS ONE 10(8):e0136110. https://doi.org/10.1371/journal.pone.0136110

    Article  Google Scholar 

  32. Rojas A, Górriz JM, Ramírez J, Illán IA, Martínez-Murcia FJ, Ortiz A, Gómez Río M, Moreno-Caballero M (2013) Application of empirical mode decomposition (EMD) on DaTSCAN SPECT images to explore Parkinson disease. Expert Syst Appl 40:2756–2766. https://doi.org/10.1016/j.eswa.2012.11.017

    Article  Google Scholar 

  33. Rana B, Juneja A, Saxena M, Gudwani S, Senthil Kumaran S, Agrawal RK, Behari M (2015) Regions-of-interest based automated diagnosis of Parkinson’s disease using T1-weighted MRI. Expert Syst Appl 42:4506–4516. https://doi.org/10.1016/j.eswa.2015.01.062

    Article  Google Scholar 

  34. Feis DL, Pelzer EA, Timmermann L, Tittgemeyer M (2015) Classification of symptom-side predominance in idiopathic Parkinson’s disease. npj Park Dis 1:2–4. https://doi.org/10.1038/npjparkd.2015.18

    Article  Google Scholar 

  35. Chakraborty S, Aich S, Kim H-C (2020) 3D textural, morphological and statistical analysis of voxel of interests in 3T MRI scans for the detection of Parkinson’s disease using artificial neural networks. Healthcare 8:34. https://doi.org/10.3390/healthcare8010034

    Article  Google Scholar 

  36. Mabrouk R, Chikhaoui B, Bentabet L (2018) Machine learning based classification using clinical and DaTSCAN SPECT imaging features: a study on Parkinson’s disease and SWEDD. IEEE Trans Radiat Plasma Med Sci 3:170–177. https://doi.org/10.1109/trpms.2018.2877754

    Article  Google Scholar 

  37. Kour N, Sunanda AS (2019) Computer-vision based diagnosis of Parkinson’s disease via gait: a survey. IEEE Access 7:156620–156645. https://doi.org/10.1109/ACCESS.2019.2949744

    Article  Google Scholar 

  38. Ali L, Zhu C, Zhang Z, Liu Y (2019) Automated detection of Parkinson’s disease based on multiple types of sustained phonations using linear discriminant analysis and genetically optimised neural network. IEEE J Transl Eng Heal Med. https://doi.org/10.1109/JTEHM.2019.2940900

    Article  Google Scholar 

  39. Akyol K (2017) A study on the diagnosis of Parkinson’s disease using digitized wacom graphics tablet dataset. Int J Inf Technol Comput Sci 9:45–51. https://doi.org/10.5815/ijitcs.2017.12.06

    Article  Google Scholar 

  40. Drotár P, Mekyska J, Rektorová I, Masarová L, Smékal Z, Faundez-Zanuy M (2015) Decision support framework for Parkinson’s disease based on novel handwriting markers. IEEE Trans Neural Syst Rehabil Eng 23:508–516. https://doi.org/10.1109/TNSRE.2014.2359997

    Article  Google Scholar 

  41. Pereira CR, Pereira DR, Rosa GH, Albuquerque VHC, Weber SAT, Hook C, Papa JP (2018) Handwritten dynamics assessment through convolutional neural networks: an application to Parkinson’s disease identification. Artif Intell Med 87:67–77. https://doi.org/10.1016/j.artmed.2018.04.001

    Article  Google Scholar 

  42. Rosenblum S, Samuel M, Zlotnik S, Erikh I, Schlesinger I (2013) Handwriting as an objective tool for Parkinson’s disease diagnosis. J Neurol 260:2357–2361. https://doi.org/10.1007/s00415-013-6996-x

    Article  Google Scholar 

  43. Printy BP, Renken LM, Herrmann JP, Lee I, Johnson B, Knight E, Varga G, Whitmer D (2014) Smartphone application for classification of motor impairment severity in Parkinson’s disease. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf 2014:2686–2689. https://doi.org/10.1109/embc.2014.6944176

    Article  Google Scholar 

  44. Wahid F, Begg RK, Hass CJ, Halgamuge S, Ackland DC (2015) Classification of Parkinson’s disease gait using spatial-temporal gait features. IEEE J Biomed Heal Informatics 19:1794–1802. https://doi.org/10.1109/JBHI.2015.2450232

    Article  Google Scholar 

  45. Mazilu S, Hardegger M, Zhu Z, Roggen D, Tr G, Plotnik M, Hausdorff JM (2012) Online detection of freezing of gait with smartphones and machine learning techniques. In: 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, pp 123-130 https://doi.org/10.4108/icst.pervasivehealth.2012.248680

  46. Adams WR (2017) High-accuracy detection of early Parkinson’s Disease using multiple characteristics of finger movement while typing. PLoS ONE. https://doi.org/10.1371/journal.pone.0188226

    Article  Google Scholar 

  47. Aghanavesi S, Nyholm D, Senek M, Bergquist F, Memedi M (2017) Informatics in medicine unlocked a smartphone-based system to quantify dexterity in Parkinson ’ s disease patients. Informatics Med Unlocked 9:11–17. https://doi.org/10.1016/j.imu.2017.05.005

    Article  Google Scholar 

  48. Ul Haq A, Li J, Memon MH, Khan J, Din SU, Ahad I, Sun R, Lai Z (2019) Comparative analysis of the classification performance of machine learning classifiers and deep neural network classifier for prediction of Parkinson disease. 2018 15th Int Comput Conf Wavelet Act Media Technol Inf Process ICCWAMTIP l:101–106. https://doi.org/10.1109/ICCWAMTIP.2018.8632613

  49. Soumaya Z, Drissi Taoufiq B, Benayad N, Yunus K, Abdelkrim A (2021) The detection of Parkinson disease using the genetic algorithm and SVM classifier. Appl Acoust 171:107528. https://doi.org/10.1016/j.apacoust.2020.107528

    Article  Google Scholar 

  50. Sakar CO, Serbes G, Gunduz A, Tunc HC, Nizam H, Sakar BE, Tutuncu M, Aydin T, Isenkul ME, Apaydin H (2019) A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform. Appl Soft Comput J 74:255–263. https://doi.org/10.1016/j.asoc.2018.10.022

    Article  Google Scholar 

  51. Prashanth R, Dutta Roy S (2018) Novel and improved stage estimation in Parkinson’s disease using clinical scales and machine learning. Neurocomputing 305:78–103. https://doi.org/10.1016/j.neucom.2018.04.049

    Article  Google Scholar 

  52. Benmalek E, Elmhamdi J, Jilbab A (2017) Multiclass classification of Parkinson’s disease using different classifiers and LLBFS feature selection algorithm. Int J Speech Technol 20:179–184. https://doi.org/10.1007/s10772-017-9401-9

    Article  Google Scholar 

  53. Smekal Z, Mekyska J, Galaz Z, Mzourek Z, Rektorova I, Faundez-Zanuy M (2015) Analysis of phonation in patients with Parkinson’s disease using empirical mode decomposition. ISSCS 2015 - Int Symp Signals, Circuits Syst, pp 1–4. https://doi.org/10.1109/ISSCS.2015.7203931

  54. Hariharan M, Polat K, Sindhu R (2014) A new hybrid intelligent system for accurate detection of Parkinson’s disease. Comput Methods Programs Biomed 113:904–913. https://doi.org/10.1016/j.cmpb.2014.01.004

    Article  Google Scholar 

  55. Avci D, Dogantekin A (2016) An expert diagnosis system for Parkinson disease based on genetic algorithm-wavelet kernel-extreme learning machine. Parkinsons Dis. https://doi.org/10.1155/2016/5264743

    Article  Google Scholar 

  56. Das S, Trutoiu L, Murai A, Alcindor D, Oh M, De La Torre F, Hodgins J (2011) Quantitative measurement of motor symptoms in Parkinson’s disease: a study with full-body motion capture data. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS. https://doi.org/10.1109/IEMBS.2011.6091674

    Article  Google Scholar 

  57. Chen HL, Huang CC, Yu XG, Xu X, Sun X, Wang G, Wang SJ (2013) An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach. Expert Syst Appl 40:263–271. https://doi.org/10.1016/j.eswa.2012.07.014

    Article  Google Scholar 

  58. Almeida JS, Rebouças Filho PP, Carneiro T, Wei W, Damaševičius R, Maskeliūnas R, de Albuquerque VHC (2019) Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques. Pattern Recognit Lett 125:55–62. https://doi.org/10.1016/j.patrec.2019.04.005

    Article  Google Scholar 

  59. Karabayir I, Goldman SM, Pappu S, Akbilgic O (2020) Gradient boosting for Parkinson’s disease diagnosis from voice recordings. BMC Med Informatics Decis Making 20(1):1–7

    Article  Google Scholar 

  60. Bhosale MPG, Patil S (2012) Classification of EMG signals using wavelet transform and hybrid classifier for parkinson’s disease detection. Int J Eng Res Technol 2:106–112

    Google Scholar 

  61. Sharma P, Sundaram S, Sharma M, Sharma (2019) Multiclass diagnosis of neurodegenerative diseases: a neuroimaging machine-learning-based approach †. NeuroImage Clin 7:21710–21745. https://doi.org/10.1016/j.jneumeth.2013.11.016

  62. Peng B, Wang S, Zhou Z, Liu Y, Tong B, Zhang T, Dai Y (2017) A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson’s disease. Neurosci Lett 651:88–94. https://doi.org/10.1016/j.neulet.2017.04.034

    Article  Google Scholar 

  63. Sateesh Babu G, Suresh S, Mahanand BS (2014) A novel PBL-McRBFN-RFE approach for identification of critical brain regions responsible for Parkinson’s disease. Expert Syst Appl 41:478–488. https://doi.org/10.1016/j.eswa.2013.07.073

    Article  Google Scholar 

  64. Abós A, Baggio HC, Segura B, García-Díaz AI, Compta Y, Martí MJ, Valldeoriola F, Junqué C (2017) Discriminating cognitive status in Parkinson’s disease through functional connectomics and machine learning. Sci Rep 7:1–13. https://doi.org/10.1038/srep45347

    Article  Google Scholar 

  65. Rubbert C, Mathys C, Jockwitz C, Hartmann CJ, Eickhoff SB, Hoffstaedter F, Caspers S, Eickhoff CR, Sigl B, Teichert NA, Südmeyer M (2019) Machine-learning identifies Parkinson’s disease patients based on resting-state between-network functional connectivity. Br J Radiol 92(1101):20180886

    Article  Google Scholar 

  66. Zhang L, Liu C, Zhang X, Tang YY (2017) Classification of Parkinson’s disease and essential tremor based on structural MRI. Proc - 2016 7th Int Conf Cloud Comput Big Data, CCBD. pp 353–356 https://doi.org/10.1109/CCBD.2016.075

  67. Chen Y, Yang W, Long J, Zhang Y, Feng J, Li Y, Huang B (2015) Discriminative analysis of Parkinson’s disease based on whole-brain functional connectivity. PLoS ONE 10:1–16. https://doi.org/10.1371/journal.pone.0124153

    Article  Google Scholar 

  68. Adeli E, Shia F, Ana L, Weea CY, b, Wua G, Wanga T, c, d and DS (2016) Joint feature-sample selection and robust diagnosis of Parkinson’s disease from MRI data. Neuroimage 176:1570–1573. https://doi.org/10.1038/s41395-018-0061-4

  69. Zeng LL, Xie L, Shen H, Luo Z, Fang P, Hou Y, Tang B, Wu T, Hu D (2017) Differentiating patients with Parkinson’s disease from normal controls using gray matter in the cerebellum. Cerebellum 16:151–157. https://doi.org/10.1007/s12311-016-0781-1

    Article  Google Scholar 

  70. Juutinen M, Wang C, Zhu J, Haladjian J, Ruokolainen J, Puustinen J, Vehkaoja A (2020) Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study. PLoS ONE 15:1–19. https://doi.org/10.1371/journal.pone.0236258

    Article  Google Scholar 

  71. Félix JP, Vieira FHT, Cardoso ÁA, Ferreira MVG, Franco RAP, Ribeiro MA, Araújo SG, Corrêa HP, Carneiro ML (2019) A Parkinson’s disease classification method: an approach using gait dynamics and detrended fluctuation analysis. 2019 IEEE Can Conf Electr Comput Eng CCECE, pp 57–60. https://doi.org/10.1109/CCECE.2019.8861759

  72. Nancy Noella RS, Gupta D, Priyadarshini J (2019) Diagnosis of Parkinson’s disease using gait dynamics and images. Procedia Comput Sci 165:428–434. https://doi.org/10.1016/j.procs.2020.01.002

    Article  Google Scholar 

  73. Pereira CR, Pereira DR, Silva FAD, Hook C, Weber SAT, Pereira LAM, Papa JP (2015) A step towards the automated diagnosis of parkinson’s disease: Analysing handwriting movements. Proc - IEEE Symp Comput Med Syst pp 171–176 https://doi.org/10.1109/CBMS.2015.34

  74. Bhatele KR, Bhadauria SS (2020) Brain structural disorders detection and classification approaches: a review. Artif Intell Rev 53:3349–3401. https://doi.org/10.1007/s10462-019-09766-9

    Article  Google Scholar 

  75. Alissa M (2021) Parkinson’s disease diagnosis using deep learning. arXiv preprint arXiv:2101.05631

  76. Begum A, Fatima F, Sabahath A (2019) Implementation of deep learning algorithm with perceptron using tenzorflow library. Proc 2019 IEEE Int Conf Commun Signal Process ICCSP, pp 172–175. https://doi.org/10.1109/ICCSP.2019.8697910

  77. Jamshidi M, Lalbakhsh A, Talla J, Peroutka Z, Hadjilooei F, Lalbakhsh P, Jamshidi M, La SL, Mirmozafari M, Dehghani M, Sabet A, Roshani S, Roshani S, Bayat-Makou N, Mohamadzade B, Malek Z, Jamshidi A, Kiani S, Hashemi-Dezaki H, Mohyuddin W (2020) Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment. IEEE Access 8:109581–109595. https://doi.org/10.1109/ACCESS.2020.3001973

    Article  Google Scholar 

  78. Caliskan A, Badem H, Basturk A, Yuksel ME (2017) Diagnosis of the parkinson disease by using deep neural network classifier. IU-J Electr Electron Eng 17(2):3311–3318

    Google Scholar 

  79. Xiong Y, Lu Y (2020) Deep feature extraction from the vocal vectors using sparse autoencoders for Parkinson’s classification. IEEE Access 8:27821–27830. https://doi.org/10.1109/ACCESS.2020.2968177

    Article  Google Scholar 

  80. Frid A, Kantor A, Svechin D, Manevitz LM (2016) Diagnosis of Parkinson’s disease from continuous speech using deep convolutional networks without manual selection of features. In: 2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE). pp 1–4 https://doi.org/10.1109/ICSEE.2016.7806118.

  81. Zahid L, Maqsood M, Durrani MY, Bakhtyar M, Baber J, Jamal H, Mehmood I, Song OY (2020) A spectrogram-based deep feature assisted computer-aided diagnostic system for Parkinson’s disease. IEEE Access 8:35482–35495. https://doi.org/10.1109/ACCESS.2020.2974008

    Article  Google Scholar 

  82. Al-Fatlawi AH, Jabardi MH, Ling SH (2016) An efficient diagnosis system for parkinson’s disease using deep belief network In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp 1324–1330. IEEE

  83. Gunduz H (2019) Deep learning-based parkinson’s disease classification using vocal feature sets. IEEE Access 7:115540–115551. https://doi.org/10.1109/access.2019.2936564

    Article  Google Scholar 

  84. Wodzinski M, Skalski A, Hemmerling D, Orozco-Arroyave JR, Noth E (2019) Deep learning approach to Parkinson’s disease detection using voice recordings and convolutional neural network dedicated to image classification. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS. https://doi.org/10.1109/EMBC.2019.8856972

    Article  Google Scholar 

  85. Zhao A, Qi L, Li J, Dong J, Yu H (2018) A hybrid spatio-temporal model for detection and severity rating of Parkinson’s disease from gait data. Neurocomputing 315:1–8. https://doi.org/10.1016/j.neucom.2018.03.032

    Article  Google Scholar 

  86. Khojasteh P, Viswanathan R, Aliahmad B, Ragnav S, Zham P, Kumar DK (2018) Parkinson’s disease diagnosis based on multivariate deep features of speech signal. 2018 IEEE Life Sci Conf LSC, pp 187–190. https://doi.org/10.1109/LSC.2018.8572136

  87. Wingate J, Kollia I, Bidaut L, Kollias S (2020) Unified deep learning approach for prediction of Parkinson’s disease. IET Image Process 14:1980–1989. https://doi.org/10.1049/iet-ipr.2019.1526

    Article  Google Scholar 

  88. Pahuja G, Nagabhushan TN, Prasad B (2020) Early detection of parkinson’s disease by using SPECT imaging and biomarkers. J Intell Syst 29:1329–1344. https://doi.org/10.1515/jisys-2018-0261

    Article  Google Scholar 

  89. Gil-Martín M, Montero JM, San-Segundo R (2019) Parkinson’s disease detection from drawing movements using convolutional neural networks. Electron 8:1–10. https://doi.org/10.3390/electronics8080907

    Article  Google Scholar 

  90. Esmaeilzadeh S, Yang Y, Adeli E (2018) End-to-end Parkinson disease diagnosis using brain MR-images by 3D-CNN. arXiv preprint arXiv:1806.05233

  91. Kaur S, Aggarwal H, Rani R (2021) Diagnosis of Parkinson’s disease using deep CNN with transfer learning and data augmentation. Multimed Tools Appl 80:10113–10139. https://doi.org/10.1007/s11042-020-10114-1

    Article  Google Scholar 

  92. Wan S, Liang Y, Zhang Y, Guizani M (2018) Deep multi-layer perceptron classifier for behavior analysis to estimate Parkinson’s disease severity using smartphones. IEEE Access 6:36825–36833. https://doi.org/10.1109/ACCESS.2018.2851382

    Article  Google Scholar 

  93. Grover S, Bhartia S, Akshama YA, Seeja KR (2018) Predicting severity of Parkinson’s disease using deep learning. Procedia Comput Sci 132:1788–1794. https://doi.org/10.1016/j.procs.2018.05.154

    Article  Google Scholar 

  94. Anju P, Varghese A, Roy A, Suresh S, Joy E, Sunder R (2020) recent survey on Parkinson disease diagnose using deep learning mechanism. 2nd Int Conf Innov Mech Ind Appl ICIMIA 2020 - Conf Proc pp 340–343. https://doi.org/10.1109/ICIMIA48430.2020.9074896

  95. Sivaranjini S, Sujatha CM (2020) Deep learning based diagnosis of Parkinson’s disease using convolutional neural network. Multimed Tools Appl 79:15467–15479. https://doi.org/10.1007/s11042-019-7469-8

    Article  Google Scholar 

  96. Mohammed F, He X, Lin Y (2021) An easy-to-use deep-learning model for highly accurate diagnosis of Parkinson’s disease using SPECT images. Comput Med Imaging Graph 87:101810. https://doi.org/10.1016/j.compmedimag.2020.101810

    Article  Google Scholar 

  97. Ali L, Zhu C, Zhou M, Liu Y (2019) Early diagnosis of Parkinson’s disease from multiple voice recordings by simultaneous sample and feature selection. Expert Syst Appl 137:22–28. https://doi.org/10.1016/j.eswa.2019.06.052

    Article  Google Scholar 

  98. Shivangi, Johri A, Tripathi A (2019) Parkinson disease detection using deep neural networks. 2019 12th Int Conf Contemp Comput IC3 pp 1–4. https://doi.org/10.1109/IC3.2019.8844941

  99. Oh SL, Hagiwara Y, Raghavendra U, Yuvaraj R, Arunkumar N, Murugappan M, Acharya UR (2018) A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3689-5

    Article  Google Scholar 

  100. Dai Y, Tang Z, Wang Y, Xu Z (2019) Data driven intelligent diagnostics for Parkinson’s disease. IEEE Access 7:106941–106950. https://doi.org/10.1109/ACCESS.2019.2931744

    Article  Google Scholar 

  101. Alharthi AS and Ozanyan KB (2019) Deep learning for ground reaction force data analysis: Application to wide-area floor sensing. In: 2019 IEEE 28th Int Symp Ind Electron (ISIE), pp 1401–1406. IEEE

  102. Banerjee M, Chakraborty R, Archer D, Vaillancourt D and Vemuri BC (2019) Dmr-cnn: A cnn tailored for dmr scans with applications to pd classification." In: 2019 IEEE 16th Int Symp Biomed Imaging (ISBI 2019), pp 388–391. IEEE

  103. Balaji C, Suresh DS (2019) Implications of EEG and speech signal in the analysis of neurological disorders-a survey. J Biomed Eng Biosci https://doi.org/10.32474/OAJBEB.2019.03.000165

  104. Gautam R, Sharma M (2020) Prevalence and diagnosis of neurological disorders using different deep learning techniques: a meta-analysis. J Med Syst. https://doi.org/10.1007/s10916-019-1519-7

    Article  Google Scholar 

  105. Naranjo L, Pérez CJ, Martín J, Campos-Roca Y (2017) A two-stage variable selection and classification approach for Parkinson’s disease detection by using voice recording replications. Comput Methods Programs Biomed 142:147–156. https://doi.org/10.1016/j.cmpb.2017.02.019

    Article  Google Scholar 

  106. Sadek RM, Mohammed SA, Abunbehan ARK, Ghattas AKHA, Badawi MR, Mortaja MN, Abu-Nasser BS, Abu-Naser SS (2019) Parkinson’s disease prediction using artificial neural network. 3:1–8

  107. Shinde S, Prasad S, Saboo Y, Kaushick R, Saini J, Pal P.K, Ingalhalikar, M (2019) Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI. NeuroImage: Clin 22:101748. https://doi.org/10.1016/j.nicl.2019.101748

    Article  Google Scholar 

Download references

Acknowledgements

Authors wish to express their sincere thanks to the SPARC (SPARC/2018-2019/P31/SL) and Professor Lakshmi Narayana Samavedham, National University of Singapore. The authors also wish to thank the ABI-SHOWATECH private limited and SASTRA Deemed University, Thanjavur, India, for funding the scholar and extending infrastructural support to carry out this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rengarajan Amirtharajan.

Ethics declarations

Conflict of interest

The authors have no relevant conflicts of interest to disclose.

Ethical approval

This article does not contain any studies with human participants or animals performed by any authors.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saravanan, S., Ramkumar, K., Adalarasu, K. et al. A Systematic Review of Artificial Intelligence (AI) Based Approaches for the Diagnosis of Parkinson’s Disease. Arch Computat Methods Eng 29, 3639–3653 (2022). https://doi.org/10.1007/s11831-022-09710-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-022-09710-1

Navigation