Skip to main content
SpringerLink
Log in
Book cover

Machine Learning in Clinical Neuroscience pp 319–331Cite as

Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction

  • Conference paper
  • First Online:

Part of the book series: Acta Neurochirurgica Supplement ((NEUROCHIRURGICA,volume 134))

Abstract

Machine learning (ML) is a rapidly rising research tool in biomedical sciences whose applications include segmentation, classification, disease detection, and outcome prediction. With respect to traditional statistical methods, ML algorithms have the potential to learn and improve their predictive performance when fed with large data sets without the need of being specifically programmed. In recent years, this technology has been increasingly applied for tackling clinical issues in intracranial aneurysm (IA) research. Several studies attempted to provide reliable models for enhanced aneurysm detection. Convolutional neural networks trained with variable degrees of human interaction on data from diverse imaging modalities showed high sensitivity in aneurysm detection tasks, also outperforming expert image analysis. Algorithms were also shown to differentiate ruptured from unruptured IAs, with however limited clinical relevance. For prediction of rupture and stability assessment, ML was preliminarily shown to achieve better performance compared to conventional statistical methods and existing risk scores. ML-based complication and functional outcome prediction in the event of SAH have been more extensively reported, in contrast with periprocedural outcome investigation in unruptured IA patients. ML has the potential to be a game changer in IA patient management. Currently clinical translation of experimental results is limited.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Etminan N, Rinkel GJ. Unruptured intracranial aneurysms: development, rupture and preventive management. Nat Rev Neurol. 2016;12(12):699–713.

    PubMed  Google Scholar 

  2. Macdonald RL. Spontaneous subarachnoid haemorrhage. Lancet. 2017;389:12.

    Google Scholar 

  3. Steiner T, Juvela S, Unterberg A, Jung C, Forsting M, Rinkel G. European stroke organization guidelines for the management of intracranial aneurysms and subarachnoid haemorrhage. Cerebrovasc Dis. 2013;35(2):93–112.

    PubMed  Google Scholar 

  4. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317.

    PubMed  Google Scholar 

  5. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.

    PubMed  CAS  Google Scholar 

  6. Connolly ES, Rabinstein AA, Carhuapoma JR, et al. Guidelines for the management of aneurysmal subarachnoid hemorrhage: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2012;43(6):1711–37.

    PubMed  Google Scholar 

  7. Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging. 2017;30(4):449–59.

    PubMed  PubMed Central  Google Scholar 

  8. Razzak MI, Naz S, Zaib A. Deep learning for medical image processing: overview, challenges and future, vol. 30; 2017. p. 449–59.

    Google Scholar 

  9. Senders JT, Staples PC, Karhade AV, Zaki MM, Gormley WB, Broekman MLD, Smith TR, Arnaout O. Machine learning and neurosurgical outcome prediction: a systematic review. World Neurosurg. 2018;109:476–486.e1.

    PubMed  Google Scholar 

  10. Swinburne NC, Schefflein J, Sakai Y, Oermann EK, Titano JJ, Chen I, Tadayon S, Aggarwal A, Doshi A, Nael K. Machine learning for semiautomated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging. Ann Transl Med. 2019;7(11):232–2.

    Google Scholar 

  11. Arvind V, Kim JS, Oermann EK, Kaji D, Cho SK. Predicting surgical complications in adult patients undergoing anterior cervical discectomy and fusion using machine learning. Neurospine. 2018;15(4):329–37.

    PubMed  PubMed Central  Google Scholar 

  12. Kim JS, Arvind V, Oermann EK, Kaji D, Ranson W, Ukogu C, Hussain AK, Caridi J, Cho SK. Predicting surgical complications in patients undergoing elective adult spinal deformity procedures using machine learning. Spine Deformity. 2018;6(6):762–70.

    PubMed  Google Scholar 

  13. Siccoli A, de Wispelaere MP, Schröder ML, Staartjes VE. Machine learning–based preoperative predictive analytics for lumbar spinal stenosis. Neurosurg Focus. 2019;46(5):E5.

    PubMed  Google Scholar 

  14. Staartjes VE, Serra C, Muscas G, Maldaner N, Akeret K, van Niftrik CHB, Fierstra J, Holzmann D, Regli L. Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study. Neurosurg Focus. 2018;45(5):E12.

    PubMed  Google Scholar 

  15. Staartjes VE, Zattra CM, Akeret K, Maldaner N, Muscas G, van Niftrik CH, Fierstra J, Regli L, Serra C. Neural network–based identification of patients at high risk for intraoperative cerebrospinal fluid leaks in endoscopic pituitary surgery. J Neurosurg. 2019:1–7.

    Google Scholar 

  16. Van Niftrik CHB, van der Wouden F, Staartjes VE, et al. Machine learning algorithm identifies patients at high risk for early complications after intracranial tumor surgery: registry-based cohort study. Neurosurgery. 2019;85(4):E756–64.

    PubMed  Google Scholar 

  17. Shi Z, Hu B, Schoepf UJ, Savage RH, Dargis DM, Pan CW, Li XL, Ni QQ, Lu GM, Zhang LJ. Artificial intelligence in the management of intracranial aneurysms: current status and future perspectives. AJNR Am J Neuroradiol. 2020;41(3):373–9.

    PubMed  PubMed Central  CAS  Google Scholar 

  18. Duan H, Huang Y, Liu L, Dai H, Chen L, Zhou L. Automatic detection on intracranial aneurysm from digital subtraction angiography with cascade convolutional neural networks. Biomed Eng Online. 2019;18(1):110.

    PubMed  PubMed Central  Google Scholar 

  19. Hainc N, Mannil M, Anagnostakou V, Alkadhi H, Blüthgen C, Wacht L, Bink A, Husain S, Kulcsár Z, Winklhofer S. Deep learning based detection of intracranial aneurysms on digital subtraction angiography: a feasibility study. Neuroradiol J. 2020;33(4):311–7.

    PubMed  PubMed Central  Google Scholar 

  20. Heo J, Park SJ, Kang S-H, Oh CW, Bang JS, Kim T. Prediction of intracranial aneurysm risk using machine learning. Sci Rep. 2020;10(1):6921.

    PubMed  PubMed Central  CAS  Google Scholar 

  21. Joo B, Ahn SS, Yoon PH, Bae S, Sohn B, Lee YE, Bae JH, Park MS, Choi HS, Lee S-K. A deep learning algorithm may automate intracranial aneurysm detection on MR angiography with high diagnostic performance. Eur Radiol. 2020;30(11):5785–93.

    PubMed  Google Scholar 

  22. Nakao T, Hanaoka S, Nomura Y, Sato I, Nemoto M, Miki S, Maeda E, Yoshikawa T, Hayashi N, Abe O. Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography. J Magn Reson Imaging. 2018;47(4):948–53.

    PubMed  Google Scholar 

  23. Park A, Chute C, Rajpurkar P, et al. Deep learning–assisted diagnosis of cerebral aneurysms using the HeadXNet model. JAMA Netw Open. 2019;2(6):e195600.

    PubMed  PubMed Central  Google Scholar 

  24. Podgorsak AR, Rava RA, Shiraz Bhurwani MM, Chandra AR, Davies JM, Siddiqui AH, Ionita CN. Automatic radiomic feature extraction using deep learning for angiographic parametric imaging of intracranial aneurysms. J NeuroIntervent Surg. 2020;12(4):417–21.

    Google Scholar 

  25. Poppenberg KE, Tutino VM, Li L, et al. Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm. J Transl Med. 2020;18(1):392.

    PubMed  PubMed Central  CAS  Google Scholar 

  26. Sichtermann T, Faron A, Sijben R, Teichert N, Freiherr J, Wiesmann M. Deep learning–based detection of intracranial aneurysms in 3D TOF-MRA. AJNR Am J Neuroradiol. 2019;40(1):25–32.

    PubMed  PubMed Central  CAS  Google Scholar 

  27. Ueda D, Yamamoto A, Nishimori M, et al. Deep learning for MR angiography: automated detection of cerebral aneurysms. Radiology. 2019;290(1):187–94.

    PubMed  Google Scholar 

  28. Liu J, Chen Y, Lan L, et al. Prediction of rupture risk in anterior communicating artery aneurysms with a feed-forward artificial neural network. Eur Radiol. 2018;28(8):3268–75.

    PubMed  Google Scholar 

  29. Liu Q, Jiang P, Jiang Y, Ge H, Li S, Jin H, Li Y. Prediction of aneurysm stability using a machine learning model based on PyRadiomics-derived morphological features. Stroke. 2019;50(9):2314–21.

    PubMed  Google Scholar 

  30. Lv N, Karmonik C, Shi Z, Chen S, Wang X, Liu J, Huang Q. A pilot study using a machine-learning approach of morphological and hemodynamic parameters for predicting aneurysms enhancement. Int J CARS. 2020;15(8):1313–21.

    Google Scholar 

  31. Ou C, Chong W, Duan C-Z, Zhang X, Morgan M, Qian Y. A preliminary investigation of radiomics differences between ruptured and unruptured intracranial aneurysms. Eur Radiol. 2020;31(5):2716–25. https://doi.org/10.1007/s00330-020-07325-3.

    Article  PubMed  Google Scholar 

  32. Zhu W, Li W, Tian Z, Zhang Y, Wang K, Zhang Y, Liu J, Yang X. Stability assessment of intracranial aneurysms using machine learning based on clinical and morphological features. Transl Stroke Res. 2020;11(6):1287–95. https://doi.org/10.1007/s12975-020-00811-2.

    Article  PubMed  Google Scholar 

  33. Greving JP, Wermer MJH, Brown RD, et al. Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms: a pooled analysis of six prospective cohort studies. Lancet Neurol. 2014;13(1):59–66.

    PubMed  Google Scholar 

  34. Tominari S, Morita A, Ishibashi T, et al. Prediction model for 3-year rupture risk of unruptured cerebral aneurysms in Japanese patients: cerebral aneurysm rupture risk. Ann Neurol. 2015;77(6):1050–9.

    PubMed  Google Scholar 

  35. Capoglu S, Savarraj JP, Sheth SA, Choi HA, Giancardo L. Representation Learning of 3D Brain Angiograms, an Application for Cerebral Vasospasm Prediction. In: 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC). Berlin: IEEE; 2019. p. 3394–8.

    Google Scholar 

  36. Muscas G, Matteuzzi T, Becattini E, et al. Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage. Acta Neurochir. 2020;162(12):3093–105.

    PubMed  Google Scholar 

  37. Paliwal N, Jaiswal P, Tutino VM, Shallwani H, Davies JM, Siddiqui AH, Rai R, Meng H. Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning. Neurosurg Focus. 2018;45(5):E7.

    PubMed  PubMed Central  Google Scholar 

  38. pSEED Group, Tanioka S, Ishida F, Nakano F, Kawakita F, Kanamaru H, Nakatsuka Y, Nishikawa H, Suzuki H. Machine learning analysis of Matricellular proteins and clinical variables for early prediction of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage. Mol Neurobiol. 2019;56(10):7128–35.

    Google Scholar 

  39. Ramos LA, van der Steen WE, Sales Barros R, et al. Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage. J NeuroIntervent Surg. 2019;11(5):497–502.

    Google Scholar 

  40. Rubbert C, Patil KR, Beseoglu K, et al. Prediction of outcome after aneurysmal subarachnoid haemorrhage using data from patient admission. Eur Radiol. 2018;28(12):4949–58.

    PubMed  Google Scholar 

  41. Staartjes VE, Sebök M, Blum PG, Serra C, Germans MR, Krayenbühl N, Regli L, Esposito G. Development of machine learning-based preoperative predictive analytics for unruptured intracranial aneurysm surgery: a pilot study. Acta Neurochir. 2020;162(11):2759–65. https://doi.org/10.1007/s00701-020-04355-0.

    Article  PubMed  Google Scholar 

  42. de Toledo P, Rios PM, Ledezma A, Sanchis A, Alen JF, Lagares A. Predicting the outcome of patients with subarachnoid hemorrhage using machine learning techniques. IEEE Trans Inform Technol Biomed. 2009;13(5):794–801.

    Google Scholar 

  43. Zafar SF, Postma EN, Biswal S, et al. Electronic health data predict outcomes after aneurysmal subarachnoid hemorrhage. Neurocrit Care. 2018;28(2):184–93.

    PubMed  PubMed Central  Google Scholar 

  44. Hostettler IC, Muroi C, Richter JK, Schmid J, Neidert MC, Seule M, Boss O, Pangalu A, Germans MR, Keller E. Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis. J Neurosurg. 2018:1–12.

    Google Scholar 

  45. Rinkel GJ. Intracranial aneurysm screening: indications and advice for practice. Lancet Neurol. 2005;4(2):122–8.

    PubMed  Google Scholar 

  46. Brown RD, Broderick JP. Unruptured intracranial aneurysms: epidemiology, natural history, management options, and familial screening. Lancet Neurol. 2014;13(4):393–404.

    PubMed  Google Scholar 

  47. Rose S. Machine learning for prediction in electronic health data. JAMA Netw Open. 2018;1(4):e181404.

    PubMed  Google Scholar 

  48. Koumakis L. Deep learning models in genomics; are we there yet? Comput Struct Biotechnol J. 2020;18:1466–73.

    PubMed  PubMed Central  CAS  Google Scholar 

  49. Su C, Tong J, Wang F. Mining genetic and transcriptomic data using machine learning approaches in Parkinson’s disease. NPJ Parkinsons Dis. 2020;6(1):24.

    PubMed  PubMed Central  Google Scholar 

  50. Tutino VM, Poppenberg KE, Jiang K, et al. Circulating neutrophil transcriptome may reveal intracranial aneurysm signature. PLoS One. 2018;13(1):e0191407.

    PubMed  PubMed Central  Google Scholar 

  51. Tutino VM, Poppenberg KE, Li L, et al. Biomarkers from circulating neutrophil transcriptomes have potential to detect unruptured intracranial aneurysms. J Transl Med. 2018;16(1):373.

    PubMed  PubMed Central  CAS  Google Scholar 

  52. Renowden S, Nelson R. Management of incidental unruptured intracranial aneurysms. Pract Neurol. 2020;20(5):347–55.

    PubMed  Google Scholar 

  53. Lubicz B, Levivier M, Francois O, Thoma P, Sadeghi N, Collignon L, Baleriaux D. Sixty-four-row multisection CT angiography for detection and evaluation of ruptured intracranial aneurysms: interobserver and Intertechnique reproducibility. Am J Neuroradiol. 2007;28(10):1949–55.

    PubMed  PubMed Central  CAS  Google Scholar 

  54. Dhillon A, Verma GK. Convolutional neural network: a review of models, methodologies and applications to object detection. Prog Artif Intell. 2020;9(2):85–112.

    Google Scholar 

  55. Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK. Medical image analysis using convolutional neural networks: a review. J Med Syst. 2018;42(11):226.

    PubMed  Google Scholar 

  56. Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018;9(4):611–29.

    PubMed  PubMed Central  Google Scholar 

  57. Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, Lu J. Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph. 2019;75:34–46.

    PubMed  Google Scholar 

  58. Björkman J, Frösen J, Tähtinen O, et al. Irregular shape identifies ruptured intracranial aneurysm in subarachnoid hemorrhage patients with multiple aneurysms. Stroke. 2017;48(7):1986–9.

    PubMed  Google Scholar 

  59. Rajabzadeh-Oghaz H, Wang J, Varble N, et al. Novel models for identification of the ruptured aneurysm in patients with subarachnoid hemorrhage with multiple aneurysms. AJNR Am J Neuroradiol. 2019;40:1939–46.

    PubMed  PubMed Central  CAS  Google Scholar 

  60. Detmer FJ, Chung BJ, Mut F, Slawski M, Hamzei-Sichani F, Putman C, Jiménez C, Cebral JR. Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics. Int J CARS. 2018;13(11):1767–79.

    Google Scholar 

  61. Detmer FJ, Fajardo-Jiménez D, Mut F, Juchler N, Hirsch S, Pereira VM, Bijlenga P, Cebral JR. External validation of cerebral aneurysm rupture probability model with data from two patient cohorts. Acta Neurochir. 2018;160(12):2425–34.

    PubMed  Google Scholar 

  62. Silva MA, Patel J, Kavouridis V, et al. Machine learning models can detect aneurysm rupture and identify clinical features associated with rupture. World Neurosurg. 2019;131:e46–51.

    PubMed  Google Scholar 

  63. Kim HC, Rhim JK, Ahn JH, et al. Machine learning application for rupture risk assessment in small-sized intracranial aneurysm. J Clin Med. 2019;8(5):683.

    PubMed  PubMed Central  Google Scholar 

  64. Backes D, Rinkel GJE, Greving JP, et al. ELAPSS score for prediction of risk of growth of unruptured intracranial aneurysms. Neurology. 2017;88(17):1600–6.

    PubMed  Google Scholar 

  65. Juvela S. Scoring of growth of unruptured intracranial aneurysms. J Clin Med. 2020;9(10):3339.

    PubMed  PubMed Central  Google Scholar 

  66. Etminan N, Brown RD, Beseoglu K, et al. The unruptured intracranial aneurysm treatment score: a multidisciplinary consensus. Neurology. 2015;85(10):881–9.

    PubMed  PubMed Central  Google Scholar 

  67. Juvela S. Treatment scoring of unruptured intracranial aneurysms. Stroke. 2019;50(9):2344–50.

    PubMed  Google Scholar 

  68. Stumpo V, Sturiale CL. Inquiring the real-world clinical performance of the unruptured intracranial aneurysm treatment score (UIATS). Neurosurg Rev. 2020;44:1–3. https://doi.org/10.1007/s10143-020-01354-8.

    Article  Google Scholar 

  69. Sturiale CL, Stumpo V, Ricciardi L, Trevisi G, Valente I, D’Arrigo S, Latour K, Barbone P, Albanese A. Retrospective application of risk scores to ruptured intracranial aneurysms: would they have predicted the risk of bleeding? Neurosurg Rev. 2020;44:1655–63. https://doi.org/10.1007/s10143-020-01352-w.

    Article  PubMed  Google Scholar 

  70. Liu Q, Jiang P, Wu J, Li M, Gao B, Zhang Y, Ning B, Cao Y, Wang S. Intracranial aneurysm rupture score may correlate to the risk of rebleeding before treatment of ruptured intracranial aneurysms. Neurol Sci. 2019;40(8):1683–93.

    PubMed  Google Scholar 

  71. Xiang J, Yu J, Choi H, Dolan Fox JM, Snyder KV, Levy EI, Siddiqui AH, Meng H. Rupture resemblance score (RRS): toward risk stratification of unruptured intracranial aneurysms using hemodynamic–morphological discriminants. J NeuroIntervent Surg. 2015;7(7):490–5.

    Google Scholar 

  72. Molyneux AJ, Kerr RSC, Yu L-M, Clarke M, Sneade M, Yarnold JA, Sandercock P. International subarachnoid aneurysm trial (ISAT) of neurosurgical clipping versus endovascular coiling in 2143 patients with ruptured intracranial aneurysms: a randomised comparison of effects on survival, dependency, seizures, rebleeding, subgroups, and aneurysm occlusion. Lancet. 2005;366:9.

    Google Scholar 

  73. Rutledge C, Jonzzon S, Winkler EA, Raper D, Lawton MT, Abla AA. Small aneurysms with low PHASES scores account for most subarachnoid hemorrhage cases. World Neurosurg. 2020;139:e580–4.

    PubMed  Google Scholar 

  74. Chen JH, Asch SM. Machine learning and prediction in medicine—beyond the peak of inflated expectations. N Engl J Med. 2017;376(26):2507–9.

    PubMed  PubMed Central  Google Scholar 

  75. Adams H, Ban VS, Leinonen V, et al. Risk of shunting after aneurysmal subarachnoid hemorrhage: a collaborative study and initiation of a consortium. Stroke. 2016;47(10):2488–96.

    PubMed  Google Scholar 

  76. Pegoli M, Mandrekar J, Rabinstein AA, Lanzino G. Predictors of excellent functional outcome in aneurysmal subarachnoid hemorrhage. J Neurosurg. 2015;122:414–8.

    PubMed  Google Scholar 

  77. van Donkelaar CE, Bakker NA, Birks J, Veeger NJGM, Metzemaekers JDM, Molyneux AJ, Groen RJM, van Dijk JMC. Prediction of outcome after aneurysmal subarachnoid hemorrhage: development and validation of the SAFIRE grading scale. Stroke. 2019;50(4):837–44.

    PubMed  Google Scholar 

  78. Flemming KD, Lanzino G. Management of unruptured intracranial aneurysms and cerebrovascular malformations. Continuum. 2017;23(1):181–210.

    PubMed  Google Scholar 

  79. Staartjes VE, Broggi M, Zattra CM, et al. Development and external validation of a clinical prediction model for functional impairment after intracranial tumor surgery. J Neurosurg. 2020:1–8.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    Vittorio Stumpo, Victor E. Staartjes, Giuseppe Esposito, Carlo Serra & Luca Regli

  2. Institute of Neurosurgery, Università Cattolica del Sacro Cuore, Rome, Italy

    Vittorio Stumpo & Alessandro Olivi

  3. Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy

    Alessandro Olivi & Carmelo Lucio Sturiale

Authors
  1. Vittorio Stumpo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Victor E. Staartjes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giuseppe Esposito
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carlo Serra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Luca Regli
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alessandro Olivi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Carmelo Lucio Sturiale
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Victor E. Staartjes .

Editor information

Editors and Affiliations

  1. Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    Victor E. Staartjes

  2. Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    Luca Regli

  3. Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    Carlo Serra

1 Electronic Supplementary Material

Table S1

Aneurysm rupture assessment studies (DOCX 21 kb)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Stumpo, V. et al. (2022). Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction. In: Staartjes, V.E., Regli, L., Serra, C. (eds) Machine Learning in Clinical Neuroscience. Acta Neurochirurgica Supplement, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-030-85292-4_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85292-4_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85291-7

  • Online ISBN: 978-3-030-85292-4

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation