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An introduction and overview of machine learning in neurosurgical care

  • Review Article - Neurosurgical Techniques
  • Published:
Acta Neurochirurgica Aims and scope Submit manuscript

Abstract

Background

Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the considerable potential of ML has yet to find its way into mainstream medical research and day-to-day clinical care. The complex diagnostic and therapeutic modalities used in neurosurgery provide a vast amount of data that is ideally suited for ML models. This systematic review explores ML’s potential to assist and improve neurosurgical care.

Method

A systematic literature search was performed in the PubMed and Embase databases to identify all potentially relevant studies up to January 1, 2017. All studies were included that evaluated ML models assisting neurosurgical treatment.

Results

Of the 6,402 citations identified, 221 studies were selected after subsequent title/abstract and full-text screening. In these studies, ML was used to assist surgical treatment of patients with epilepsy, brain tumors, spinal lesions, neurovascular pathology, Parkinson’s disease, traumatic brain injury, and hydrocephalus. Across multiple paradigms, ML was found to be a valuable tool for presurgical planning, intraoperative guidance, neurophysiological monitoring, and neurosurgical outcome prediction.

Conclusions

ML has started to find applications aimed at improving neurosurgical care by increasing the efficiency and precision of perioperative decision-making. A thorough validation of specific ML models is essential before implementation in clinical neurosurgical care. To bridge the gap between research and clinical care, practical and ethical issues should be considered parallel to the development of these techniques.

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References

  1. Abbasi S, Tajeripour F (2017) Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing 219:526–535

    Article  Google Scholar 

  2. Akbari H, Macyszyn L, Da X, Bilello M, Wolf RL, Martinez-Lage M, Biros G, Alonso-Basanta M, O’Rourke DM, Davatzikos C (2016) Imaging surrogates of infiltration obtained via multiparametric imaging pattern analysis predict subsequent location of recurrence of glioblastoma. Neurosurgery 78:572–580. https://doi.org/10.1227/neu.0000000000001202

    Article  PubMed  PubMed Central  Google Scholar 

  3. Akbari H, Macyszyn L, Da X, Wolf RL, Bilello M, Verma R, O’Rourke DM, Davatzikos C (2014) Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity. Radiology 273:502–510. https://doi.org/10.1148/radiol.14132458

    Article  PubMed  PubMed Central  Google Scholar 

  4. Antoni ST, Rinast J, Ma X, Schupp S, Schlaefer A (2016) Online model checking for monitoring surrogate-based respiratory motion tracking in radiation therapy. Int J Comput Assist Radiol Surg 11(11):2085-2096. https://doi.org/10.1007/s11548-016-1423-2

  5. Arle JE, Perrine K, Devinsky O, Doyle WK (1999) Neural network analysis of preoperative variables and outcome in epilepsy surgery. J Neurosurg 90:998–1004. https://doi.org/10.3171/jns.1999.90.6.0998

    Article  CAS  PubMed  Google Scholar 

  6. Asadi H, Kok HK, Looby S, Brennan P, O’Hare A, Thornton J (2016) Outcomes and complications after endovascular treatment of brain Arteriovenous malformations: a prognostication attempt using artificial intelligence. World Neurosurg 96:562–569.e561

    Article  PubMed  Google Scholar 

  7. Azami ME, Hammers A, Jung J, Costes N, Bouet R, Lartizien C (2016) Detection of lesions underlying intractable epilepsy on T1-weighted MRI as an outlier detection problem. PLoS One 11:e0161498

    Article  PubMed  PubMed Central  Google Scholar 

  8. Azimi P, Benzel EC, Shahzadi S, Azhari S, Mohammadi HR (2016) The prediction of successful surgery outcome in lumbar disc herniation based on artificial neural networks. J Neurosurg Sci 60:173–177

    PubMed  Google Scholar 

  9. Azimi P, Benzel EC, Shahzadi S, Azhari S, Mohammadi HR (2014) Use of artificial neural networks to predict surgical satisfaction in patients with lumbar spinal canal stenosis: clinical article. J Neurosurg Spine 20:300–305. https://doi.org/10.3171/2013.12.spine13674

    Article  PubMed  Google Scholar 

  10. Azimi P, Mohammadi HR (2014) Predicting endoscopic third ventriculostomy success in childhood hydrocephalus: an artificial neural network analysis. J Neurosurg Pediatr 13:426–432. https://doi.org/10.3171/2013.12.peds13423

    Article  PubMed  Google Scholar 

  11. Bibault JE, Giraud P, Burgun A (2016) Big data and machine learning in radiation oncology: state of the art and future prospects. Cancer Lett 382:110–117. https://doi.org/10.1016/j.canlet.2016.05.033

    Article  CAS  PubMed  Google Scholar 

  12. Campillo-Gimenez B, Garcelon N, Jarno P, Chapplain JM, Cuggia M (2013) Full-text automated detection of surgical site infections secondary to neurosurgery in Rennes, France. Stud Health Technol Inform 192:572–575

    PubMed  Google Scholar 

  13. Canchi T, Kumar SD, Ng EY, Narayanan S (2015) A review of computational methods to predict the risk of rupture of abdominal aortic aneurysms. Biomed Res Int 2015:861627. https://doi.org/10.1155/2015/861627

    Article  PubMed  PubMed Central  Google Scholar 

  14. Celtikci E (2017) A systematic review on machine learning in neurosurgery: the future of decision making in patient care. Turk Neurosurg. https://doi.org/10.5137/1019-5149.JTN.20059-17.1

  15. Clarke LP, Velthuizen RP, Clark M, Gaviria J, Hall L, Goldgof D, Murtagh R, Phuphanich S, Brem S (1998) MRI measurement of brain tumor response: comparison of visual metric and automatic segmentation. Magn Reson Imaging 16:271–279

    Article  CAS  PubMed  Google Scholar 

  16. De Momi E, Ferrigno G (2010) Robotic and artificial intelligence for keyhole neurosurgery: the ROBOCAST project, a multi-modal autonomous path planner. Proc Inst Mech Eng H J Eng Med 224:715–727

    Article  Google Scholar 

  17. Deo RC (2015) Machine learning in medicine. Circulation 132:1920–1930. https://doi.org/10.1161/CIRCULATIONAHA.115.001593

    Article  PubMed  Google Scholar 

  18. Di Ieva A, Boukadoum M, Lahmiri S, Cusimano MD (2015) Computational analyses of arteriovenous malformations in neuroimaging. J Neuroimaging 25:354–360. https://doi.org/10.1111/jon.12200

    Article  PubMed  Google Scholar 

  19. Dolz J, Betrouni N, Quidet M, Kharroubi D, Leroy HA, Reyns N, Massoptier L, Vermandel M (2016) Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: a clinical study. Comput Med Imaging Graph 52:8–18. https://doi.org/10.1016/j.compmedimag.2016.03.003

    Article  PubMed  Google Scholar 

  20. Dumont TM, Rughani AI, Tranmer BI (2011) Prediction of symptomatic cerebral vasospasm after aneurysmal subarachnoid hemorrhage with an artificial neural network: feasibility and comparison with logistic regression models. World Neurosurg 75:57–63; discussion 25-58. https://doi.org/10.1016/j.wneu.2010.07.007

    Article  PubMed  Google Scholar 

  21. Eberlin LS, Norton I, Dill AL, Golby AJ, Ligon KL, Santagata S, Graham Cooks R, Agar NYR (2012) Classifying human brain tumors by lipid imaging with mass spectrometry. Cancer Res 72:645–654

    Article  CAS  PubMed  Google Scholar 

  22. Emblem KE, Nedregaard B, Hald JK, Nome T, Due-Tonnessen P, Bjornerud A (2009) Automatic glioma characterization from dynamic susceptibility contrast imaging: brain tumor segmentation using knowledge-based fuzzy clustering. J Magn Reson Imaging 30:1–10. https://doi.org/10.1002/jmri.21815

    Article  PubMed  Google Scholar 

  23. Emblem KE, Pinho MC, Zollner FG, Due-Tonnessen P, Hald JK, Schad LR, Meling TR, Rapalino O, Bjornerud A (2015) A generic support vector machine model for preoperative glioma survival associations. Radiology 275:228–234. https://doi.org/10.1148/radiol.14140770

    Article  PubMed  Google Scholar 

  24. Fan B, Li HX, Hu Y (2016) An intelligent decision system for intraoperative somatosensory evoked potential monitoring. IEEE Trans Neural Syst Rehabil Eng 24:300–307. https://doi.org/10.1109/tnsre.2015.2477557

    Article  PubMed  Google Scholar 

  25. Focke NK, Yogarajah M, Symms MR, Gruber O, Paulus W, Duncan JS (2012) Automated MR image classification in temporal lobe epilepsy. NeuroImage 59:356–362. https://doi.org/10.1016/j.neuroimage.2011.07.068

    Article  PubMed  Google Scholar 

  26. Foroni R, Giri MG, Gerosa MA, Nicolato A, Piovan E, Zampieri PG, Pasqualin A, Bortolazzi E, Pasoli A, Marchini G et al (1995) A euristic approach to the volume reconstruction of arteriovenous malformations from biplane angiography. Stereotact Funct Neurosurg 64:134–146

    Article  PubMed  Google Scholar 

  27. Fusco R, Sansone M, Filice S, Carone G, Amato DM, Sansone C, Petrillo A (2016) Pattern recognition approaches for breast cancer DCE-MRI classification: a systematic review. J Med Biol Eng 36:449–459. https://doi.org/10.1007/s40846-016-0163-7

    Article  PubMed  PubMed Central  Google Scholar 

  28. Gazit T, Andelman F, Glikmann-Johnston Y, Gonen T, Solski A, Shapira-Lichter I, Ovadia M, Kipervasser S, Neufeld MY, Fried I et al (2016) Probabilistic machine learning for the evaluation of presurgical language dominance. J Neurosurg 125:1–13. https://doi.org/10.3171/2015.7.jns142568

    Article  Google Scholar 

  29. Ghahramani Z (2015) Probabilistic machine learning and artificial intelligence. Nature 521:452–459. https://doi.org/10.1038/nature14541

    Article  CAS  PubMed  Google Scholar 

  30. Grigsby J, Kramer RE, Schneiders JL, Gates JR, Brewster Smith W (1998) Predicting outcome of anterior temporal lobectomy using simulated neural networks. Epilepsia 39:61–66

    Article  CAS  PubMed  Google Scholar 

  31. Grossman RL, Heath AP, Ferretti V, Varmus HE, Lowy DR, Kibbe WA, Staudt LM (2016) Toward a shared vision for cancer genomic data. N Engl J Med 375:1109–1112. https://doi.org/10.1056/NEJMp1607591

    Article  PubMed  Google Scholar 

  32. Hamzei-Sichani F, Sperling M, Fuertinger S, Sharan A, Simonyan K (2016) Cortical networks high frequency EEG activity patterns in patients undergoing epilepsy surgery. J Neurosurg 124:A1184

    Google Scholar 

  33. Hastie T, Tibshirani R, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, New York

    Book  Google Scholar 

  34. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H (2016) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31. https://doi.org/10.1016/j.media.2016.05.004

    Article  PubMed  Google Scholar 

  35. Johnson AE, Ghassemi MM, Nemati S, Niehaus KE, Clifton DA, Clifford GD (2016) Machine learning and decision support in critical care. Proc IEEE Inst Electr Electron Eng 104:444–466. https://doi.org/10.1109/JPROC.2015.2501978

    Article  PubMed  PubMed Central  Google Scholar 

  36. Jones TL, Byrnes TJ, Yang G, Howe FA, Bell BA, Barrick TR (2015) Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique. Neuro-Oncology 17:466–476. https://doi.org/10.1093/neuonc/nou159

    CAS  PubMed  Google Scholar 

  37. Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349:255–260. https://doi.org/10.1126/science.aaa8415

    Article  CAS  PubMed  Google Scholar 

  38. Juan-Albarracín J, Fuster-Garcia E, Manjón JV, Robles M, Aparici F, Martí-Bonmatí L, García-Gómez JM (2015) Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification. PLoS One 10:e0125143

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kalkanis SN, Kast RE, Rosenblum ML, Mikkelsen T, Yurgelevic SM, Nelson KM, Raghunathan A, Poisson LM, Auner GW (2014) Raman spectroscopy to distinguish grey matter, necrosis, and glioblastoma multiforme in frozen tissue sections. J Neurooncol 116:477–485

    Article  CAS  PubMed  Google Scholar 

  40. Kanevsky J, Corban J, Gaster R, Kanevsky A, Lin S, Gilardino M (2016) Big data and machine learning in plastic surgery: a new frontier in surgical innovation. Plast Reconstr Surg 137:890e–897e. https://doi.org/10.1097/PRS.0000000000002088

    Article  CAS  PubMed  Google Scholar 

  41. Kassahun Y, Yu B, Tibebu AT, Stoyanov D, Giannarou S, Metzen JH, Vander Poorten E (2016) Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions. Int J Comput Assist Radiol Surg 11:553–568. https://doi.org/10.1007/s11548-015-1305-z

    Article  PubMed  Google Scholar 

  42. Keihaninejad S, Heckemann RA, Gousias IS, Hajnal JV, Duncan JS, Aljabar P, Rueckert D, Hammers A (2012) Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation. PLoS One 7:e33096. https://doi.org/10.1371/journal.pone.0033096

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Kenngott HG, Wagner M, Nickel F, Wekerle AL, Preukschas A, Apitz M, Schulte T, Rempel R, Mietkowski P, Wagner F et al (2015) Computer-assisted abdominal surgery: new technologies. Langenbecks Arch Surg 400:273–281. https://doi.org/10.1007/s00423-015-1289-8

    Article  CAS  PubMed  Google Scholar 

  44. Kohli M, Prevedello LM, Filice RW, Geis JR (2017) Implementing machine learning in radiology practice and research. AJR Am J Roentgenol 208:754-760. https://doi.org/10.2214/AJR.16.17224

    Article  PubMed  Google Scholar 

  45. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI (2015) Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 13:8–17. https://doi.org/10.1016/j.csbj.2014.11.005

    Article  CAS  PubMed  Google Scholar 

  46. Lin E, Lane HY (2017) Machine learning and systems genomics approaches for multi-omics data. Biomark Res 5:2. https://doi.org/10.1186/s40364-017-0082-y

    Article  PubMed  PubMed Central  Google Scholar 

  47. Madani Tonekaboni SA, Soltan Ghoraie L, Manem VS, Haibe-Kains B (2016) Predictive approaches for drug combination discovery in cancer. Brief Bioinform. https://doi.org/10.1093/bib/bbw104

  48. Mariak Z, Swiercz M, Krejza J, Lewko J, Lyson T (2000) Intracranial pressure processing with artificial neural networks: classification of signal properties. Acta Neurochir 142:407–411 discussion 411-402

    Article  CAS  PubMed  Google Scholar 

  49. Mitchell TJ, Hacker CD, Breshears JD, Szrama NP, Sharma M, Bundy DT, Pahwa M, Corbetta M, Snyder AZ, Shimony JS et al (2013) A novel data-driven approach to preoperative mapping of functional cortex using resting-state functional magnetic resonance imaging. Neurosurgery 73:969–982; discussion 982-963. https://doi.org/10.1227/neu.0000000000000141

    Article  PubMed  PubMed Central  Google Scholar 

  50. Mitchell TM (1997) Machine learning. McGraw-Hill Science, New York

    Google Scholar 

  51. Moghim N, Corne DW (2014) Predicting epileptic seizures in advance. PLoS One 9:e99334. https://doi.org/10.1371/journal.pone.0099334

    Article  PubMed  PubMed Central  Google Scholar 

  52. Nowinski WL, Belov D, Benabid AL (2003) An algorithm for rapid calculation of a probabilistic functional atlas of subcortical structures from electrophysiological data collected during functional neurosurgery procedures. NeuroImage 18:143–155

    Article  PubMed  Google Scholar 

  53. Nucci CG, De Bonis P, Mangiola A, Santini P, Sciandrone M, Risi A, Anile C (2016) Intracranial pressure wave morphological classification: automated analysis and clinical validation. Acta Neurochir 158:581–588; discussion 588. https://doi.org/10.1007/s00701-015-2672-5

    Article  PubMed  Google Scholar 

  54. Obermeyer Z, Emanuel EJ (2016) Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med 375:1216–1219. https://doi.org/10.1056/NEJMp1606181

    Article  PubMed  PubMed Central  Google Scholar 

  55. Orringer D, Ji M, Lewis S, Camelo-Piragua S, Johnson T, Sagher O, Wang A, Maher C, Heth J, Xie X (2015) Visualizing brain tumor infiltration with stimulated Raman scattering microscopy. J Neurosurg 123:A523

    Google Scholar 

  56. Saha M, Mukherjee R, Chakraborty C (2016) Computer-aided diagnosis of breast cancer using cytological images: a systematic review. Tissue Cell 48:461–474. https://doi.org/10.1016/j.tice.2016.07.006

    Article  PubMed  Google Scholar 

  57. Schmidt B, Bocklisch SF, Pässler M, Czosnyka M, Schwarze JJ, Klingelhöfer J (2005) Fuzzy pattern classification of hemodynamic data can be used to determine noninvasive intracranial pressure. Acta Neurochir Suppl 95:345–349

    Article  CAS  PubMed  Google Scholar 

  58. Senders JT, Arnaout O, Karhade AV, Dasenbrock HH, Gormley WB, Broekman ML, Smith TR (2017) Natural and artificial intelligence in neurosurgery: a systematic review. Neurosurgery. https://doi.org/10.1093/neuros/nyx384

  59. Senders JT, Staples PC, Karhade AV, Zaki MM, Gormley WB, Broekman MLD, Smith TR, Arnaout O (2017) Machine learning and neurosurgical outcome prediction: a systematic review. World Neurosurg. https://doi.org/10.1016/j.wneu.2017.09.149

  60. Shamim MS, Enam SA, Qidwai U (2009) Fuzzy logic in neurosurgery: predicting poor outcomes after lumbar disk surgery in 501 consecutive patients. Surg Neurol 72:565–572; discussion 572. https://doi.org/10.1016/j.surneu.2009.07.012

    Article  PubMed  Google Scholar 

  61. Shamir R, Duchin Y, Kim JJK, Marmor O, Bergman H, Vitek JL, Sapiro G, Bick AS, Eliyahu R, Eitan R et al (2016) MER validation of a new targeting approach for STN-DBS surgery based on machine-learning and 7T-MRI database (10661). Neuromodulation 19:e67

    Google Scholar 

  62. Shi HY, Hwang SL, Lee KT, Lin CL (2013) In-hospital mortality after traumatic brain injury surgery: a nationwide population-based comparison of mortality predictors used in artificial neural network and logistic regression models. J Neurosurg 118:746–752. https://doi.org/10.3171/2013.1.jns121130

    Article  PubMed  Google Scholar 

  63. Shih JJ, Krusienski DJ (2009) Electrocorticography in a brain-computer interface (BCI) paradigm. Epilepsia 50:327

    Google Scholar 

  64. Skrobala A (2012) Beam orientation in stereotactic radiosurgery using artificial neural network. Radiother Oncol 103:S220

    Article  Google Scholar 

  65. Swiercz M, Mariak Z, Krejza J, Lewko J, Szydlik P (2000) Intracranial pressure processing with artificial neural networks: prediction of ICP trends. Acta Neurochir 142:401–406

    Article  CAS  PubMed  Google Scholar 

  66. Taghva A (2010) An automated navigation system for deep brain stimulator placement using hidden Markov models. Neurosurgery 66:108–117; discussion 117. https://doi.org/10.1227/01.NEU.0000365369.48392.E8

    Article  PubMed  Google Scholar 

  67. Taghva A (2011) Hidden semi-Markov models in the computerized decoding of microelectrode recording data for deep brain stimulator placement. World Neurosurg 75:758–763.E754

    Article  PubMed  Google Scholar 

  68. Wang J, You X, Wu W, Guillen MR, Cabrerizo M, Sullivan J, Donner E, Bjornson B, Gaillard WD, Adjouadi M (2014) Classification of fMRI patterns—a study of the language network segregation in pediatric localization related epilepsy. Hum Brain Mapp 35:1446–1460. https://doi.org/10.1002/hbm.22265

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Neurosurgery, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands

    Joeky T. Senders & Marike L. Broekman

  2. Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA

    Joeky T. Senders, Mark M. Zaki, Aditya V. Karhade, Bliss Chang, William B. Gormley, Marike L. Broekman, Timothy R. Smith & Omar Arnaout

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  1. Joeky T. Senders
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Correspondence to Omar Arnaout.

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J.T.S., M.M.Z., O.A., A.V.K., B.C., M.L.B., T.R.S. have nothing to disclose. W.B.G.: Codman, Coviden Proctor, Consultant.

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Joeky T. Senders and Mark M. Zaki shared first author.

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Senders, J.T., Zaki, M.M., Karhade, A.V. et al. An introduction and overview of machine learning in neurosurgical care. Acta Neurochir 160, 29–38 (2018). https://doi.org/10.1007/s00701-017-3385-8

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