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01-01-2018 | Review Article - Neurosurgical Techniques

An introduction and overview of machine learning in neurosurgical care

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

Published in: Acta Neurochirurgica | Issue 1/2018

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

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 CrossRefPubMedPubMedCentral

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 CrossRefPubMedPubMedCentral

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

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 CrossRefPubMed

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.e561CrossRefPubMed

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:e0161498CrossRefPubMedPubMedCentral

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 60:173–177PubMed

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 CrossRefPubMed

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 CrossRefPubMed

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 CrossRefPubMed

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 192:572–575PubMed

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 CrossRefPubMedPubMedCentral

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 16:271–279CrossRefPubMed

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 224:715–727CrossRef

17.

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

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 CrossRefPubMed

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 CrossRefPubMed

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 CrossRefPubMed

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 72:645–654CrossRefPubMed

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 CrossRefPubMed

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 CrossRefPubMed

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 CrossRefPubMed

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 CrossRefPubMed

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 64:134–146CrossRefPubMed

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 CrossRefPubMedPubMedCentral

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 CrossRef

29.

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

30.

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

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 CrossRefPubMed

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

33.

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

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 CrossRefPubMed

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 CrossRefPubMedPubMedCentral

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 PubMed

37.

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

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:e0125143CrossRefPubMedPubMedCentral

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 116:477–485CrossRefPubMed

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 CrossRefPubMed

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 CrossRefPubMed

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 CrossRefPubMedPubMedCentral

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 CrossRefPubMed

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 CrossRefPubMed

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 CrossRefPubMed

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 CrossRefPubMedPubMedCentral

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 CrossRefPubMed

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 CrossRefPubMedPubMedCentral

50.

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

51.

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

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 18:143–155CrossRefPubMed

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 CrossRefPubMed

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 CrossRefPubMedPubMedCentral

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

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 CrossRefPubMed

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 95:345–349CrossRefPubMed

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 CrossRefPubMed

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

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 CrossRefPubMed

63.

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

64.

Skrobala A (2012) Beam orientation in stereotactic radiosurgery using artificial neural network. Radiother Oncol 103:S220CrossRef

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 142:401–406CrossRefPubMed

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 CrossRefPubMed

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.E754CrossRefPubMed

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 CrossRefPubMed

Title: An introduction and overview of machine learning in neurosurgical care
Authors: Joeky T. Senders
Mark M. Zaki
Aditya V. Karhade
Bliss Chang
William B. Gormley
Marike L. Broekman
Timothy R. Smith
Omar Arnaout
Publication date: 01-01-2018
Publisher: Springer Vienna
Published in: Acta Neurochirurgica / Issue 1/2018
Print ISSN: 0001-6268
Electronic ISSN: 0942-0940
DOI: https://doi.org/10.1007/s00701-017-3385-8

Keynote webinar | Spotlight on medication adherence

Springer Medicine

An introduction and overview of machine learning in neurosurgical care

Abstract

Background

Method

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Method

Results

Conclusions

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