An introduction and overview of machine learning in neurosurgical care
Open accessAbstract
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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Data-induced bias refers to the bias when the training data is not representative or insufficient that affects the algorithm model.
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Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955 · see citing works
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652 · see citing works
Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466 · see citing works
Machine Learning Applications in Cancer Prognosis and Prediction
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.11.005 · Cited by 3,338 · see citing works
Brain tumor segmentation with Deep Neural Networks
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.05.004 · Cited by 3,278 · see citing works
Probabilistic machine learning and artificial intelligence
Ghahramani, Z. (2015) · Nature · doi:10.1038/nature14541 · Cited by 1,995 · see citing works
Toward a Shared Vision for Cancer Genomic Data
Grossman, R. L., Heath, A. P., Ferretti, V., Varmus, H. E., Lowy, D. R., Kibbe, W. A., & Staudt, L. M. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1607591 · Cited by 1,680 · see citing works
Machine learning and neurosurgical outcome prediction: a systematic review
Senders, J. T., Staples, P. C., Karhade, A. V., Zaki, M. M., Gormley, W. B., Broekman, M. L. D., Smith, T. R., & Arnaout, O. (2018) · World Neurosurgery · doi:10.1016/j.wneu.2017.09.149 · Cited by 486 · see citing works
Self-citationMachine Learning and Decision Support in Critical Care
Johnson, A. E. W., Ghassemi, M. M., Nemati, S., Niehaus, K. E., Clifton, D. A., & Clifford, G. D. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2501978 · Cited by 438 · see citing works
Implementing Machine Learning in Radiology Practice and Research
Kohli, M. D., Prevedello, L. M. S., Filice, R. W., & Geis, J. R. (2017) · American Journal of Roentgenology · doi:10.2214/ajr.16.17224 · Cited by 301 · see citing works
Big Data and machine learning in radiation oncology: State of the art and future prospects
Bibault, J.-E., Giraud, P., & Burgun, A. (2016) · Cancer Letters · doi:10.1016/j.canlet.2016.05.033 · Cited by 298 · see citing works
Classifying Human Brain Tumors by Lipid Imaging with Mass Spectrometry
Eberlin, L. S., Norton, I. H., Dill, A. L., Golby, A. J., Ligon, K. L., Santagata, S., Cooks, R. G., & Agar, N. Y. R. (2011) · Cancer Research · doi:10.1158/0008-5472.can-11-2465 · Cited by 294 · see citing works
Natural and Artificial Intelligence in Neurosurgery: A Systematic Review
Senders, J. T., Arnaout, O. M., Karhade, A. V., Dasenbrock, H. H., Gormley, W. B., Broekman, M. L. D., & Smith, T. R. (2017) · Neurosurgery · doi:10.1093/neuros/nyx384 · Cited by 264 · see citing works
Self-citationKassahun, Y., Yu, B., Tibebu, A. T., Stoyanov, D., Giannarou, S., Metzen, J. H., & Vander Poorten, E. (2016) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-015-1305-z · Cited by 238 · see citing works
Abbasi, S., & Tajeripour, F. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.09.051 · Cited by 205 · see citing works
Machine learning and systems genomics approaches for multi-omics data
Lin, E., & Lane, H. (2017) · Biomarker Research · doi:10.1186/s40364-017-0082-y · Cited by 192 · see citing works
Akbari, H., Macyszyn, L. J., Da, X., Bilello, M., Wolf, R. L., Martinez‐Lage, M., Biros, G., Alonso‐Basanta, M., O’Rourke, D. M., & Davatzikos, C. A. (2016) · Neurosurgery · doi:10.1227/neu.0000000000001202 · Cited by 164 · see citing works
Kalkanis, S. N., Kast, R. E., Rosenblum, M. L., Mikkelsen, T., Yurgelevic, S. M., Nelson, K., Raghunathan, A., Poisson, L., & Auner, G. W. (2014) · Journal of Neuro-Oncology · doi:10.1007/s11060-013-1326-9 · Cited by 150 · see citing works
Big Data and Machine Learning in Plastic Surgery: A New Frontier in Surgical Innovation
Kanevsky, J., Corban, J. P. A. H., Gaster, R. S., Kanevsky, A., Lin, S. J., & Gilardino, M. S. (2016) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0000000000002088 · Cited by 138 · see citing works
Mitchell, T. J., Hacker, C. D., Breshears, J. D., Szrama, N. P., Sharma, M., Bundy, D. T., Pahwa, M., Corbetta, M., Snyder, A. Z., Shimony, J. S., et al. (2013) · Neurosurgery · doi:10.1227/neu.0000000000000141 · Cited by 134 · see citing works
Predicting Epileptic Seizures in Advance
Moghim, N., & Corne, D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099334 · Cited by 121 · see citing works
Predictive approaches for drug combination discovery in cancer
Tonekaboni, S. A. M., Ghoraie, L. S., Manem, V. S., & Haibe‐Kains, B. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw104 · Cited by 118 · see citing works
Juan–Albarracín, J., Fuster‐García, E., Manjón, J. V., Robles, M., Aparici, F., Martí‐Bonmatí, L., & Garcia-Gomez, J. M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0125143 · Cited by 115 · see citing works
A Generic Support Vector Machine Model for Preoperative Glioma Survival Associations
Emblem, K. E., Pinho, M. C., Zöllner, F. G., Due‐Tønnessen, P., Hald, J. K., Schad, L. R., Meling, T. R., Rapalino, O., & Bjørnerud, A. (2014) · Radiology · doi:10.1148/radiol.14140770 · Cited by 112 · see citing works
MRI Measurement of Brain Tumor Response: Comparison of Visual Metric and Automatic Segmentation
Clarke, L. P., Velthuizen, R. P., Clark, M., Gaviria, J., Hall, L., Goldgof, D. B., Murtagh, R. F., Phuphanich, S., & Brem, S. S. (1998) · Magnetic Resonance Imaging · doi:10.1016/s0730-725x(97)00302-0 · Cited by 104 · see citing works
Akbari, H., Macyszyn, L. J., Da, X., Wolf, R. L., Bilello, M., Verma, R., O’Rourke, D. M., & Davatzikos, C. A. (2014) · Radiology · doi:10.1148/radiol.14132458 · Cited by 100 · see citing works
Shi, H., Hwang, S., Lee, K., & Lin, C. (2013) · Journal of neurosurgery · doi:10.3171/2013.1.jns121130 · Cited by 97 · see citing works
Automated MR image classification in temporal lobe epilepsy
Focke, N. K., Yogarajah, M., Symms, M. R., Gruber, O., Paulus, W., & Duncan, J. S. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.068 · Cited by 92 · see citing works
Pattern Recognition Approaches for Breast Cancer DCE-MRI Classification: A Systematic Review
Fusco, R., Sansone, M., Filice, S., Carone, G., Amato, D. M., Sansone, C., & Petrillo, A. (2016) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-016-0163-7 · Cited by 91 · see citing works
Intracranial pressure wave morphological classification: automated analysis and clinical validation
Nucci, C. G., BONIS, P. D., Mangiola, A., Santini, P., Sciandrone, M., Risi, A., & Anile, C. (2016) · Acta Neurochirurgica · doi:10.1007/s00701-015-2672-5 · Cited by 89 · see citing works
Computer-assisted abdominal surgery: new technologies
Kenngott, H. G., Wagner, M., Nickel, F., Wekerle, A.-L., Preukschas, A. A., Apitz, M., Schulte, T., Rempel, R., Mietkowski, P., Wagner, F., et al. (2015) · Langenbeck s Archives of Surgery · doi:10.1007/s00423-015-1289-8 · Cited by 77 · see citing works
Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique
Jones, T. L., Byrnes, T. J. D., Yang, G., Howe, F. A., Bell, B. A., & Barrick, T. R. (2014) · Neuro-Oncology · doi:10.1093/neuonc/nou159 · Cited by 74 · see citing works
Emblem, K. E., Nedregaard, B., Hald, J. K., Nome, T., Due‐Tønnessen, P., & Bjørnerud, A. (2009) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21815 · Cited by 73 · see citing works
Shiva Keihaninejad, A., Heckemann, R. A., Gousias, I. S., Hajnal, J. V., Duncan, J. S., Aljabar, P., Rueckert, D., & Hammers, A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033096 · Cited by 71 · see citing works
Asadi, H., Kok, H. K., Looby, S., Brennan, P., O'Hare, A., & Thornton, J. (2016) · World Neurosurgery · doi:10.1016/j.wneu.2016.09.086 · Cited by 67 · see citing works
Dumont, T. M., Rughani, A. I., & Tranmer, B. I. (2011) · World Neurosurgery · doi:10.1016/j.wneu.2010.07.007 · Cited by 66 · see citing works
Nowinski, W. L., Belov, D., & Benabid, A. L. (2002) · NeuroImage · doi:10.1006/nimg.2002.1299 · Cited by 65 · see citing works
A Review of Computational Methods to Predict the Risk of Rupture of Abdominal Aortic Aneurysms
Canchi, T., Srinivasan, D. K., Ng, E. Y. K., & Narayanan, S. (2015) · BioMed Research International · doi:10.1155/2015/861627 · Cited by 62 · see citing works
Azimi, P., Benzel, E. C., Shahzadi, S., Azhari, S., & Mohammadi, H. R. (2014) · Journal of Neurosurgery Spine · doi:10.3171/2013.12.spine13674 · Cited by 61 · see citing works
Momi, E. D., & Ferrigno, G. (2009) · Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · doi:10.1243/09544119jeim585 · Cited by 58 · see citing works
Azimi, P., & Mohammadi, H. R. (2014) · Journal of Neurosurgery Pediatrics · doi:10.3171/2013.12.peds13423 · Cited by 55 · see citing works
Computer-aided diagnosis of breast cancer using cytological images: A systematic review
Saha, M., Mukherjee, R., & Chakraborty, C. (2016) · Tissue and Cell · doi:10.1016/j.tice.2016.07.006 · Cited by 55 · see citing works
Çeltikçi, E. (2017) · Turkish Neurosurgery · doi:10.5137/1019-5149.jtn.20059-17.1 · Cited by 53 · see citing works
Predicting Outcome of Anterior Temporal Lobectomy Using Simulated Neural Networks
Grigsby, J., Kramer, R. E., Schneiders, J. L., Gates, J. R., & Smith, W. B. (1998) · Epilepsia · doi:10.1111/j.1528-1157.1998.tb01275.x · Cited by 49 · see citing works
Dolz, J., Betrouni, N., Quidet, M., Kharroubi, D., Leroy, H. A., Reyns, N., Massoptier, L., & Vermandel, M. (2016) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2016.03.003 · Cited by 48 · see citing works
Intracranial Pressure Processing with Artificial Neural Networks: Prediction of ICP Trends
Świercz, M., Mariak, Z. D., Krejza, J., Lewko, J., & Szydlik, P. (2000) · Acta Neurochirurgica · doi:10.1007/s007010050449 · Cited by 47 · see citing works
Neural network analysis of preoperative variables and outcome in epilepsy surgery
Arle, J. E., Perrine, K. R., Devinsky, O., & Doyle, W. (1999) · Journal of neurosurgery · doi:10.3171/jns.1999.90.6.0998 · Cited by 43 · see citing works
Azami, M. E., Hammers, A., Jung, J., Costes, N., Bouet, R., & Lartizien, C. (2016) · PLoS ONE · doi:10.1371/journal.pone.0161498 · Cited by 38 · see citing works
Taghva, A. S., Li, K. W., Liu, J. C., Gokaslan, Z. L., & Hsieh, P. C. (2010) · Neurosurgery · doi:10.1227/01.neu.0000365270.23815.b1 · Cited by 31 · see citing works
Shamim, M. S., Enam, S. A., & Qidwai, U. A. (2009) · Surgical Neurology · doi:10.1016/j.surneu.2009.07.012 · Cited by 30 · see citing works
Campillo‐Gimenez, B., Garcelon, N., Jarno, P., Marc, C. J., & Marc Cuggia, A. (2013) · Studies in health technology and informatics · doi:10.3233/978-1-61499-289-9-572 · Cited by 29 · see citing works
Mariak, Z. D., Świercz, M., Krejza, J., Lewko, J., & Łysoń, T. (2000) · Acta Neurochirurgica · doi:10.1007/s007010050450 · Cited by 25 · see citing works
Azimi, P., Benzel, E. C., Shahzadi, S., Azhari, S., & Mohammadi, H. R. (2016) · PubMed · Cited by 24 · see citing works
Probabilistic machine learning for the evaluation of presurgical language dominance
Gazit, T., Andelman, F., Glikmann‐Johnston, Y., Gonen, T., Solski, A., Shapira‐Lichter, I., Ovadia, M., Kipervasser, S., Neufeld, M. Y., Fried, I., et al. (2016) · Journal of neurosurgery · doi:10.3171/2015.7.jns142568 · Cited by 23 · see citing works
Computational Analyses of Arteriovenous Malformations in Neuroimaging
Ieva, A. D., Boukadoum, M., Lahmiri, S., & Cusimano, M. D. (2014) · Journal of Neuroimaging · doi:10.1111/jon.12200 · Cited by 23 · see citing works
Taghva, A. S. (2011) · World Neurosurgery · doi:10.1016/j.wneu.2010.11.008 · Cited by 21 · see citing works
An Automated Navigation System for Deep Brain Stimulator Placement Using Hidden Markov Models
Taghva, A. S. (2010) · Operative Neurosurgery · doi:10.1227/01.neu.0000365369.48392.e8 · Cited by 17 · see citing works
Schmidt, B. T., Bocklisch, S. F., Päßler, M., Czosnyka, M., Schwarze, J. J., & Klingelhöfer, J. (2006) · Springer eBooks · doi:10.1007/3-211-32318-x_71 · Cited by 17 · see citing works
An Intelligent Decision System for Intraoperative Somatosensory Evoked Potential Monitoring
Fan, B., Li, H., & Hu, Y. S. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2477557 · Cited by 16 · see citing works
Obituary: Albert Loren Rhoton Jr., MD, 1932–2016
Friedman, W. A. (2016) · Journal of neurosurgery · doi:10.3171/2016.3.jns16524 · Cited by 14 · see citing works
Bodensteiner, J. B. (2009) · Epilepsia · doi:10.1111/j.1528-1167.2008.01869.x · Cited by 14 · see citing works
Wang, J., You, X., Wu, W., Guillen, M. R., Cabrerizo, M., Sullivan, J. E., Donner, E. J., Björnson, B., Gaillard, W. D., & Adjouadi, M. (2013) · Human Brain Mapping · doi:10.1002/hbm.22265 · Cited by 13 · see citing works
Antoni, S.-T., Rinast, J., Ma, X., Schupp, S., & Schlaefer, A. (2016) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-016-1423-2 · Cited by 8 · see citing works
Foroni, R. I., Giri, M. G., Gerosa, M. A., Nicolato, A., Piovan, E., Zampieri, P., Pasqualin, A., Bortolazzi, E., Pasoli, A., Marchini, G., et al. (1995) · Stereotactic and Functional Neurosurgery · doi:10.1159/000098773 · Cited by 4 · see citing works
OC-0550 BEAM ORIENTATION IN STEREOTACTIC RADIOSURGERY USING ARTIFICIAL NEURAL NETWORK
Skrobała, A. (2012) · Radiotherapy and Oncology · doi:10.1016/s0167-8140(12)70889-7 · Cited by 2 · see citing works
Flagged first, then by citations
2 of the 68 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Mitchell TM (1997) Machine learning. McGraw-Hill Science, New York
Cited by
Sorted by citations
Speech Recognition Using Deep Neural Networks: A Systematic Review
Nassif, A. B., Shahin, I. M. A., Attili, I. B., Azzeh, M., & Shaalan, K. F. (2019) · IEEE Access · doi:10.1109/access.2019.2896880 · Cited by 1,159 · see citing works
Ethics and governance of trustworthy medical artificial intelligence
Zhang, J., & Zhang, Z.-M. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02103-9 · Cited by 507 · see citing works
“Data-induced bias refers to the bias when the training data is not representative or insufficient that affects the algorithm model [27, 28].”
BackgroundArtificial intelligence for good health: a scoping review of the ethics literature.
Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · BMC Medical Ethics · doi:10.1186/s12910-021-00577-8 · Cited by 435 · see citing works
Artificial intelligence in reproductive medicine
Wang, R., Pan, W. D., Jin, L., Li, Y., Geng, Y., Gao, C., Chen, G., Wang, H., Ma, D., & Liao, S. (2019) · Reproduction · doi:10.1530/rep-18-0523 · Cited by 255 · see citing works
Buchlak, Q. D., Esmaili, N., Leveque, J., Farrokhi, F. R., Bennett, C. C., Piccardi, M., & Sethi, R. K. (2019) · Neurosurgical Review · doi:10.1007/s10143-019-01163-8 · Cited by 226 · see citing works
The impact of machine learning on patient care: A systematic review
Ben-Israel, D., Jacobs, W. B., Casha, S., Lang, S., Ryu, W. H. A., de Lotbiniere-Bassett, M., & Cadotte, D. W. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101785 · Cited by 207 · see citing works
Artificial intelligence for brain diseases: A systematic review
Segato, A., Marzullo, A., Calimeri, F., & Momi, E. D. (2020) · APL Bioengineering · doi:10.1063/5.0011697 · Cited by 178 · see citing works
A systematic literature review of speech emotion recognition approaches
Singh, Y. B., & Goel, S. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.028 · Cited by 150 · see citing works
Huang, Y., Talwar, A., Chatterjee, S., & Aparasu, R. R. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01284-z · Cited by 139 · see citing works
Artificial intelligence, physiological genomics, and precision medicine
Williams, A. M., Liu, Y., Regner, K. R., Jotterand, F., Liu, P., & Liang, M. (2018) · Physiological Genomics · doi:10.1152/physiolgenomics.00119.2017 · Cited by 129 · see citing works
Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning
Paliwal, N., Jaiswal, P., Tutino, V. M., Shallwani, H., Davies, J. M., Siddiqui, A. H., Rai, R., & Meng, H. (2018) · Neurosurgical FOCUS · doi:10.3171/2018.8.focus18332 · Cited by 109 · see citing works
Davids, J., Manivannan, S., Darzi, A., Giannarou, S., Ashrafian, H., & Marcus, H. J. (2021) · Neurosurgical Review · doi:10.1007/s10143-020-01378-0 · Cited by 106 · see citing works
Pennisi, F., Pinto, A., Ricciardi, G. E., Signorelli, C., & Gianfredi, V. (2025) · Antibiotics · doi:10.3390/antibiotics14020134 · Cited by 113 · see citing works
Machine learning for sperm selection
You, J. B., McCallum, C., Wang, Y., Riordon, J., Nosrati, R., & Sinton, D. A. (2021) · Nature Reviews Urology · doi:10.1038/s41585-021-00465-1 · Cited by 101 · see citing works
Ugga, L., Cuocolo, R., Solari, D., Guadagno, E., D’Amico, A., Somma, T., Cappabianca, P., De, M. D. B., Cavallo, L. M., & Brunetti, A. (2019) · Neuroradiology · doi:10.1007/s00234-019-02266-1 · Cited by 97 · see citing works
Machine learning–based preoperative predictive analytics for lumbar spinal stenosis
Siccoli, A., de Wispelaere, M. P., Schröder, M. L., & Staartjes, V. E. (2019) · Neurosurgical FOCUS · doi:10.3171/2019.2.focus18723 · Cited by 91 · see citing works
Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future
Tangsrivimol, J. A., Schonfeld, E., Zhang, M., Veeravagu, A., Smith, T. R., Härtl, R., Lawton, M. T., Sherbini, A. E., Prevedello, D. M., Glicksberg, B. S., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13142429 · Cited by 86 · see citing works
Self-citationMachine learning in neurosurgery: a global survey
Staartjes, V. E., Stumpo, V., Kernbach, J. M., Klukowska, A. M., Gadjradj, P. S., Schröder, M. L., Veeravagu, A., Stienen, M. N., van Niftrik, C. H. B., Serra, C., et al. (2020) · Acta Neurochirurgica · doi:10.1007/s00701-020-04532-1 · Cited by 78 · see citing works
Cuocolo, R., Ugga, L., Solari, D., Corvino, S., D’Amico, A., Russo, D., Cappabianca, P., Cavallo, L. M., & Elefante, A. (2020) · Neuroradiology · doi:10.1007/s00234-020-02502-z · Cited by 76 · see citing works
Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm
Williams, S. C., Horsfall, H. L., Funnell, J. P., HANRAHAN, J. G., Khan, D. Z., Muirhead, W. R., Stoyanov, D. V., & Marcus, H. J. (2021) · Cancers · doi:10.3390/cancers13195010 · Cited by 77 · see citing works
Stanzione, A., Cuocolo, R., Cocozza, S., Romeo, V., Persico, F., Fusco, F., Longo, N., Brunetti, A., & Imbriaco, M. (2019) · Academic Radiology · doi:10.1016/j.acra.2018.12.025 · Cited by 73 · see citing works
Hale, A. T., Stonko, D. P., Wang, L. A., Strother, M. K., & Chambless, L. B. (2018) · Neurosurgical FOCUS · doi:10.3171/2018.8.focus18191 · Cited by 67 · see citing works
Nerve optic segmentation in CT images using a deep learning model and a texture descriptor
Ranjbarzadeh, R., Dorosti, S., Ghoushchi, S. J., Safavi, S., Razmjooy, N., Sarshar, N. T., Anari, S., & Bendechache, M. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00694-w · Cited by 65 · see citing works
Turner, O. C., Aeffner, F., Bangari, D. S., High, W., Knight, B., Forest, T., Cossic, B., Himmel, L. E., Rudmann, D. G., Bawa, B., et al. (2020) · Toxicologic Pathology · doi:10.1177/0192623319881401 · Cited by 64 · see citing works
Tunthanathip, T., Sae-heng, S., Oearsakul, T., Sakarunchai, I., Kaewborisutsakul, A., & Taweesomboonyat, C. (2019) · Neurosurgical FOCUS · doi:10.3171/2019.5.focus19241 · Cited by 62 · see citing works
Nguyen, A. V. P., Blears, E. E., Ross, E., Lall, R. R., & Ortega-Barnett, J. R. (2018) · Neurosurgical FOCUS · doi:10.3171/2018.8.focus18325 · Cited by 59 · see citing works
Big data, machine learning, and artificial intelligence: a field guide for neurosurgeons
Raju, B., Jumah, F., Ashraf, O., Narayan, V., Gupta, G., Sun, H., Hilden, P., & Nanda, A. (2020) · Journal of neurosurgery · doi:10.3171/2020.5.jns201288 · Cited by 58 · see citing works
Courville, E., Kazim, S. F., Vellek, J., Tarawneh, O. H., Stack, J., Roster, K., Roy, J. M., Schmidt, M. H., & Bowers, C. A. (2023) · Surgical Neurology International · doi:10.25259/sni_312_2023 · Cited by 57 · see citing works
Topics and trends in artificial intelligence assisted human brain research
Chen, X., Chen, J., Cheng, G., & Gong, T. (2020) · PLoS ONE · doi:10.1371/journal.pone.0231192 · Cited by 56 · see citing works
Machine Learning and Artificial Intelligence in Neurosurgery: Status, Prospects, and Challenges
Dagi, T. F., Barker, F. G., & Glass, J. L. (2021) · Neurosurgery · doi:10.1093/neuros/nyab170 · Cited by 53 · see citing works
Biosensors for Epilepsy Management: State-of-Art and Future Aspects
Tiwari, S., Sharma, V., Mujawar, M. A., Mishra, Y. K., Kaushik, A. K., & Ghosal, A. (2019) · Sensors · doi:10.3390/s19071525 · Cited by 51 · see citing works
Using an artificial neural network to predict traumatic brain injury
Hale, A. T., Stonko, D. P., Lim, J., Guillamondegui, O. D., Shannon, C. N., & Patel, M. B. (2018) · Journal of Neurosurgery Pediatrics · doi:10.3171/2018.8.peds18370 · Cited by 44 · see citing works
Chen, C., Qin, Y., Chen, H., Zhu, D., Gao, F., & Zhou, X. (2021) · Insights into Imaging · doi:10.1186/s13244-021-01034-1 · Cited by 42 · see citing works
In Reply: Big Data Research in Neurosurgery: A Critical Look at this Popular New Study Design
Oravec, C. S., Motiwala, M., Reed, K., Kondziolka, D. S., Barker, F. G., Michael, L. M., & Klimo, P. (2018) · Neurosurgery · doi:10.1093/neuros/nyy102 · Cited by 41 · see citing works
Zoli, M., Staartjes, V. E., Guaraldi, F., Friso, F., Rustici, A., Asioli, S., Sollini, G., Pasquini, E., Regli, L., Serra, C., et al. (2020) · Neurosurgical FOCUS · doi:10.3171/2020.3.focus2060 · Cited by 39 · see citing works
Marcus, A. P., Marcus, H. J., Camp, S., Nandi, D., Kitchen, N. D., & Thorne, L. W. (2020) · Scientific Reports · doi:10.1038/s41598-020-62160-2 · Cited by 36 · see citing works
Danilov, G., Shifrin, M. A., Kotik, K., Ishankulov, T., Orlov, Y. N., Kulikov, A. S., & Aa, P. (2020) · Современные технологии в медицине · doi:10.17691/stm2020.12.5.12 · Cited by 35 · see citing works
Machine learning in deep brain stimulation: A systematic review
Peralta, M., Jannin, P., & Baxter, J. S. H. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102198 · Cited by 34 · see citing works
WITHDRAWN: Voice recognition system using machine learning techniques
Ali, A. T., Abdullah, H. S., & Fadhil, M. N. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.04.075 · Cited by 35 · see citing works
RetractedDraelos, R. L., Ezekian, J. E., Zhuang, F., Moya‐Mendez, M. E., Zhang, Z., Rosamilia, M. B., Manivannan, P. K. R., Henao, R., & Landstrom, A. P. (2022) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.121.010326 · Cited by 34 · see citing works
A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy
Ahmed, M. I. B., Alotaibi, S., Rahman, A., Dash, S., Nabil, M., & AlTurki, A. O. (2022) · SN Computer Science · doi:10.1007/s42979-022-01358-9 · Cited by 34 · see citing works
Wu, X., Sun, Y., Xu, X. L., Steyerberg, E. W., Helmrich, I. R., Lecky, F. E., Guo, J., Li, X., Feng, J., Mao, Q., et al. (2023) · Journal of Neurotrauma · doi:10.1089/neu.2022.0221 · Cited by 34 · see citing works
Kwong, J. C. C., Wu, J., Malik, S., Khondker, A., Gupta, N. K., Bodnariuc, N., Narayana, K., Malik, M., van der Kwast, T., Johnson, A. E. W., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01088-7 · Cited by 35 · see citing works
Boaro, A., Kaczmarzyk, J., Kavouridis, V. K., Harary, M., Mammi, M., Dawood, H. Y., Shea, A., Cho, E. Y., Juvekar, P., Noh, T., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-19356-5 · Cited by 32 · see citing works
Self-citationSanpablo, A. I. P., Quinzaños-Fresnedo, J., Gutiérrez-Martínez, J., Rodríguez, I. G. L., & Roldán-Valadéz, E. (2025) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/0115734056370620250403030638 · Cited by 34 · see citing works
Natheir, S., Christie, S., Yilmaz, R., Winkler-Schwartz, A., Bajunaid, K. M., Sabbagh, A. J., Werthner, P., Fares, J., Azarnoush, H., & Maestro, R. F. D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106286 · Cited by 31 · see citing works
Ethical Dilemmas of Using Artificial Intelligence in Medicine
Astărăstoae, V., Rogozea, L. M., Leașu, F., & Ioan, B. (2024) · American Journal of Therapeutics · doi:10.1097/mjt.0000000000001693 · Cited by 23 · see citing works
Staartjes, V. E., Sebök, M., Blum, P. G., Serra, C., Germans, M. R., Krayenbühl, N., Regli, L., & Esposito, G. (2020) · Acta Neurochirurgica · doi:10.1007/s00701-020-04355-0 · Cited by 30 · see citing works
Noor, N. S. E. M., & Ibrahim, H. (2020) · IEEE Access · doi:10.1109/access.2020.2998934 · Cited by 30 · see citing works
The Intersection of Artificial Intelligence, Telemedicine, and Neurophysiology
Tavares, D. L., Lopes, A. I., Castro, C., Maia, G. H., Leite, L., & Quintas, M. (2023) · Advances in medical education, research, and ethics (AMERE) book series · doi:10.4018/978-1-6684-7164-7.ch006 · Cited by 30 · see citing works
Huang, J., Shlobin, N. A., DeCuypere, M., & Lam, S. K. (2021) · Neurosurgery · doi:10.1227/neu.0000000000001736 · Cited by 28 · see citing works
Isa, I. S., Yusof, U. K., & Zain, M. M. (2023) · Applied Sciences · doi:10.3390/app13021195 · Cited by 25 · see citing works
Uparela-Reyes, M. J., Villegas-Trujillo, L. M., Cespedes, J., Velásquez-Vera, M., & Rubiano, A. M. (2024) · World Neurosurgery · doi:10.1016/j.wneu.2024.05.065 · Cited by 26 · see citing works
Jain, S., Naicker, D., Raj, R., Patel, V., Hu, Y., Srinivasan, K., & Jen, C. (2023) · Diagnostics · doi:10.3390/diagnostics13091563 · Cited by 22 · see citing works
Chen, C., Qin, Y., Chen, H., Cheng, J., He, B., Wan, Y., Zhu, D., Gao, F., & Zhou, X. (2022) · European Radiology · doi:10.1007/s00330-021-08465-w · Cited by 20 · see citing works
Application of artificial intelligence in gametes and embryos selection
Si, K., Huang, B., & Jin, L. (2023) · Human Fertility · doi:10.1080/14647273.2023.2256980 · Cited by 20 · see citing works
Vedaei, F., Mashhadi, N., Zabrecky, G. P., Monti, D., Navarreto, E., Hriso, C., Wintering, N., Newberg, A. B., & Mohamed, F. B. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1099560 · Cited by 19 · see citing works
Mooney, C., Eogan, M., Áinle, F. N., Cleary, B., Gallagher, J., O'Loughlin, J. A., & Drew, R. J. (2020) · International Journal of Laboratory Hematology · doi:10.1111/ijlh.13434 · Cited by 18 · see citing works
A Brief History of Machine Learning in Neurosurgery
Schilling, A. T., Shah, P. P., Feghali, J., Jimenez, A. E., & Azad, T. D. (2021) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-030-85292-4_27 · Cited by 20 · see citing works
Artificial intelligence in neuroradiology: a scoping review of some ethical challenges
Khosravi, P., & Schweitzer, M. (2023) · Frontiers in Radiology · doi:10.3389/fradi.2023.1149461 · Cited by 18 · see citing works
Computer-vision based analysis of the neurosurgical scene – A systematic review
Buyck, F., Vandemeulebroucke, J., Ceranka, J., Gestel, F. V., Cornelius, J. F., Duerinck, J., & Bruneau, M. (2023) · Brain and Spine · doi:10.1016/j.bas.2023.102706 · Cited by 18 · see citing works
Soldozy, S., Farzad, F., Young, S., Yağmurlu, K., Norat, P., Sokolowski, J. D., Park, M. S., Jane, J. A., & Syed, H. R. (2020) · World Neurosurgery · doi:10.1016/j.wneu.2020.07.104 · Cited by 16 · see citing works
King, H., Wright, J. M., Treanor, D. E., Williams, B. J., & Randell, R. (2022) · Journal of Medical Internet Research · doi:10.2196/38039 · Cited by 19 · see citing works
Noor, N. S. E. M., Ibrahim, H., Lah, M. H. C., & Abdullah, J. M. (2021) · IEEE Access · doi:10.1109/access.2021.3109780 · Cited by 16 · see citing works
Miyagawa, T., Sasaki, M., & Yamaura, A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0240845 · Cited by 16 · see citing works
Applications of Machine Learning in Pediatric Hydrocephalus
Pahwa, B., Bali, O., Goyal, S., & Kedia, S. (2021) · Neurology India · doi:10.4103/0028-3886.332287 · Cited by 15 · see citing works
Bhatele, K. R., & Bhadauria, S. S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13439-1 · Cited by 15 · see citing works
Discrepancies in Stroke Distribution and Dataset Origin in Machine Learning for Stroke
Velagapudi, L., Mouchtouris, N., Baldassari, M. P., Nauheim, D. O., Khanna, O., Saiegh, F. A., Herial, N. A., Gooch, M. R., Tjoumakaris, S. I., Rosenwasser, R. H., et al. (2021) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2021.105832 · Cited by 15 · see citing works
Miyagawa, T., Saga, M., Sasaki, M., Shimizu, M., & Yamaura, A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0278562 · Cited by 15 · see citing works
Chen, C., Qin, Y., Cheng, J., Gao, F., & Zhou, X. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.701289 · Cited by 14 · see citing works
Tool-tissue force segmentation and pattern recognition for evaluating neurosurgical performance
Baghdadi, A., Lama, S., Singh, R. S., & Sutherland, G. R. (2023) · Scientific Reports · doi:10.1038/s41598-023-36702-3 · Cited by 14 · see citing works
Kernbach, J. M., & Staartjes, V. E. (2021) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-030-85292-4_2 · Cited by 14 · see citing works
Revolutionizing Neurosurgery with GPT-4: A Leap Forward or Ethical Conundrum?
Li, W., Fu, M., Liu, S., & Yu, H. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03240-y · Cited by 14 · see citing works
AI-Driven Automation in a Human-Centered Cyber World
Smith, N. L., Teerawanit, J., & Hamid, O. H. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00551 · Cited by 14 · see citing works
Sharma, S., Singh, G., & Singh, D. (2019) · ICST Transactions on Scalable Information Systems · doi:10.4108/eai.13-7-2018.158419 · Cited by 14 · see citing works
Levy, A. S., Bhatia, S., Merenzon, M. A., Andryski, A. L., Rivera, C. A., Daggubati, L. C., Di, L., Shah, A. H., Komotar, R. J., & Ivan, M. E. (2023) · World Neurosurgery · doi:10.1016/j.wneu.2023.10.042 · Cited by 13 · see citing works
Windermere, S. A., Shah, S., Hey, G. E., McGrath, K. V., & Rahman, M. (2025) · World Neurosurgery · doi:10.1016/j.wneu.2025.123809 · Cited by 13 · see citing works
Daily Pain Prediction Using Smartphone Speech Recordings of Patients With Spine Disease
Duey, A. H., Rana, A., Siddi, F., Hussein, H., Onnela, J.-P. “., & Smith, T. R. (2023) · Neurosurgery · doi:10.1227/neu.0000000000002474 · Cited by 13 · see citing works
Self-citationA review of artificial intelligence applications in in vitro fertilization
Zhang, Q. R., Liang, X., & Chen, Z. (2024) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-024-03284-6 · Cited by 12 · see citing works
Survival Prediction After Neurosurgical Resection of Brain Metastases: A Machine Learning Approach
Hulsbergen, A. F. C., Lo, Y. T., Awakimjan, I., Kavouridis, V. K., Phillips, J. G., Smith, T. R., Verhoeff, J. J. C., Yu, K., Broekman, M. L. D., & Arnaout, O. M. (2022) · Neurosurgery · doi:10.1227/neu.0000000000002037 · Cited by 12 · see citing works
Self-citationVillalonga, J. F., Solari, D., Cuocolo, R., Lucia, V. D., Ugga, L., Gragnaniello, C., Pailler, J. I., Cervio, A., Campero, Á., Cavallo, L. M., et al. (2022) · Frontiers in Surgery · doi:10.3389/fsurg.2022.934721 · Cited by 12 · see citing works
Information-Based Medicine in Glioma Patients: A Clinical Perspective
Senders, J. T., Harary, M., Stopa, B. M., Staples, P. C., Broekman, M. L. D., Smith, T. R., Gormley, W. B., & Arnaout, O. M. (2018) · Computational and Mathematical Methods in Medicine · doi:10.1155/2018/8572058 · Cited by 12 · see citing works
Self-citationPepa, G. M. D., Caccavella, V. M., Menna, G., Ius, T., Auricchio, A., Chiesa, S., Gaudino, S., Marchese, E., & Olivi, A. (2021) · World Neurosurgery · doi:10.1016/j.wneu.2021.01.082 · Cited by 11 · see citing works
Kim, S. H., Lee, S. H., & Shin, D. A. (2022) · Clinical Spine Surgery A Spine Publication · doi:10.1097/bsd.0000000000001295 · Cited by 11 · see citing works
Mládek, A., Gerla, V., Skalický, P., Vlasák, A., Zazay, A., Lhotská, L., Beneš, V., Beneš, V., & Bradáč, O. (2022) · Neurosurgery · doi:10.1227/neu.0000000000001838 · Cited by 11 · see citing works
Agadi, K., Dominari, A., Tebha, S. S., Mohammadi, A., & Zahid, S. (2022) · Journal of Korean Neurosurgical Society · doi:10.3340/jkns.2021.0213 · Cited by 11 · see citing works
Nahum, U., Refardt, J. C., Chifu, I. O., Fenske, W. K., Faßnacht, M., Szinnai, G., Christ‐Crain, M., & Pfister, M. (2022) · European Journal of Endocrinology · doi:10.1530/eje-22-0368 · Cited by 10 · see citing works
Velagapudi, L., Saiegh, F. A., Swaminathan, S., Mouchtouris, N., Khanna, O., Sabourin, V. M., Gooch, M. R., Herial, N. A., Tjoumakaris, S. I., Rosenwasser, R. H., et al. (2022) · Clinical Neurology and Neurosurgery · doi:10.1016/j.clineuro.2022.107547 · Cited by 10 · see citing works
Hyper-parameter tuning for support vector machine using an improved cat swarm optimization algorithm
Abdulraheem, S. A., Aliyu, S., & Abdullahi, F. B. (2023) · Journal of the Nigerian Society of Physical Sciences · doi:10.46481/jnsps.2023.1007 · Cited by 9 · see citing works
A Deep Neural Network-Based Model Predicting Peritumoral Edema After Radiosurgery for Meningioma
Kim, K. H., Jung, S., Lee, H.-J., Kwon, H., Choi, S.-W., Koh, H., Youm, J.-Y., & Kim, S. (2022) · World Neurosurgery · doi:10.1016/j.wneu.2022.04.125 · Cited by 9 · see citing works
Wu, M., Jiang, X., Du, K., Xu, Y., & Zhang, W. (2023) · Scientific Reports · doi:10.1038/s41598-023-33930-5 · Cited by 9 · see citing works
Machine learning-based clinical prediction modeling -- A practical guide for cliniciansPreprint
Kernbach, J. M., & Staartjes, V. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15069 · Cited by 8 · see citing works
Fernandes, R. T., Fernandes, F. W., Kundu, M., Ramsay, D. S. C., Salih, A. A., Namireddy, S. R., Janković, D., Kalasauskas, D., Ottenhausen, M., Kramer, A., et al. (2024) · World Neurosurgery · doi:10.1016/j.wneu.2024.09.087 · Cited by 7 · see citing works
Predicting Outcomes in Patients with Traumatic Brain Injury Using Machine Learning Models
Noor, N. S. E. M., & Ibrahim, H. (2019) · Lecture notes in mechanical engineering · doi:10.1007/978-981-13-9539-0_2 · Cited by 7 · see citing works
Rivera, C. A., Bhatia, S., Uppalapati, V., Berke, C. N., Merenzon, M. A., Daggubati, L. C., Levy, A. S., Shah, A. H., Komotar, R. J., & Ivan, M. E. (2024) · Neurosurgical FOCUS · doi:10.3171/2024.8.focus24434 · Cited by 6 · see citing works
A bibliometric analysis of perioperative medicine and artificial intelligence
Chan, L. K. M., Mao, B. P., & Zhu, R. (2025) · Journal of Perioperative Practice · doi:10.1177/17504589251320811 · Cited by 6 · see citing works
SHADE-AD: An LLM-Based Framework for Synthesizing Activity Data of Alzheimer’s Patients
Fu, H., Chen, H., Lin, S., & Xing, G. (2025) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3715014.3722062 · Cited by 8 · see citing works
Emani, S. S., Swaminathan, A., Grobman, B., Duvall, J. B., López, I., Arnaout, O. M., & Huang, K. T. (2023) · Neurosurgical FOCUS · doi:10.3171/2023.3.focus2352 · Cited by 7 · see citing works
Self-citationMachine Intelligence in Clinical Neuroscience: Taming the Unchained Prometheus
Staartjes, V. E., Regli, L., & Serra, C. (2021) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-030-85292-4_1 · Cited by 6 · see citing works
Machine Learning Techniques for Diagnosis of Lower Gastrointestinal Cancer: A Systematic Review
Langarizadeh, M., & Sayadi, M. (2021) · Iranian Red Crescent Medical Journal · doi:10.32592/ircmj.2021.23.7.436 · Cited by 6 · see citing works
- Authors
- Senders, J. T., Zaki, M. M., Karhade, A. V., Chang, B., Gormley, W. B., Broekman, M. L., Smith, T. R., & Arnaout, O.
- Affiliations
- Brigham and Women's Hospital (US)
- Corresponding author
- Arnaout, Omar
- ORCID
- Venue
- Acta Neurochirurgica
- Published
- 2017-11-13
- Volume
- 160
- Issue
- 1
- Pages
- 29-38
- ISSN
- 0001-6268 (ISSN-L)
- 0942-0940
- OpenAlex
- W2768899399
- Cited by
- 178
- References
- 68
- Citations by year
- 2026: 10 · 2025: 22 · 2024: 9 · 2023: 28 · 2022: 29 · 2021: 33 · 2020: 22 · 2019: 15 · 2018: 10
- FWCI
- 0.35
- Type
- journal article
- Language
- en
- Open access
- Locations
- Acta Neurochirurgica (publishedVersion)
- PubMed (publishedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- machine learning, neurosurgery, epilepsy, brain tumors, spinal lesions, neurovascular pathology, Parkinson's disease, traumatic brain injury, hydrocephalus, presurgical planning, intraoperative guidance, neuromonitoring, clinical implementation, model validation, AI ethics
- MeSH terms
- Machine Learning, Brain, Decision Making, Humans, Prognosis, Spinal Cord, Neurosurgical Procedures
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