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Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review

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Senders, Joeky T., Staples, Patrick C., Karhade, Aditya V., Zaki, Mark M., Gormley, William B., Broekman, Marike L.D., Smith, Timothy R., Arnaout, Omar·2018·World Neurosurgery

DOI 10.1016/j.wneu.2017.09.149

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Abstract

Objective Accurate measurement of surgical outcomes is highly desirable to optimize surgical decision-making. An important element of surgical decision making is identification of the patient cohort that will benefit from surgery before the intervention. Machine learning (ML) enables computers to learn from previous data to make accurate predictions on new data. In this systematic review, we evaluate the potential of ML for neurosurgical outcome prediction.Methods A systematic search in the PubMed and Embase databases was performed to identify all potential relevant studies up to January 1, 2017.Results Thirty studies were identified that evaluated ML algorithms used as prediction models for survival, recurrence, symptom improvement, and adverse events in patients undergoing surgery for epilepsy, brain tumor, spinal lesions, neurovascular disease, movement disorders, traumatic brain injury, and hydrocephalus. Depending on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively. Compared with logistic regression, ML models performed significantly better and showed a median absolute improvement in accuracy and area under the receiver operating curve of 15% and 0.06, respectively. Some studies also demonstrated a better performance in ML models compared with established prognostic indices and clinical experts.Conclusions In the research setting, ML has been studied extensively, demonstrating an excellent performance in outcome prediction for a wide range of neurosurgical conditions. However, future studies should investigate how ML can be implemented as a practical tool supporting neurosurgical care.

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“Some studies were identified by Senders et al. 20 which applied ML to predict outcomes after neurosurgery for epilepsy, Parkinson ’ s disease, hemorrhages, hydrocephalus, etc.”
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Alshammari, D. A., Obaidullah, A. J., Khasawneh, M. A., El-Sakhawy, M. A.-M., Elkholi, S. M. A., & Albaghdadi, M. F. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106828 · Cited by 7
Predicting the Severity and Discharge Prognosis of Traumatic Brain Injury Based on Intracranial Pressure Data Using Machine Learning Algorithms
Zhu, J., Shan, Y., Li, Y., Wu, X., & Gao, G. (2024) · World Neurosurgery · doi:10.1016/j.wneu.2024.03.085 · Cited by 7
Multicenter external validation of prediction models for clinical outcomes after spinal fusion for lumbar degenerative disease
Grob, A., Rohr, J., Stumpo, V., Vieli, M., Ciobanu-Caraus, O., Ricciardi, L., Maldaner, N., Raco, A., Miscusi, M., Perna, A., et al. (2024) · European Spine Journal · doi:10.1007/s00586-024-08395-3 · Cited by 7
“ODI oswestry disability index, BP back pain, LP leg pain, NRS numerical rating scale, LOESS locally estimated scatterplot smoothing range of patient characteristics [5], CPMs were developed assisting in the decision-making process [23].”
Predicting inferior vena cava filter complications using machine learning
Li, B., Eisenberg, N., Beaton, D., Lee, D. S., Al‐Omran, L., Wijeysundera, D. N., Hussain, M. A., Rotstein, O. D., de Mestral, C., Mamdani, M., et al. (2024) · Journal of Vascular Surgery Venous and Lymphatic Disorders · doi:10.1016/j.jvsv.2024.101943 · Cited by 7
Machine learning-based modified BAT score in predicting hematoma enlargement after spontaneous intracerebral hemorrhage
Zhou, H., Zhou, Z., Song, Z., & Li, X. (2021) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2021.09.030 · Cited by 7
Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost Model
Noor, N. S. E. M., Ibrahim, H., Lah, M. H. C., & Abdullah, J. M. (2022) · BioMedInformatics · doi:10.3390/biomedinformatics2010007 · Cited by 7
“The great majority of research included in the review [23,28] found that identifying the most informative qEEG that characterizes the recovery outcome level is crucial to ensure early targeted prediction after TBI post-injury.”
Background
Epileptic Seizure Prediction Based on EEG by Auto-Machine Learning
Chen, C., Peng, F., Sun, Y., Lv, D., Zhang, N., Wang, X., & Wang, L. (2022) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar54675.2022.9872265 · Cited by 7
Machine-learning derived algorithms for prediction of radiographic progression in early axial spondyloarthritis
Garofoli, R., Resche‐Rigon, M., Roux, C., van der Heijde, D. M. F. M., Dougados, M., & Moltó, A. (2022) · Clinical and Experimental Rheumatology · doi:10.55563/clinexprheumatol/mm2uzu · Cited by 7
Postoperative Delirium After Intracranial Surgery: A Retrospective Cohort Study
Kappen, P. R., Kappen, H. J., Dirven, C. M. F., Klimek, M., Jeekel, J., Andrinopoulou, E. R., Osse, R. J., & Vincent, A. J. P. E. (2023) · World Neurosurgery · doi:10.1016/j.wneu.2022.12.132 · Cited by 7
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 9
Artificial Intelligence in Clinical Neurosurgery: More than Machinery
Tewarie, I. A., Hulsbergen, A. F. C., Gormley, W. B., Peul, W. C., & Broekman, M. L. D. (2021) · World Neurosurgery · doi:10.1016/j.wneu.2021.02.057 · Cited by 7
Self-citation
Smart Neuropathy Detection using Machine Intelligence: Filling the Void Between Clinical Practice and Early Diagnosis
Salahuddin, T., Al-Maadeed, S. A., Petropoulos, I. N., Malik, R. A., Kamran, S., & Qidwai, U. A. (2019) · World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4) · doi:10.1109/worlds4.2019.8904015 · Cited by 7
Random Forest Prognostication of Survival and 6-Month Outcome in Pediatric Patients Following Decompressive Craniectomy for Traumatic Brain Injury
Morgan, R. D., Youssi, B. W., Cação, R., Hernandez, C., & Nagy, L. (2024) · World Neurosurgery · doi:10.1016/j.wneu.2024.10.075 · Cited by 6
Leveraging machine learning for preoperative prediction of supramaximal ablation in laser interstitial thermal therapy for brain tumors
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
Evaluating predictive performance, validity, and applicability of machine learning models for predicting HIV treatment interruption: a systematic review
Kwarah, W., Vroom, F. B. D.-C., Dwomoh, D., & Bosomprah, S. (2025) · BMC Global and Public Health · doi:10.1186/s44263-025-00184-4 · Cited by 7
Predicting 1-year successful clinical use of an arteriovenous access for hemodialysis using machine learning
Li, B., Eisenberg, N., Beaton, D., Lee, D. S., Al‐Omran, L., Wijeysundera, D. N., Hussain, M. A., Rotstein, O. D., Greco, E., de Mestral, C., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02187-9 · Cited by 9
“These models were chosen based on their established ef ﬁ cacy in predicting postoperative outcomes using structured data 38,44,45 .”
New insights for predicting surgery outcome in patients with temporal lobe epilepsy. A systematic review
Baciu, M. V., O'Sullivan, L., Torlay, L., & Banjac, S. (2023) · Revue Neurologique · doi:10.1016/j.neurol.2023.02.067 · Cited by 6
Machine Learning Models Using Routinely Collected Clinical Data Offer Robust and Interpretable Predictions of 90-Day Unplanned Acute Care Use for Cancer Immunotherapy Patients
Lu, S.-C., Knafl, M., Turin, A., Offodile, A. C., Ravi, V., & Sidey-Gibbons, C. J. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00123 · Cited by 6
The first case of glioma detected by an artificial intelligence algorithm running on real-time data in neurosurgery: illustrative case
Sözer, A., Börcek, A. Ö., Sağıroğlu, Ş., Poshtkouh, A., Demirtas, Z., Karaaslan, M. M., Kuzucu, P., & Çeltikçi, E. (2023) · Journal of Neurosurgery Case Lessons · doi:10.3171/case22536 · Cited by 6
“Artificial intelligence (AI) technology includes deep learning and machine learning algorithms.(1,2) Like all fields, neurosurgery has become an increasing focus of research.”
Background
Construction of a predictive model based on MIV-SVR for prognosis and length of stay in patients with traumatic brain injury: Retrospective cohort study
Pan, Y., Fang, C., Zhu, X., & Wan, J. (2023) · Digital Health · doi:10.1177/20552076231217814 · Cited by 6
Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics
Volovăț, S. R., Popa, T. O., Rusu, D. I., Ochiuz, L., Vasincu, D., Agop, M., Buzea, C. G., & Volovăț, C. C. (2024) · Diagnostics · doi:10.3390/diagnostics14182091 · Cited by 6
“However, their widespread adoption faces several challenges [17–21].”
Background
Prediction of Major Adverse Limb Events in Females with Peripheral Artery Disease using Blood-Based Biomarkers and Clinical Features
Li, B., Khan, H., Shaikh, F., Zamzam, A., Abdin, R., & Qadura, M. (2024) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-024-10574-y · Cited by 6
“This algorithm was selected because it is widely used in literature and demonstrates excellent performance for predicting health outcomes [49–51].”
Predicting mortality risk following major lower extremity amputation using machine learning
Li, B., Eisenberg, N., Beaton, D., Lee, D. S., Al‐Omran, L., Wijeysundera, D. N., Hussain, M. A., Rotstein, O. D., de Mestral, C., Mamdani, M., et al. (2025) · Journal of Vascular Surgery · doi:10.1016/j.jvs.2025.03.198 · Cited by 6
Preoperative nonmedical predictors of functional impairment after brain tumor surgery
Schiavolin, S., Mariniello, A., Broggi, M. A., DiMeco, F., Ferroli, P., & Leonardi, M. (2022) · Supportive Care in Cancer · doi:10.1007/s00520-021-06732-6 · Cited by 9
“Age, tumor characteristics, patient’s neurological and functional state are the most investigated predictors of clinical outcome [6, 7].”
Background
Prediction of Peripheral Artery Disease Prognosis Using Clinical and Inflammatory Biomarker Data
Li, B., Shaikh, F., Zamzam, A., Raphael, R., Syed, M. H., Younes, H. K., Abdin, R., & Qadura, M. (2024) · Journal of Inflammation Research · doi:10.2147/jir.s471150 · Cited by 6
“29 32 Using data from the model discovery cohort, the random forest model was trained with ten-fold cross validation to predict PAD prognosis (2-year MALE) using the following input features: 1) clinical characteristics (sex, age, dyslipidemia, hypertension, diabetes, past/current smoking, CHF,…”
Method
Introduction. Predictive analytics in medicine
Asher, A. L., Devin, C. J., Harbaugh, R. E., & Bydon, M. (2018) · Neurosurgical FOCUS · doi:10.3171/2018.8.focus18441 · Cited by 6
Narrative Review of Predictive Analytics of Patient-Reported Outcomes in Adult Spinal Deformity Surgery
Lehner, K., Ehresman, J., Pennington, Z. A., Ahmed, A. K., Lubelski, D., & Sciubba, D. M. (2020) · Global Spine Journal · doi:10.1177/2192568220963060 · Cited by 6
“When a limited data set is used to train an algorithm with a high percentage of the data used for training, it is prone to bias and overfitting.(24) Many of the studies described here benefit from the use of a prospectively collected multicenter database, which should minimize this drawback.”
BackgroundMethod
Machine 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
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
Angioarchitectural features of brain arteriovenous malformation presented with seizures
Yan, L., Tao, W., Zhan, Q., Huang, Z., Chen, F., & Li, S. (2022) · Neurosurgical Review · doi:10.1007/s10143-022-01814-3 · Cited by 6
“showed that ML had a higher average accuracy than LR in predicting neurosurgical outcomes [29].”
Editorial. Prognostication in traumatic brain injury: now what?
Hawryluk, G. W. J. (2024) · Journal of neurosurgery · doi:10.3171/2023.12.jns232644 · Cited by 6
Causal Effect Estimation on Imaging and Clinical Data for Treatment Decision Support of Aneurysmal Subarachnoid Hemorrhage
Ma, W., Chen, C., Gong, Y., Chan, N. Y., Jiang, M., Mak, C. H., Abrigo, J., & Dou, Q. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3390812 · Cited by 5
“As accurate outcome prediction is highly desirable to optimize treatment decision-making for stroke, more and more machine learning-based methods have been proposed to estimate the treatment outcome in clinical practice [31].”
Background
Predicting outcomes following endovascular aortoiliac revascularization using machine learning
Li, B., Aljabri, B. A., Beaton, D., Al‐Omran, L., Hussain, M. A., Lee, D. S., Wijeysundera, D. N., Rotstein, O. D., de Mestral, C., Mamdani, M., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01865-y · Cited by 5
“We selected these models because of their demonstrated ef ﬁ cacy in predicting postoperative outcomes 57 – 59 .”
Big data and artificial intelligence in post-stroke aphasia: A mapping review
Pottinger, G., & Kearns, Á. (2024) · Advances in Communication and Swallowing · doi:10.3233/acs-230005 · Cited by 7
Implementation and evaluation of a system for assessment of the quality of long-term management of patients at a geriatric hospital
Shalom, E., Goldstein, A., Weiss, R., Selivanova, M., Cohen, N. M., & D, Y. S. M. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104686 · Cited by 5
Inflammatory Protein Panel: Exploring Diagnostic Insights for Peripheral Artery Disease Diagnosis in a Cross-Sectional Study
Li, B., Nassereldine, R., Shaikh, F., Younes, H. K., AbuHalimeh, B. J., Zamzam, A., Abdin, R., & Qadura, M. (2024) · Diagnostics · doi:10.3390/diagnostics14171847 · Cited by 6
“Random forest is commonly used in the literature and performs well with healthcare data [24–26].”
Method
Predicting prolonged length of stay following revision total knee arthroplasty: A national database analysis using machine learning models
Mittal, A., Buddhiraju, A., Subih, M. A., Chen, T. L., Shimizu, M. R., Seo, H. H., Rezazadehsaatlou, M., Xiao, P., & Kwon, Y. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105634 · Cited by 5
Prognosis at Your Fingertips: A Machine Learning-Based Web Application for Outcome Prediction in Acute Traumatic Epidural Hematoma
Karabacak, M., & Margetis, K. D. (2023) · Journal of Neurotrauma · doi:10.1089/neu.2023.0122 · Cited by 5
Developing Predictive Models to Anticipate Shunt Complications in 33,248 Pediatric Patients with Shunted Hydrocephalus Utilizing Machine Learning
Shahrestani, S., Shlobin, N. A., Gendreau, J. L., Brown, N. J., Himstead, A. S., Patel, N., Pierzchajlo, N., Chakravarti, S., Lee, D. J., Chiarelli, P. A., et al. (2023) · Pediatric Neurosurgery · doi:10.1159/000531754 · Cited by 5
Association of the classification of intraoperative adverse events (ClassIntra) with complications and neurological outcome after neurosurgical procedures: a prospective cohort study
Drexler, R., Ricklefs, F. L., Pantel, T., Göttsche, J., Nitzschke, R., Zöllner, C., Westphal, M., & Dührsen, L. (2023) · Acta Neurochirurgica · doi:10.1007/s00701-023-05672-w · Cited by 5
“Recent studies focused mainly on the postoperative outcome [4, 9, 33, 37].”
Background
Applications of machine learning for nursing monitoring of electroencephalography
Zabihi, M. R., Rohampour, K., Rashtiani, S., Alizadeh, T., & Akhoondian, M. J. (2023) · Journal of Nursing Reports in Clinical Practice · doi:10.32598/jnrcp.23.49 · Cited by 5

Authors: Senders, J. T., Staples, P. C., Karhade, A. V., Zaki, M. M., Gormley, W. B., Broekman, M. L. D., Smith, T. R., & Arnaout, O.
Affiliations: Brigham and Women's Hospital (US)
Harvard University (US)
Corresponding author: Arnaout, Omar
ORCID: Senders, Joeky T.
Staples, Patrick C.
Karhade, Aditya V.
Zaki, Mark M.
Gormley, William B.
Broekman, Marike L.D.
Smith, Timothy R.
Arnaout, Omar
Venue: World Neurosurgery
Publisher: Elsevier BV
Published: 2018-01
Volume: 109
Pages: 476-486.e1
ISSN: 1878-8750 (ISSN-L)
DOI: 10.1016/j.wneu.2017.09.149
OpenAlex: W2761529114
Cited by: 486
References: 47
Citations by year: 2026: 49 · 2025: 51 · 2024: 63 · 2023: 68 · 2022: 85 · 2021: 67 · 2020: 58 · 2019: 31 · 2018: 13
FWCI: 4.10
Type: journal article
Language: en
Open access: green
scholarlypublications.universiteitleiden.nl
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: World Neurosurgery (publishedVersion)
PubMed (publishedVersion)
Leiden Repository (Leiden University) (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: machine learning, surgical decision-making, epilepsy surgery, brain tumor surgery, spinal lesions, neurovascular disease, movement disorders, traumatic brain injury, hydrocephalus, survival prediction, recurrence prediction, adverse events, prognostic score, clinical decision support
MeSH terms: Machine Learning, Computer Simulation, Decision Support Techniques, Humans, Nervous System Diseases, Prognosis, Outcome Assessment, Health Care, Neurosurgical Procedures

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Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review

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