Cited by 486Machine Learning in HealthcareArtificial Intelligence in HealthcareArtificial Intelligence in Healthcare and Education

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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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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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