The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review
Open accessAbstract
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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“ML tools for optimizing care outcomes have been used in various settings, including critical care for optimizing sepsis management (27), management of chronic conditions (28), and optimizing surgical outcomes (29).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [29]
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Citations
Cites
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
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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
Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466
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
Probabilistic machine learning and artificial intelligence
Ghahramani, Z. (2015) · Nature · doi:10.1038/nature14541 · Cited by 1,995
Applications of Machine Learning in Cancer Prediction and Prognosis
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Applications of machine learning in cancer prediction and prognosis.
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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 271
Self-citationMachine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective
Kang, J., Schwartz, R. S., Flíckinger, J. C., & Beriwal, S. (2015) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2015.07.2286 · Cited by 227
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Sorted by citations
Logistic regression was as good as machine learning for predicting major chronic diseases
Nusinovici, S., Tham, Y. C., Yan, M. Y. C., Ting, D. S. W., Li, J., Sabanayagam, C., Wong, T. Y., & Cheng, C. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.03.002 · Cited by 597
Accessing Artificial Intelligence for Clinical Decision-Making
Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.645232 · Cited by 326
“ML tools for optimizing care outcomes have been used in various settings, including critical care for optimizing sepsis management (27), management of chronic conditions (28), and optimizing surgical outcomes (29).”
MethodBuchlak, 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 234
“These statistics allow the comparison of the performance of different forms of supervised learning models and allow the researcher to determine the applicability of the model to clinical practice [36, 98].”
BackgroundThe 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
Wang, W., Kiik, M., Peek, N. B., Ćurčin, V., Marshall, I. J., Rudd, A. G., Wang, Y., Douiri, A., Wolfe, C. D. A., & Bray, B. (2020) · PLoS ONE · doi:10.1371/journal.pone.0234722 · Cited by 205
“We combined published PubMed search filters for stroke [8], ML [9], and prediction models [10].”
Method
Metadata
- 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
- Venue
- World Neurosurgery
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 109
- Pages
- 476-486.e1
- ISSN
- 1878-8750 (ISSN-L)
- 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
- 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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