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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“Machine learning model assessment was performed through four different methods in concordance with the literature to provide a robust model assessment [34]: (1) discrimination (area under the receiver operating curve [AUC]), (2) calibration (calibration plot—intercept and slope), (3) Brier score, and (4) decision curv…”
Method
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Background
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“Useofarti ﬁ cial intelligence (AI) in healthcare and its utility in medicine, from diagnosis and risk assessment to outcome predictions for a wide variety of illnesses has been extensively described in the literature (Koivu et al., 2018; Senders et al., 2018; Harris et al., 2019).”
Background
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Self-citation
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“13 A similar but broader systematic review of ML-driven models in surgical settings found that these algorithms used for postoperative predictive outcome models and risk stratiﬁca-tion were more accurate than validated prognostic scores and traditional statistics.”
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“This gap in the literature is coherent across a wide range of medical specialties and diseases, including trauma [9], cancer [11], neurosurgery [10], depression [12], acute gastrointestinal bleeding [13], sepsis [14], acute liver failure [15], ischemic stroke [16], thermal injury [17], and…”
Background
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Background
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“Accuracy refers to the proportion of correct predictions among the total number of predictions, and AUC corresponds to the probability that a binary classifier will rank a randomly chosen positive instance higher than a randomly chosen negative instance (Senders et al. 2018).”
Background
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“To code the implementation strategies, the modified Effective Practice and Organisation of Care (EPOC) classification was used 22 ; descriptions of categories can be found in online supplemental appendix 6.”
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Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion
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Ovary Cancer Diagnosing Empowered with Machine Learning
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“For starters, AI is not the same as traditional computer programming [10].”
Background
Deep Natural Language Processing to Identify Symptom Documentation in Clinical Notes for Patients With Heart Failure Undergoing Cardiac Resynchronization Therapy
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Development of machine learning-based preoperative predictive analytics for unruptured intracranial aneurysm surgery: a pilot study
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Machine Learning Algorithms and Quantitative Electroencephalography Predictors for Outcome Prediction in Traumatic Brain Injury: A Systematic Review
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“ML, which is a ﬁeld of computer science, falls into a subdivision of artiﬁcial intelligence (AI) that enables algorithms to learn patterns in large, complex datasets without being explicitly programmed [19], [20].”
BackgroundMethodInfluential
Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery
Mantelakis, A., Assael, Y., Sorooshian, P., & Khajuria, A. (2021) · Plastic & Reconstructive Surgery Global Open · doi:10.1097/gox.0000000000003638 · Cited by 32
Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage
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“Recently, machine learning (ML) has emerged as a powerful tool to develop predictive algorithms from a large amount of data [35].”
Background

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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