Natural and Artificial Intelligence in Neurosurgery: A Systematic Review

Neurosurgery - Tập 83 Số 2 - Trang 181-192 - 2018
Joeky T. Senders1,2,3, Omar Arnaout1,2,3, Aditya V. Karhade1,2,3, Hormuzdiyar H. Dasenbrock1,2,3, William B. Gormley1,2,3, Marike L. D. Broekman1,2,3, Timothy R. Smith1,2,3
1Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
2Department of Neurological Surgery, Northwestern University School of Medicine, Chicago, Illinois
3Department of Neurosurgery, University Medical Center, Utrecht, The Netherlands

Tóm tắt

Abstract BACKGROUND Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed. OBJECTIVE To summarize neurosurgical applications of ML where it has been compared to clinical expertise, here referred to as “natural intelligence.” METHODS A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature. RESULTS Twenty-three studies were identified that used ML algorithms for diagnosis, presurgical planning, or outcome prediction in neurosurgical patients. Compared to clinical experts, ML models demonstrated a median absolute improvement in accuracy and area under the receiver operating curve of 13% (interquartile range 4-21%) and 0.14 (interquartile range 0.07-0.21), respectively. In 29 (58%) of the 50 outcome measures for which a P-value was provided or calculated, ML models outperformed clinical experts (P < .05). In 18 of 50 (36%), no difference was seen between ML and expert performance (P > .05), while in 3 of 50 (6%) clinical experts outperformed ML models (P < .05). All 4 studies that compared clinicians assisted by ML models vs clinicians alone demonstrated a better performance in the first group. CONCLUSION We conclude that ML models have the potential to augment the decision-making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting. Shifting from the preconceptions of a human-vs-machine to a human-and-machine paradigm could be essential to overcome these hurdles.

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Neurosurgery

Tập 83 Số 2

181-192

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