Joeky T Senders1,2, Omar Arnaout2,3, Aditya V Karhade2, Hormuzdiyar H Dasenbrock2, William B Gormley2, Marike L Broekman1,2, Timothy R Smith2. 1. Department of Neurosurgery, University Medical Center, Utrecht, the Netherlands. 2. Cushing Neurosurgery Outcomes Cen-ter, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts. 3. Department of Neurological Surgery, Northwestern University School of Medicine, Chicago, Illinois.
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.
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.
Authors: Miguel Hueso; Alfredo Vellido; Nuria Montero; Carlo Barbieri; Rosa Ramos; Manuel Angoso; Josep Maria Cruzado; Anders Jonsson Journal: Kidney Dis (Basel) Date: 2018-01-25
Authors: Andrew T Hale; David P Stonko; Jaims Lim; Oscar D Guillamondegui; Chevis N Shannon; Mayur B Patel Journal: J Neurosurg Pediatr Date: 2018-11-02 Impact factor: 2.713
Authors: Olivier Q Groot; Michiel E R Bongers; Paul T Ogink; Joeky T Senders; Aditya V Karhade; Jos A M Bramer; Jorrit-Jan Verlaan; Joseph H Schwab Journal: Clin Orthop Relat Res Date: 2020-12 Impact factor: 4.755