The DOI matches its Crossref record; 66 of the 68 references it cites were found in a registry or an index.
An introduction and overview of machine learning in neurosurgical care
Open accessAbstract
Background Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the considerable potential of ML has yet to find its way into mainstream medical research and day-to-day clinical care. The complex diagnostic and therapeutic modalities used in neurosurgery provide a vast amount of data that is ideally suited for ML models. This systematic review explores ML's potential to assist and improve neurosurgical care.Method A systematic literature search was performed in the PubMed and Embase databases to identify all potentially relevant studies up to January 1, 2017. All studies were included that evaluated ML models assisting neurosurgical treatment.Results Of the 6,402 citations identified, 221 studies were selected after subsequent title/abstract and full-text screening. In these studies, ML was used to assist surgical treatment of patients with epilepsy, brain tumors, spinal lesions, neurovascular pathology, Parkinson's disease, traumatic brain injury, and hydrocephalus. Across multiple paradigms, ML was found to be a valuable tool for presurgical planning, intraoperative guidance, neurophysiological monitoring, and neurosurgical outcome prediction.Conclusions ML has started to find applications aimed at improving neurosurgical care by increasing the efficiency and precision of perioperative decision-making. A thorough validation of specific ML models is essential before implementation in clinical neurosurgical care. To bridge the gap between research and clinical care, practical and ethical issues should be considered parallel to the development of these techniques.
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“Data-induced bias refers to the bias when the training data is not representative or insufficient that affects the algorithm model”
From Ethics and governance of trustworthy medical artificial intelligence (2023) · cites this paper as [28]
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Cites
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Machine learning and neurosurgical outcome prediction: a systematic review
Senders, J. T., Staples, P. C., Karhade, A. V., Zaki, M. M., Gormley, W. B., Broekman, M. L. D., Smith, T. R., & Arnaout, O. (2018) · World Neurosurgery · doi:10.1016/j.wneu.2017.09.149 · Cited by 486
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Cited by
Sorted by citations
Speech Recognition Using Deep Neural Networks: A Systematic Review
Nassif, A. B., Shahin, I. M. A., Attili, I. B., Azzeh, M., & Shaalan, K. F. (2019) · IEEE Access · doi:10.1109/access.2019.2896880 · Cited by 1,215
Ethics and governance of trustworthy medical artificial intelligence
Zhang, J., & Zhang, Z.-M. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02103-9 · Cited by 507
“Data-induced bias refers to the bias when the training data is not representative or insufficient that affects the algorithm model [27, 28].”
BackgroundArtificial intelligence for good health: a scoping review of the ethics literature.
Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · BMC Medical Ethics · doi:10.1186/s12910-021-00577-8 · Cited by 435
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Buchlak, 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
Metadata
- Authors
- Senders, J. T., Zaki, M. M., Karhade, A. V., Chang, B., Gormley, W. B., Broekman, M. L., Smith, T. R., & Arnaout, O.
- Affiliations
- Brigham and Women's Hospital (US)
- Corresponding author
- Arnaout, Omar
- ORCID
- Venue
- Acta Neurochirurgica
- Published
- 2017-11-13
- Volume
- 160
- Issue
- 1
- Pages
- 29-38
- ISSN
- 0001-6268 (ISSN-L)
- 0942-0940
- OpenAlex
- W2768899399
- Cited by
- 178
- References
- 68
- Citations by year
- 2026: 10 · 2025: 22 · 2024: 9 · 2023: 28 · 2022: 29 · 2021: 33 · 2020: 22 · 2019: 15 · 2018: 10
- FWCI
- 0.35
- Type
- journal article
- Language
- en
- Open access
- Locations
- Acta Neurochirurgica (publishedVersion)
- PubMed (publishedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- machine learning, neurosurgery, epilepsy, brain tumors, spinal lesions, neurovascular pathology, Parkinson's disease, traumatic brain injury, hydrocephalus, presurgical planning, intraoperative guidance, neuromonitoring, clinical implementation, model validation, AI ethics
- MeSH terms
- Machine Learning, Brain, Decision Making, Humans, Prognosis, Spinal Cord, Neurosurgical Procedures
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