Charting the potential of brain computed tomography deep learning systems
Publication Details
Buchlak, Q. D.,
Milne, M. R.,
Seah, J.,
Johnson, A.,
Samarasinghe, G.,
Hachey, B.,
Esmaili, N.,
Tran, A.,
Leveque, J.,
Farrokhi, F.,
Goldschlager, T.,
Edelstein, S.,
&
Brotchie, P.
(2022).
Charting the potential of brain computed tomography deep learning systems.
Journal of Clinical Neuroscience, 99, 217-223.
Abstract
Brain computed tomography (CTB) scans are widely used to evaluate intracranial pathology. The implementation and adoption of CTB has led to clinical improvements. However, interpretation errors occur and may have substantial morbidity and mortality implications for patients. Deep learning has shown promise for facilitating improved diagnostic accuracy and triage. This research charts the potential of deep learning applied to the analysis of CTB scans. It draws on the experience of practicing clinicians and technologists involved in development and implementation of deep learning-based clinical decision support systems. We consider the past, present and future of the CTB, along with limitations of existing systems as well as untapped beneficial use cases. Implementing deep learning CTB interpretation systems and effectively navigating development and implementation risks can deliver many benefits to clinicians and patients, ultimately improving efficiency and safety in healthcare.
Keywords
brain computed tomography, machine learning, deep learning, patient safety, clinical decision making
Link to Publisher Version (URL)
