Charting the potential of brain computed tomography deep learning systems

Quinlan D. Buchlak, Michael R. Milne, Jarrel Seah, Andrew Johnson, Gihan Samarasinghe, Ben Hachey, Nazanin Esmaili, Aengus Tran, Jean Christophe Leveque, Farrokh Farrokhi, Tony Goldschlager, Simon Edelstein, Peter Brotchie

Research output: Contribution to journalReview ArticleResearchpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)217-223
Number of pages7
JournalJournal of Clinical Neuroscience
Volume99
DOIs
Publication statusPublished - May 2022

Keywords

  • Brain computed tomography
  • Clinical decision making
  • Deep learning
  • Machine learning
  • Patient safety

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