Buchlak Quinlan D, Milne Michael R, Seah Jarrel, Johnson Andrew, Samarasinghe Gihan, Hachey Ben, Esmaili Nazanin, Tran Aengus, Leveque Jean-Christophe, Farrokhi Farrokh, Goldschlager Tony, Edelstein Simon, Brotchie Peter
Annalise.ai, Sydney, NSW, Australia; School of Medicine, University of Notre Dame Australia, Sydney, NSW, Australia; Department of Neurosurgery, Monash Health, Melbourne, VIC, Australia.
Annalise.ai, Sydney, NSW, Australia.
J Clin Neurosci. 2022 May;99:217-223. doi: 10.1016/j.jocn.2022.03.014. Epub 2022 Mar 12.
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.
脑部计算机断层扫描（CTB）广泛用于评估颅内病变。CTB的应用和采用带来了临床改善。然而，解读错误时有发生，可能对患者的发病率和死亡率产生重大影响。深度学习已显示出有助于提高诊断准确性和分诊的潜力。本研究描绘了深度学习应用于CTB扫描分析的潜力。它借鉴了参与基于深度学习的临床决策支持系统开发和实施的执业临床医生和技术专家的经验。我们考虑了CTB的过去、现在和未来，以及现有系统的局限性和未开发的有益用例。实施深度学习CTB解读系统并有效应对开发和实施风险可为临床医生和患者带来诸多益处，最终提高医疗保健的效率和安全性。
