人工智能用于检测结核病:对全球健康的影响
Eui Jin Hwang1, Won Gi Jeong1, Pierre-Marie David1
1From the Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy, University of Montréal, Montréal, Canada (P.M.D.); OBVIA-Observatoire sur les Impacts Sociétaux de l'IA et du Numérique, Québec, Canada (P.M.D.); and FIND-The Global Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.).
使用深度学习的计算机辅助诊断 (CAD) 系统显示出在胸部放射图上检测结核病的前景,可能有助于全球卫生倡议. 这些系统正在接近专家性能,为资源有限的设置提供解决方案.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 全球卫生与流行病学
背景情况:
- 结核病仍然是一个重大的全球卫生挑战,特别是在发展中国家.
- 胸部放射学在结核病查中的作用自2010年代以来大幅扩大.
- 深度学习推动了计算机辅助诊断 (CAD) 在结核病检测方面取得重大进展.
研究的目的:
- 批判性地评估现有的结核病CAD软件中关于发育过程的报告.
- 探讨在高负担国家扩大CAD解决方案的考虑因素.
- 评估CAD在扩大基于X光的诊断到其他疾病方面的潜力.
主要方法:
- 使用医学成像人工智能检查清单对CAD软件开发报告的评估.
- 探索扩展CAD解决方案的因素,包括验证,经济,政治和道德.
主要成果:
- 基于深度学习的用于肺结核检测的CAD系统已经实现了与人类专家可比的诊断性能.
- 目前的CAD系统为低收入和中等收入国家的人类阅读器短缺提供了潜在的解决方案.
- 该研究批判性地评估了报告标准,并确定了未来开发和实施的关键领域.
结论:
- 结核病的CAD是一个重要的深度学习应用程序,有可能促进全球健康和健康公平.
- 解决制造商独立验证,经济,政治和伦理因素对于成功扩展至关重要.
- 在结核病中CAD的成功可能催化了其他基于X光学诊断的类似进展.
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