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商业上可用的AI算法提高了放射科医生在X射线上检测手腕和手部骨折的灵敏度,与基于CT的地面真相相比
Thibaut Jacques1,2, Nicolas Cardot3, Jeanne Ventre4
1Department of Musculoskeletal Radiology, Lille University Hospital, Rue du Professeur Emile Laine, 59000, Lille, France. thib.jacques@gmail.com.
European radiology
|November 3, 2023
概括
人工智能 (AI) 提高了放射科医生在X射线上检测手腕和手部骨折的能力. 使用CT扫描作为基本事实揭示了AI性能更接近临床现实,尽管比之前报道的要差.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 基于X射线的骨折检测算法在临床实践中越来越多地使用.
- 仅在X射线数据上评估这些算法可能会导致有偏见的性能评估.
- 计算机断层扫描 (CT) 为骨折识别提供了更明确的基本真相.
研究的目的:
- 评估商业上可用的AI算法对放射科医生在检测手腕和手部骨折方面的表现的影响.
- 将放射科医生的表现与人工智能辅助和无人工智能辅助进行比较,使用CT作为基准真相.
- 评估基于CT的地面真相对AI算法的独立性能.
主要方法:
- 追溯分析296个创伤后手和手腕病例,包括CT和X射线.
- 根据CT发现的骨折,X线图被标记为骨折.
- 23名放射科医生审查了带有和没有人工智能协助的X射线,对CT结果视而不见.
主要成果:
- 人工智能显著提高了放射科医生的灵敏度 (0.658到0.703) 和负预测值 (0.585到0.618).
- 放射科医生的特异性和积极的预测值不受人工智能协助的影响.
- 独立AI的性能 (Se: 0.771,Sp: 0.898,NPV: 0.684,PPV: 0.915,AUROC: 0.764) 比之前报告的要低.
结论:
- 人工智能提高了放射科医生的精度,可以在X光照上检测手腕和手部骨折,特别是提高了灵敏度和NPV.
- 使用CT作为基本事实提供了对AI性能更现实的评估,这表明先前研究中的潜在高估.
- 这些发现突出了强大的基础真相在AI算法评估临床应用中的重要性.
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