评估人工智能 (AI) 算法的性能,以检测胸部病理在胸部X射线图上
Hubert Bettinger1, Gregory Lenczner2, Jean Guigui2
1H-CAB, 47 Rue du Rocher, 75008 Paris, France.
Diagnostics (Basel, Switzerland)
|June 19, 2024
概括
一个深度学习的人工智能工具显著提高了放射科医生在诊断胸部X射线图 (CXRs) 上胸部异常的性能. 人工智能辅助阅读提高了诊断准确度,减少了阅读时间,展示了其在临床实践中的潜力.
科学领域:
- 放射学 放射学是指放射学
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 在胸部放射图 (CXR) 上确切诊断胸部异常对于患者的结果至关重要.
- 基于深度学习的人工智能 (AI) 工具正在出现,以协助医学图像解释.
- 评估人工智能工具的性能及其对临床医生的表现的影响对于临床整合至关重要.
研究的目的:
- 评估使用深度学习人工智能工具 (Rayvolve) 对CXRs胸部异常的读者的诊断性能.
- 评估Rayvolve AI工具在检测胸部病理方面的独立性能.
- 确定人工智能辅助对读者诊断准确度和阅读时间的影响.
主要方法:
- 一个回顾性,多中心研究涉及两个阶段.
- 第一个阶段:9名读者通过人工智能辅助和无人工智能辅助审查了900个CXR;通过放射学家的共识确定了基本事实.
- 第二阶段:Rayvolve的独立性能在1500个CXR中进行了评估.
主要成果:
- 人工智能辅助阅读显著增加了曲线下的面积 (AUC) 15.94% (0.88对0.759).
- 在AI的帮助下,阅读时间减少了35.81%.
- 在人工智能协助下,灵敏度和特异性显著提高 (分别增加11.44%和2.95%).
- 独立人工智能性能:灵敏度为0.964,特异性为0.844,PPV为0.757,NPV为0.9798.
结论:
- 深度学习的人工智能工具 (Rayvolve) 在诊断CXRs胸部异常时显著提高了读者性能.
- 人工智能辅助可以提高诊断准确度 (灵敏度和特异性),提高阅读效率.
- 雷沃尔夫AI工具在检测胸部病理方面表现出强大的独立性能.
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