可解释的机会性骨质疏松症从胸部X射线查:基础模型的回顾性比较
Jaewon Kim1, Sangmin Kwak2, Hyeokjong Lee1
1Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, 03080, Republic of Korea.
概括
基金会模型可以使用胸部X射线选骨质疏松症. DINOv2表现出高精度和可解释性,强调了可解释AI在医疗应用中的重要性.
科学领域:
- 放射学中的人工智能
- 医学成像分析 医学成像分析
- 骨质疏松症的诊断 骨质疏松症的诊断
背景情况:
- 深度学习模型提供了通过胸部X射线进行机会性骨质疏松症查的潜力.
- 现有的模型经常使用卷积神经网络,解释能力有限.
- 本研究解决了评估基础模型的需要,重点关注可解释性.
研究的目的:
- 引入一个定量框架来评估基础模型的可解释性.
- 系统地比较骨质疏松症查的各种基础模型.
- 在预测性能和模型可解释性之间找到最佳平衡.
主要方法:
- 利用了21,031张来自14502名女性患者的胸部X射线的回顾性数据集.
- 训练了12个基础模型变体来分类骨质疏松症状态.
- 使用AUC,精度,灵敏度,特异性和可解释性指标 (遮蔽和突出性分析) 评估模型性能.
主要成果:
- 使用低级适应的DINOv2获得了最高的性能 (AUC 0.93) 和强大的解释性.
- 该模型专注于临床相关的骨结构,如脊柱和肋骨.
- 自然领域模型的表现并不总是低于医学领域模型的表现,表现并不总是与可解释性相关.
结论:
- 可解释性是选择医疗查中的AI模型的关键标准.
- 开发的框架可以应用于其他医疗AI任务.
- 优先考虑可解释性提高了信任,并促进了人工智能工具的临床部署.
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