使用基于知识蒸的深度学习在胸部X射线中进行低骨质的机会性查,并进行外部验证
Junhyeok Park1, Nha-Young Kim1,2, Hyun-Jin Bae1
1Promedius Inc., Seoul, Republic of Korea.
Archives of osteoporosis
|October 8, 2025
概括
一个新的深度学习模型,OsPenScreen,可以使用胸部X射线 (CXRs) 早期检测低骨质量 (LBM). 这种使用CXRs的LBM早期检测可以帮助预防骨质疏松症并降低医疗保健成本.
科学领域:
- 医疗成像中的人工智能
- 放射学和骨健康 放射学和骨健康
背景情况:
- 低骨质量 (LBM) 是骨质疏松症的未被诊断的前体,增加骨折风险.
- 早期发现LBM对于预防骨质疏松症的进展和相关并发症至关重要.
研究的目的:
- 开发和验证OsPenScreen,这是一个用于机会性LBM检测的深度学习模型.
- 使用常规的胸部X射线 (CXRs) 进行早期LBM识别,减少医疗保健负担.
主要方法:
- 一个卷积神经网络 (CNN) 模型,OsPenScreen,在77,812个CXR和DXA数据集上进行了训练.
- 在四个独立的数据集 (5,935张图像) 中使用了知识蒸和验证.
- 使用AUC,精度,灵敏度和特异性评估模型性能;Grad-CAM可视化决策.
主要成果:
- 在外部测试数据集上,OsPenScreen的AUC达到0.95 (95%CI:0.94-0.97).
- 在性别和年龄分组中观察到一致的表现.
- 该模型准确地识别了减少的骨质量,注意力集中在相关的骨区域.
结论:
- OsPenScreen提供了一个可扩展的,有效的工具,用于通过常规CXRs进行机会性LBM查.
- 该模型在各种数据集中的强大性能支持其早期检测潜力.
- 早期发现LBM可以预防骨质疏松症的进展,降低医疗保健成本.
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