深度学习用于分类与特异性炎症性肌肉病相关的间歇性肺病的成像模式
Jingping Zhang1, Liyu He1, Ying Wei2
1Department of Medical Imaging, the First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi, P.R. China.
一个新的深度学习模型有效地对异常性炎症性肌肉病 (IIM-ILD) 中的间歇性肺病的成像模式进行分类. 这种人工智能工具有望在临床实践中改善放射性诊断.
科学领域:
- 放射学
- 人工智能
- 肺病学
背景情况:
- 基于成像模式的诊断和分类具有挑战性.
- 需要专门的医生专业知识,强调需要先进的诊断工具.
研究的目的:
- 开发和验证深度学习 (DL) 模型来对IIM-ILD成像模式进行分类.
- 提高诊断准确性和确定IIM-ILD亚型的效率.
主要方法:
- 对629名IIM-ILD患者的回顾性分析,分为培训,内部测试和时间外部验证组.
- 使用高分辨率计算机断层扫描 (HRCT) 图像开发DL模型.
- 整合类激活映射和标签平滑以提高可解释性和性能.
主要成果:
- 在内部测试组中,DL模型的平均AUC为0.885和F1得分为0.706.
- 在时间外部验证组中,该模型显示AUC为0. 835和F1得分为0. 727.
- 该模型在分类各种IIM-ILD成像模式方面表现出强的表现.
结论:
- 开发的DL模型有效地分类了与IIM-ILD相关的多个成像模式.
- 这种人工智能系统在临床环境中具有价值的放射性诊断支持工具的潜力.
- 进一步验证可能会改善IIM-ILD患者的治疗.
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