基于非对比计算断层扫描 (NCCT) 的慢性血栓栓塞性肺高血压 (CTEPH) 自动诊断使用级联网络与多个实例学习
Mayang Zhao1, Liming Song1, Jiarui Zhu1
1Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China, People's Republic of China.
Physics in medicine and biology
|August 27, 2024
概括
一个新的AI模型使用非对比CT扫描自动诊断慢性血栓栓塞性肺高血压 (CTEPH). 这种方法改善了早期检测和患者的结果,而不需要额外的扫描或注释.
科学领域:
- 医疗成像中的人工智能
- 放射学和诊断成像 放射学和诊断成像
- 心血管和肺部疾病.
背景情况:
- 慢性血栓栓塞性肺高血压 (CTEPH) 诊断是具有挑战性的,因为微妙的症状和复杂的成像要求.
- 目前对CTEPH的诊断方法可能是侵入性的或需要对比剂,增加患者的负担和成本.
- 需要自动诊断工具来提高CTEPH检测的效率和准确性.
研究的目的:
- 开发一种使用非对比计算断层扫描 (NCCT) 扫描的CTEPH自动诊断方法.
- 为了实现准确的CTEPH诊断,而不需要精确的病变注释.
- 提高CTEPH检测的及时性和准确性,以获得更好的患者结果.
主要方法:
- 开发了一个具有多个实例学习 (CNMIL) 框架的新级联网络 (CN),集成了两个Resnet-18 CNN.
- 多重实例学习 (MIL) 将每个3DCT案例视为一个"袋子"的切片,使用注意力来识别诊断相关的区域.
- 在300名受试者的NCCT扫描上训练和评估了CNMIL框架 (132名正常人,168名肺部疾病包括88名CTEPH).
主要成果:
- 在CTEPH检测中,CNMIL框架实现了高诊断性能,AUC为0.807,准确度为0.833,灵敏度为0.795,特异性为0.849.
- 废弃性研究显示,MIL和CN整合显著提高了性能,达到0.978的AUC和正常分类的完美灵敏度 (1000).
- 集成的CNMIL模型表现优于其他3D网络架构,在比较分析中达到0.8419的最高AUC.
结论:
- CNMIL网络提供一种非侵入性,无注释的CTEPH诊断方法,仅基于NCCT扫描.
- 这种自动化方法有可能显著改善早期和准确的CTEPH检测.
- 开发的方法可以改善患者管理和CTEPH患者的临床结果.
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