通过在人工智能领域的保护隐私的合作,推进COVID-19诊断
Xiang Bai1,2,3, Hanchen Wang4,3, Liya Ma1,3
1Department of Radiology, Tongji Hospital and Medical College, Huazhong University of Science and Technology, Wuhan, China.
Nature machine intelligence
|January 24, 2024
概括
联合学习使人工智能 (AI) 模型能够训练COVID-19诊断,而无需共享敏感的医疗数据. 这种保护隐私的方法显著提高了多个机构的诊断准确性.
科学领域:
- 数字健康数字健康
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
背景情况:
- 人工智能 (AI) 提供了通过医学成像进行高效的COVID-19诊断的潜力.
- 数据隐私和安全问题阻碍了用于人工智能模型培训的大型代表性数据集的收集.
- 这种局限性对开发用于临床使用的通用AI诊断模型构成重大挑战.
研究的目的:
- 引入使用联合学习框架的统一CT-COVIDAI诊断计划 (UCADI).
- 实现分布式人工智能模型培训和在主机机构独立执行,而无需直接共享数据.
- 为了应对培训通用AI模型用于COVID-19诊断的挑战,同时保持患者隐私.
主要方法:
- 实施了一个联合学习框架,用于分布式训练人工智能诊断模型.
- 收集了来自中国和英国23家医院的3336名患者的9573张胸部计算机断层扫描 (CT) 扫描.
- 评估模型性能与本地模型,专业放射科医生,保留数据和异质数据相比.
主要成果:
- 联合学习模式显著超过了所有本地模式.
- 在中国达到高测试灵敏度 (0.973) 和特异性 (0.951),在英国达到高测试灵敏度 (0.730/0.942).
- 在各种数据集上表现出与专家放射科医生可比的性能和稳定性.
结论:
- 联合学习为医疗诊断中的隐私保护人工智能提供了可行的解决方案.
- 该UCADI倡议成功训练了一种使用CT扫描进行COVID-19诊断的通用AI模型.
- 这种方法提升了联合学习的潜力,以实现安全有效的数字健康应用.
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