从越南使用深度学习对心肺疾病的早期检测进行胸部放射图的异常分类
Chiharu Kai1, Satoshi Kasai1,2, Rei Teramoto3
1Department of Intelligent Information Engineering, Research Promotion Unit, School of Medical Sciences, Fujita Health University, Toyoake-City, Aichi, Japan.
Frontiers in radiology
|December 8, 2025
概括
一个人工智能 (AI) 模型是使用越南胸部放射数据来检测心肺疾病的. 人工智能系统实现了高精度,帮助越南早期发现疾病.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 越南面临着传染性和非传染性疾病的高负担.
- 胸部放射学对于早期发现心肺疾病至关重要.
- 现有的用于胸部放射分析的AI模型缺乏使用越南数据的研究.
研究的目的:
- 开发一个人工智能 (AI) 模型来分类正常和异常的胸部X射线图.
- 为了利用越南胸部X-ray数据集用于AI模型开发.
- 改善越南心肺疾病的早期检测.
主要方法:
- 对来自越南机构的12827张正常和4644张异常的胸部放射图进行了回顾性分析.
- 使用主要组件分析,视觉变压器和EfficientnetV2.2进行特征提取.
- 使用光梯度增强机器进行二进制分类,并进行5倍交叉验证.
主要成果:
- 人工智能模型的F1得分为0.668,灵敏度为0.596,特异性为0.931,精度为0.842,AUC为0.897.
- 在包括传染病,非传染病和紧急病例在内的子组评估中观察到高准确度.
- 开发的AI系统证明了对分类胸部放射图的临床实用性.
结论:
- 通过使用越南数据,成功开发了一种有效的AI模型来分类正常和异常的胸部X射线图.
- 人工智能系统显示出高临床准确性,支持早期诊断心肺疾病.
- 这项研究解决了针对越南人口和疾病负担量身定制的AI驱动诊断工具的急需问题.
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