一个基于深度学习的算法,用于在胸部X-ray中检测肺结核
Chiu-Fan Chen1,2,3, Chun-Hsiang Hsu1, You-Cheng Jiang1
1Division of Chest Medicine, Department of Internal Medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, R.O.C.
Scientific reports
|June 28, 2024
概括
这项研究使用谷歌可教机器开发了一种AI算法,用于在胸部X射线 (CXR) 上检测结核病 (TB). 人工智能表现出高准确度,与医生可比,有助于结核病诊断.
科学领域:
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
- 深度学习用于诊断.
背景情况:
- 使用胸部放射 (CXR) 诊断结核病 (TB) 是具有挑战性的,因为可变的模式模仿其他条件.
- 准确有效的诊断工具对于及时发现和管理结核病至关重要.
研究的目的:
- 评估与谷歌可教机器开发的深度神经网络 (DNN) 算法的有效性,用于从CXRs预测结核病概率.
- 将AI算法的性能与人类专家读者和放射学报告进行比较.
主要方法:
- 基于DNN的图像分类工具使用348TBCXR和3806个正常CXR的数据集进行训练.
- 外部验证对250名CXR进行了验证,其性能与五名肺病学家相比较.
- 对算法的准确性进行了评估,以检测结核病并将其与其他异常CXR区分开来.
主要成果:
- 在外部验证数据集中,AI算法在0.951和0.975的曲线 (AUC) 下实现了高面积.
- 人工智能的表现与经验丰富的肺病学家 (AUC 0.936-0.995) 的表现相当.
- 与人工智能和人类读者相结合,与单独使用人工智能相比,改善了诊断准确性 (AUC 0.862-0.885).
结论:
- 使用谷歌可教机器开发的TB CXR人工智能算法对结核病检测非常有效.
- 人工智能显示的准确性接近经验丰富的临床医生,可以成为基于CXR的结核病诊断的宝贵工具.
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