基于人工智能的可扩展胸部放射诊断方法的开发和外部验证:一个多国横截面研究
Zeye Liu1,2,3,4,5, Jing Xu6, Chengliang Yin7,8
1Department of Cardiac Surgery, Peking University People's Hospital, Peking University, Xicheng District, Beijing, China.
Research (Washington, D.C.)
|August 7, 2024
概括
使用卷积神经网络 (CNN) 的新人工智能 (AI) 工具显著提高了胸部X射线扫描诊断的准确性和效率. 这种人工智能在26种胸部疾病中表现出高性能,帮助临床医生处理复杂病例.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 胸部X射线图对于诊断胸部疾病至关重要,但由于解释错误和专家可用性有限,它面临着挑战.
- 这些局限性可能导致严重疾病患者的诊断和治疗延迟.
研究的目的:
- 开发一个强大的,多分类的人工智能 (AI) 工具,以提高胸部放射图解读的准确性和效率.
- 创建一个可靠的AI系统,能够区分26种不同的胸部诊断.
主要方法:
- 一个卷积神经网络 (CNN) 为胸部诊断的多重分类而开发.
- 美国有线电视新闻网 (CNN) 模型是在来自13个国际数据集的795,055张胸部X射线照片的大数据集上进行训练和验证的.
主要成果:
- 人工智能模型在26个诊断中实现了0.961的曲线下的高平均面积 (AUC).
- 在COVID-19检测 (AUC 1,000) 中注意到了特殊的性能,在外部验证中显示出强大的概括性 (肺不透明度AUC 0.9634).
- 人工智能表现优于人类医生,提高了各种疾病的诊断准确性,证明了可扩展性和没有性别偏见.
结论:
- 开发的AI算法,作为基于Web的软件提供,大大提高了胸部X射线图的解释.
- 这项研究代表了医学成像技术的重大进步,在临床实践中提供了宝贵的诊断支持.
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