使用临床变量和人工智能支持的放射学评估结核病的严重程度
medRxiv : the preprint server for health sciences
|September 4, 2024
概括
胸部X射线上肺部所涉及的百分比 (PLI) 是不良结核病治疗结果的关键预测指标,其表现优于腔化和其他指标. 这一发现有助于个性化结核病治疗策略,使用人工智能驱动的放射学.
科学领域:
- 肺部医学 肺部医学
- 放射学 放射学是一门学科.
- 医疗保健中的人工智能
背景情况:
- 目前通过胸部X射线 (CXR) 来评估结核病 (TB) 严重程度的方法缺乏标准化的放射学指标来预测治疗结果.
- 个性化结核病治疗,包括缩短的治疗方案,需要根据疾病严重程度对患者群体进行准确的分层.
- 数字CXR和AI为改善结核病诊断,监测和个性化治疗方法提供了潜力.
研究的目的:
- 系统地将基线CXR指标与各种现实世界的临床环境中的结核病治疗结果联系起来.
- 评估参与肺部百分比 (PLI) 作为放射学指标的实用性,用于预测不良结核病治疗结果.
- 从CXR图像开发和验证基于人工智能的方法,用于从CXR图像进行自动化的PLI测量.
主要方法:
- 对10个CXR指标 (包括PLI,腔化,蒂米卡得分) 和2,809名患者的临床变量进行了逻辑回归分析,按耐药性和艾滋病毒状态分层.
- 用5261张CXR DICOM图像进行自动PLI测量的卷积神经网络 (CNN) 的微调.
- 与其他放射学指标和临床模型相比,PLI的预测性能 (AUC) 的比较.
主要成果:
- 在所有子组中,PLI是唯一与不利结果相关的CXR发现 (例如,对无艾滋病毒药物敏感结核病的调整OR为1.11).
- 仅PLI就预测了不利的结果,比Timika得分或空洞化一样有效或更好 (AUC PLI 0.656与Timika 0.655,空洞化0.591).
- 一个PLI值>25%显示出比>50%更好的结果分离. 最好的CNN组合获得了0.850的AUC,预测PLI>25%.
结论:
- 肺部所涉及的百分比 (PLI) 是一个强大的放射性标志物,用于预测不良结核病治疗结果,优于腔化和其他指标.
- 使用CNN的自动PLI测量是准确的,并且可用于现实世界的应用.
- 将PLI与年龄,性别和涂抹等临床因素相结合,可以优化对不良肺结核治疗结果的预测.
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