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综合深度学习和临床特征的多模式放射学,用于诊断艾滋病毒/艾滋病患者的多药耐药结核病

Chang Song¹, Aichun Huang², Chunyan Zhao¹

¹Department of Tuberculosis, The Fourth People's Hospital of Nanning, Nanning, Guangxi, China, 530023; Guangxi Medical University, Nanning, Guangxi, China, 530021.

Journal of global antimicrobial resistance

|May 5, 2025

概括

一个新的多式模式模型使用临床,放射学和深度学习数据准确预测艾滋病毒/艾滋病患者的多药耐药结核病 (MDR-TB). 这种综合方法提高了个性化治疗策略的诊断准确性.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
传染性疾病传染性疾病

背景情况:

在艾滋病毒/艾滋病患者中区分多药耐药结核病 (MDR-TB) 对于有效治疗至关重要.
当前的诊断方法在复杂的病例中可能缺乏足够的准确性.
多式联网数据集成为改善诊断性能提供了一个有希望的途径.

研究的目的:

开发和验证艾滋病毒/艾滋病患者中MDR-TB的预测模型.
整合临床,放射学和深度学习功能,以提高诊断准确度.
为个性化精确诊断和治疗提供可靠的工具.

主要方法:

对具有药物敏感性结核病和多抗药性结核病的艾滋病毒/艾滋病患者进行了回顾性队列研究.
开发一个综合临床,放射学和2.5D多实例学习 (MIL) 功能的多式模式.
模型验证使用7:3培训验证分割,后勤回归,Grad-CAM,名谱,Hosmer-Lemeshow测试和决策曲线分析.

主要成果:

综合多式模式模型实现了高诊断性能 (AUC=0.943在培训中,0.899在验证中).
发现的关键预测因素包括血小板计数和C反应性蛋白.
该模型与单个组件模型相比,表现出优越的性能,校准良好,具有临床实用性.

结论:

多式联络综合模型显著提高了MDR-TB在艾滋病毒/艾滋病患者的诊断疗效.
结合临床,放射学和深度学习功能,为精确诊断提供了一种可靠的方法.
该模型支持在这个脆弱人群中针对MDR-TB的个性化治疗策略.

关键词:

深度学习 (Deep Learning) 是一种深度学习.艾滋病毒/艾滋病病毒/艾滋病病毒具有多种药物耐药性的药物.无线电学 (Radiomics) 是一种辐射学.结核病是一种疾病.

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