人工智能驱动的结核患者耐药性分析:一种转移学习方法
Prashant Wakhare1, Shagufta Md S Sheikh1, Pragati Mahale1
1All India Shri Shivaji Memorial Society's, Institute of Information Technology, Pune, Maharashtra, India.
The Indian journal of tuberculosis
|December 16, 2025
概括
人工智能和机器学习可以使用胸部X射线,基因组数据和临床信息快速识别结核病的耐药性. 这种多模式的方法为个性化治疗决策提供了低成本的解决方案,特别是在资源有限的环境中.
科学领域:
- 医学成像分析分析 医学成像分析
- 基因组数据解释的解释
- 医疗保健中的人工智能
背景情况:
- 结核病 (TB) 仍然是全球主要的传染性死亡原因.
- 多药耐药性结核病 (MDR-TB) 和广泛耐药性结核病 (XDR-TB) 的增长率使治疗复杂化.
- 目前用于药物耐药性的诊断方法耗时或在资源有限的地区无法使用.
研究的目的:
- 开发和评估人工智能支持的框架,以快速准确地分析结核病耐药性的情况.
- 整合各种数据来源,包括胸部X射线,基因组突变和临床变量,以提高诊断性能.
主要方法:
- 使用预训练的卷积神经网络 (CNN),如ResNet50,DenseNet121和EfficientNet,用于胸部X射线 (CXR) 功能提取.
- 采用基于变压器的编码器来建模基因组突变和密集层用于临床变量编码.
- 采用基于注意力的架构的融合模式特定嵌入.
主要成果:
- 三模模型实现了91.2%的精度,89.4%的灵敏度和92.7%的精度.
- 曲线下的面积 (AUC) 值在异化,利芬素和乙胺醇耐药性方面超过90%.
- 废弃性研究表明,通过整合多种数据模式,显著提高了性能.
结论:
- 转移学习和多模式数据集成加快和加强结核病耐药性的识别.
- 拟议的框架为个性化结核病治疗选择提供了一个可扩展,具有成本效益和适应性的解决方案.
- 未来的工作重点是扩大数据集和进行临床验证的前性研究.
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