背景:尽管抗结核药物通常可以治愈,但结核病(TB)每年杀死约160万人。因此,结核病病例检测和治疗监测,需要一个全面的方法。自动放射分析,结合临床,微生物,和免疫学数据,通过机器学习(ML),可以帮助实现它。
方法:对6只恒河猴进行肺部致病性结核分枝杆菌的实验接种。数据,包括计算机断层扫描(CT),在0、2、4、8、12、16和20周收集。
结果:我们基于ML的CT分析(TB-Net)有效且准确地分析了疾病进展,性能优于标准深度学习模型(LLMOpenAI的CLIPVi4)。基于TB-Net的结果比,并独立确认,两名放射科医生对手动疾病进行盲法评分,并显示出与血液生物标志物的强相关性,TB-病灶体积,和疾病发病过程中的疾病体征。
结论:所提出的方法在早期疾病检测中很有价值,监测治疗效果,和临床决策。
BACKGROUND: Tuberculosis (TB) kills approximately 1.6 million people yearly despite the fact anti-TB drugs are generally curative. Therefore, TB-case detection and monitoring of therapy, need a comprehensive approach. Automated radiological analysis, combined with clinical, microbiological, and immunological data, by machine learning (ML), can help achieve it.
METHODS: Six rhesus macaques were experimentally inoculated with pathogenic Mycobacterium tuberculosis in the lung. Data, including Computed Tomography (CT), were collected at 0, 2, 4, 8, 12, 16, and 20 weeks.
RESULTS: Our ML-based CT analysis (TB-Net) efficiently and accurately analyzed disease progression, performing better than standard deep learning model (LLM OpenAI\'s CLIP Vi4). TB-Net based results were more consistent than, and confirmed independently by, blinded manual disease scoring by two radiologists and exhibited strong correlations with blood biomarkers, TB-lesion volumes, and disease-signs during disease pathogenesis.
CONCLUSIONS: The proposed approach is valuable in early disease detection, monitoring efficacy of therapy, and clinical decision making.