发表机构
University of Stellenbosch; Swiss Federal Institute of Technology (EPFL); National Teaching Hospital for Tuberculosis and Pulmonary Diseases (CNHU-PPC); South African Medical Research Council; Stellenbosch University（斯泰伦博斯大学; 瑞士联邦理工学院（EPFL）; 国家结核病与肺部疾病教学医院（CNHU-PPC）; 南非医学研究理事会; 斯泰伦博斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究评估将常规临床与人口统计学数据与肺部超声图像融合，通过简单平均融合方法将AUROC从0.91提升至0.95，证明该策略可有效改善基于深度学习的结核病自动筛查性能。
AI 中文摘要
我们考虑了将肺部超声图像与常规收集的临床和人口统计学数据相融合，用于基于深度学习的自动化结核病（TB）筛查。此类基于深度学习的结核病筛查工具能够为非洲的医疗保健系统提供有意义的支持，因为该地区疾病负担严重且资源有限。我们以已建立的用于肺部超声图像分类的ResNet基线模型为起点，该模型在受试者工作特征曲线下面积（AUROC）达到0.91 [0.86,0.96]（95%置信区间），在此基础上，我们考虑了三种融合方法来整合临床和人口统计学数据。我们发现，对分别训练的图像分类器和临床数据分类器的输出分数进行简单的基于平均值的融合，其表现始终与更复杂的早期数据融合并训练组合分类器的方法相当或更优。通过这种方式融合图像分类器和临床分类器，得到的分类器总体AUROC为0.95 [0.91,0.99]（在灵敏度为0.93时，特异度为0.76），相比仅使用图像的基线模型，绝对提升了4%。我们还发现，贪心特征选择可用于减少临床和人口统计学输入的数量，而不会牺牲分类性能。最后，当我们区分自我报告的临床和人口统计学数据、需要一些基本测量或计算的数据以及需要床旁（POC）检测的数据时，我们发现本研究中包含的POC检测对分类性能的益处微乎其微。我们得出结论，将常规收集的临床和人口统计学数据纳入其中，是提高基于肺部超声的自动分类性能的一种有前景的方法。
英文摘要
We consider the fusion of lung ultrasound images with routinely-collected clinical and demographic data for the purpose of automated tuberculosis (TB) screening using deep-learning. Such deep-learning based screening tools for TB could meaningfully support the health care system in Africa, where the burden of disease is severe and resources are constrained. Beginning with an established ResNet baseline for classification of lung ultrasound images, which achieves an area under the receiver operating characteristic (AUROC) curve of 0.91 [0.86,0.96] (95% CI), we consider the incorporation of the clinical and demographic data using three fusion approaches. We find that a simple average-based fusion of the output scores of separately-trained image and clinical data classifiers consistently matches or outperforms a more complex approach where the data is fused earlier and a combined classifier is trained. Fusing the image and the clinical classifiers in this way leads to a classifier with an overall AUROC of 0.95 [0.91,0.99] (specificity of 0.76 at sensitivity 0.93) which is an improvement of 4% absolute over the image-only baseline. We also find that greedy feature selection can be used to reduce the number of clinical and demographic inputs without sacrificing classification performance. Finally, when we differentiate between clinical and demographic data that are self-reported, that require some basic measurement or calculation, and that require a point-of-care (POC) test, we find the inclusion of the POC tests included in this study to be of minimal benefit to classification performance. We conclude that the incorporation of routinely-collected clinical and demographic data is a promising way to improve the performance of lung ultrasound based automatic classification.
CommentsAccepted: SATNAC, Drakensberg, South Africa, 2026
