Multimodal model for the diagnosis of biliary atresia based on sonographic images and clinical parameters.

IF 12.4 1区医学 Q1 HEALTH CARE SCIENCES & SERVICES

NPJ Digital Medicine Pub Date : 2025-06-17 DOI:10.1038/s41746-025-01694-z

Wenying Zhou,Run Lin,Yuanhang Zheng,Shan Wang,Bin Xu,Zijian Tang,Ruixuan Wang,Cheng Yu,Hualin Yan,Juxian Liu,Wen Ling,Guangliang Huang,Zongjie Weng,Luyao Zhou

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引用次数: 0

Abstract

It is still challenging to diagnose biliary atresia (BA) in current clinical practice. The study aimed to develop a multimodal model incorporated with uncertainty estimation by integrating sonographic images and clinical information to help diagnose BA. Multiple models were trained on 384 infants and validated externally on 156 infants. The model fused with sonographic images and clinical information yielded best performance, with an area under the curve (AUC) of 0.941 (95% CI: 0.891-0.972) on the external dataset. Moreover, the model based on sonographic video still yielded AUC of 0.930 (0.876-0.966). By excluding 39 cases with high uncertainty (>0.95), accuracy of the model improved from 84.6% to 91.5%. In addition, six radiologists with different experiences showed improved diagnostic performance (mean AUC increase: 0.066) when aided by the model. This fusion model incorporated with uncertainty estimation could potentially help radiologists identify BA more accurately and efficiently in real clinical practice.

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基于超声图像和临床参数诊断胆道闭锁的多模态模型。

胆道闭锁（BA）的诊断在目前的临床实践中仍然具有挑战性。本研究旨在建立一种结合不确定度估计的多模态模型，将超声图像与临床信息相结合，以帮助诊断BA。多个模型对384名婴儿进行了训练，并对156名婴儿进行了外部验证。融合超声图像和临床信息的模型表现最佳，在外部数据集上的曲线下面积（AUC）为0.941 （95% CI: 0.891-0.972）。基于超声视频的模型的AUC仍为0.930（0.876-0.966）。通过排除39例高不确定性（>0.95），模型的准确率从84.6%提高到91.5%。此外，六位具有不同经验的放射科医生在该模型的帮助下表现出更高的诊断性能（平均AUC增加：0.066）。这种融合模型与不确定性估计相结合，可能有助于放射科医生在实际临床实践中更准确、更有效地识别BA。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

NPJ Digital Medicine Multiple-

CiteScore

25.10

自引率

3.30%

发文量

170

审稿时长

15 weeks

期刊介绍： npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.