Ma Ya, Yang Yuancheng, Du Yuxin, Jin Luyang, Liang Baoyu, Zhang Yuqi, Wang Yedi, Liu Luyu, Zhang Zijian, Jin Zelong, Qiu Zhimin, Ye Mao, Wang Zhengrong, Tong Chao
Department of Ultrasound, Capital Institute of Pediatrics, Beijing, China.
School of Computer Science and Engineering, Beihang University, Beijing, China.
BMC Med. 2025 Feb 27;23(1):127. doi: 10.1186/s12916-025-03962-x.
Early diagnosis of biliary atresia (BA) is crucial for improving patient outcomes, yet remains a significant global challenge. This challenge may be ameliorated through the application of artificial intelligence (AI). Despite the promise of AI in medical diagnostics, its application to multimodal BA data has not yet achieved substantial breakthroughs. This study aims to leverage diverse data sources and formats to develop an intelligent diagnostic system for BA.
We constructed the largest known multimodal BA dataset, comprising ultrasound images, clinical data, and laboratory results. Using this dataset, we developed a novel deep learning model and simplified it using easily obtainable data, eliminating the need for blood samples. The models were externally validated in a prospective study. We compared the performance of our model with human experts of varying expertise levels and evaluated the AI system's potential to enhance its diagnostic accuracy.
The retrospective study included 1579 participants. The multimodal model achieved an AUC of 0.9870 on the internal test set, outperforming human experts. The simplified model yielded an AUC of 0.9799. In the prospective study's external test set of 171 cases, the multimodal model achieved an AUC of 0.9740, comparable to that of a radiologist with over 10 years of experience (AUC = 0.9766). For less experienced radiologists, the AI-assisted diagnostic AUC improved from 0.6667 to 0.9006.
This AI-based screening application effectively facilitates early diagnosis of BA and serves as a valuable reference for addressing common challenges in rare diseases. The model's high accuracy and its ability to enhance the diagnostic performance of human experts underscore its potential for significant clinical impact.
胆道闭锁（BA）的早期诊断对于改善患者预后至关重要，但仍是一项重大的全球挑战。通过应用人工智能（AI），这一挑战可能会得到缓解。尽管AI在医学诊断方面前景广阔，但其在多模态BA数据中的应用尚未取得实质性突破。本研究旨在利用多样的数据来源和格式开发一种用于BA的智能诊断系统。
我们构建了已知最大的多模态BA数据集，包括超声图像、临床数据和实验室结果。利用该数据集，我们开发了一种新型深度学习模型，并使用易于获取的数据对其进行简化，无需血液样本。这些模型在前瞻性研究中进行了外部验证。我们将模型的性能与不同专业水平的人类专家进行了比较，并评估了AI系统提高其诊断准确性的潜力。
回顾性研究纳入了1579名参与者。多模态模型在内部测试集上的AUC为0.9870，优于人类专家。简化模型的AUC为0.9799。在171例病例的前瞻性研究外部测试集中，多模态模型的AUC为0.9740，与经验超过10年的放射科医生相当（AUC = 0.9766）。对于经验较少的放射科医生，AI辅助诊断的AUC从0.6667提高到了0.9006。
这种基于AI的筛查应用有效地促进了BA的早期诊断，并为应对罕见病的常见挑战提供了有价值的参考。该模型的高准确性及其提高人类专家诊断性能的能力凸显了其在临床上产生重大影响的潜力。
