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<p>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 (&gt;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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月17日

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  • 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

摘要

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.