人类胆管疾病中的某些危及生命的异常，例如胆管癌，如果在早期发现，则是可以治愈的，而超声波已被证明是识别这些异常疾病的有效工具。然而，诊断通常需要有经验的放射科医师的第二个意见，他们通常会被许多案例所压倒。因此，我们提出了一种名为胆管网络（BiTNet）的深度卷积神经网络模型，用于解决当前筛查系统中的问题，并避免传统深度卷积神经网络的过度自信问题。此外，我们提供了一个用于人类胆道的超声图像数据集，并展示了两个人工智能（AI）应用程序：自动预筛和辅助工具。该模型是第一个在实际医疗保健场景中自动从超声图像筛查和诊断上腹部异常的人工智能模型。我们的实验表明，预测概率对两个应用程序都有影响，我们对EfficientNet的修改解决了过度自信问题，从而改善了两个应用程序和医疗保健专业人员的表现。所提出的BiTNet可以将放射科医师的工作量减少35％，同时使假阴性率保持在每455张图像中的1张以下。我们对11名拥有不同经验水平的医疗保健专业人员进行的实验表明，BiTNet提高了所有级别参与者的诊断性能。作为辅助工具的参与者的平均准确度和精确度（分别为0.74和0.61）统计上高于没有辅助工具的参与者（分别为0.50和0.46（p <0.001））。这些实验结果证明了BiTNet在临床设置中的高潜力。版权所有©2023 Elsevier B.V.。

Certain life-threatening abnormalities, such as cholangiocarcinoma, in the human biliary tract are curable if detected at an early stage, and ultrasonography has been proven to be an effective tool for identifying them. However, the diagnosis often requires a second opinion from experienced radiologists, who are usually overwhelmed by many cases. Therefore, we propose a deep convolutional neural network model, named biliary tract network (BiTNet), developed to solve problems in the current screening system and to avoid overconfidence issues of traditional deep convolutional neural networks. Additionally, we present an ultrasound image dataset for the human biliary tract and demonstrate two artificial intelligence (AI) applications: auto-prescreening and assisting tools. The proposed model is the first AI model to automatically screen and diagnose upper-abdominal abnormalities from ultrasound images in real-world healthcare scenarios. Our experiments suggest that prediction probability has an impact on both applications, and our modifications to EfficientNet solve the overconfidence problem, thereby improving the performance of both applications and of healthcare professionals. The proposed BiTNet can reduce the workload of radiologists by 35% while keeping the false negatives to as low as 1 out of every 455 images. Our experiments involving 11 healthcare professionals with four different levels of experience reveal that BiTNet improves the diagnostic performance of participants of all levels. The mean accuracy and precision of the participants with BiTNet as an assisting tool (0.74 and 0.61, respectively) are statistically higher than those of participants without the assisting tool (0.50 and 0.46, respectively (p<0.001)). These experimental results demonstrate the high potential of BiTNet for use in clinical settings.Copyright © 2023 Elsevier B.V. All rights reserved.