Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. Previous decision support systems for appendicitis have focused on clinical, laboratory, scoring, and computed tomography data and have ignored abdominal ultrasound, despite its noninvasive nature and widespread availability. In this work, we present interpretable machine learning models for predicting the diagnosis, management and severity of suspected appendicitis using ultrasound images. Our approach utilizes concept bottleneck models (CBM) that facilitate interpretation and interaction with high-level concepts understandable to clinicians. Furthermore, we extend CBMs to prediction problems with multiple views and incomplete concept sets. Our models were trained on a dataset comprising 579 pediatric patients with 1709 ultrasound images accompanied by clinical and laboratory data. Results show that our proposed method enables clinicians to utilize a human-understandable and intervenable predictive model without compromising performance or requiring time-consuming image annotation when deployed. For predicting the diagnosis, the extended multiview CBM attained an AUROC of 0.80 and an AUPR of 0.92, performing comparably to similar black-box neural networks trained and tested on the same dataset.

翻译：阑尾炎是儿童腹部手术最常见的原因之一。既往针对阑尾炎的决策支持系统多集中于临床、实验室、评分及计算机断层扫描数据，而忽略了腹部超声检查——尽管该技术具有无创性且应用广泛。本研究提出了基于超声图像的可解释机器学习模型，用于预测疑似阑尾炎的诊断、治疗方案及严重程度。我们的方法采用概念瓶颈模型（CBM），该模型能够利用临床医生可理解的高级概念进行解释和交互。此外，我们将CBM扩展至多视角和不完整概念集的预测问题。模型训练数据集包含579例儿科患者的1709张超声图像及其对应的临床与实验室数据。结果表明，所提出的方法使临床医生能够在部署时使用人类可理解且可干预的预测模型，且无需牺牲性能或耗时的图像标注。在诊断预测任务中，扩展后的多视角CBM实现了0.80的AUROC和0.92的AUPR，其性能与基于相同数据集训练和测试的类似黑盒神经网络相当。