Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. With recent advances in machine learning, data-driven decision support could help clinicians diagnose and manage patients while reducing the number of non-critical surgeries. Previous decision support systems for appendicitis focused on clinical, laboratory, scoring and computed tomography data, mainly ignoring abdominal ultrasound, a noninvasive and readily available diagnostic modality. To this end, we developed and validated interpretable machine learning models for predicting the diagnosis, management and severity of suspected appendicitis using ultrasound images. Our models were trained on a dataset comprising 579 pediatric patients with 1709 ultrasound images accompanied by clinical and laboratory data. Our methodological contribution is the generalization of concept bottleneck models to prediction problems with multiple views and incomplete concept sets. Notably, such models lend themselves to interpretation and interaction via high-level concepts understandable to clinicians without sacrificing performance or requiring time-consuming image annotation when deployed.

翻译：阑尾炎是儿童腹部手术最常见的原因之一。随着机器学习的近期发展，数据驱动的决策支持系统可帮助临床医生诊断和管理患者，同时减少非必要手术的数量。此前针对阑尾炎的决策支持系统主要依赖临床、实验室、评分及计算机断层扫描数据，而忽略了腹部超声这一无创且易于获取的诊断工具。为此，我们开发并验证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分型、治疗方案及严重程度。该模型基于包含579名儿童患者（共1709张超声图像及配套临床与实验室数据）的数据集进行训练。我们的方法学贡献在于将概念瓶颈模型泛化至多视角预测问题及不完整概念集合场景。值得注意的是，此类模型可通过临床医生可理解的高层概念实现解释与交互，在部署时无需降低性能或耗费大量时间进行图像标注。