Development and validation of a deep learning algorithm for prediction of pediatric recurrent intussusception in ultrasound images and radiographs.

Purposes: To develop a predictive model for recurrent intussusception based on abdominal ultrasound (US) images and abdominal radiographs.

Methods: A total of 3665 cases of intussusception were retrospectively collected from January 2017 to December 2022. The cohort was randomly assigned to training and validation sets at a 6:4 ratio. Two types of images were processed: abdominal grayscale US images and abdominal radiographs. These images served as inputs for the deep learning algorithm and were individually processed by five detection models for training, with each model predicting its respective categories and probabilities. The optimal models were selected individually for decision fusion to obtain the final predicted categories and their probabilities.

Results: With US, the VGG11 model showed the best performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.669 (95% CI: 0.635-0.702). In contrast, with radiographs, the ResNet18 model excelled with an AUC of 0.809 (95% CI: 0.776-0.841). We then employed two fusion methods. In the averaging fusion method, the two models were combined to reach a diagnostic decision. Specifically, a soft voting scheme was used to average the probabilities predicted by each model, resulting in an AUC of 0.877 (95% CI: 0.846-0.908). In the stacking fusion method, a meta-model was built based on the predictions of the two optimal models. This approach notably enhanced the overall predictive performance, with LightGBM emerging as the top performer, achieving an AUC of 0.897 (95% CI: 0.869-0.925). Both fusion methods demonstrated excellent performance.

Conclusions: Deep learning algorithms developed using multimodal medical imaging may help predict recurrent intussusception.

Clinical trial number: Not applicable.