Predictive model integrating deep learning and clinical features based on ultrasound imaging data for surgical intervention in intussusception in children younger than 8 months

OBJECTIVES: The objective of this study was to identify risk factors for enema reduction failure and to establish a combined model that integrates deep learning (DL) features and clinical features for predicting surgical intervention in intussusception in children younger than 8 months of age. DESIGN: A retrospective study with a prospective validation cohort of intussusception. SETTING AND DATA: The retrospective data were collected from two hospitals in south east China between January 2017 and December 2022. The prospective data were collected between January 2023 and July 2024. PARTICIPANTS: A total of 415 intussusception cases in patients younger than 8 months were included in the study. METHODS: 280 cases collected from Centre 1 were randomly divided into two groups at a 7:3 ratio: the training cohort (n=196) and the internal validation cohort (n=84). 85 cases collected from Centre 2 were designed as external validation cohort. Pretrained DL networks were used to extract deep transfer learning features, with least absolute shrinkage and selection operator regression selecting the non-zero coefficient features. The clinical features were screened by univariate and multivariate logistic regression analyses. We constructed a combined model that integrated the selected two types of features, along with individual clinical and DL models for comparison. Additionally, the combined model was validated in a prospective cohort (n=50) collected from Centre 1. RESULTS: In the internal and external validation cohorts, the combined model (area under curve (AUC): 0.911 and 0.871, respectively) demonstrated better performance for predicting surgical intervention in intussusception in children younger than 8 months of age than the clinical model (AUC: 0.776 and 0.740, respectively) and the DL model (AUC: 0.828 and 0.793, respectively). In the prospective validation cohort, the combined model also demonstrated impressive performance with an AUC of 0.890. CONCLUSION: The combined model, integrating DL and clinical features, demonstrated stable predictive accuracy, suggesting its potential for improving clinical therapeutic strategies for intussusception.