BACKGROUND: Accurate identification of irreducible intussusception during air enema is crucial for optimizing enema strategies. Current methods are limited by subjective interpretation and inconsistent clinical criteria. We developed a deep learning (DL) framework to objectively predict irreducibility using air enema fluoroscopic images. METHODS: In this retrospective study, a hybrid ensemble DL model was developed using fluoroscopic images acquired during air enema, comprising 770 irreducible and 1214 reducible cases. Model performance was evaluated on a real-world test set (46 irreducible vs. 802 reducible cases) and an external test set (9 irreducible vs. 101 reducible cases), with benchmarking against state-of-the-art techniques. The model's performance was further compared with radiologists' interpretations, and its ability to improve diagnostic accuracy was assessed. Performance was evaluated using receiver operating characteristic (ROC) analysis and confusion matrix-derived metrics. RESULTS: The proposed model achieved areas under the ROC curves (AUCs) of 0.89 (95% CI: 0.836-0.944) and 0.883 (95% CI: 0.78-0.968) on the real-world and external test sets, respectively, outperforming comparative methods (AUC ranges: 0.823-0.877 and 0.634-0.826). The model demonstrated superior performance compared with that of the intermediate radiologist (AUC: 0.89 vs. 0.804; P < 0.001) and comparable performance to that of a senior radiologist (AUC: 0.89 vs. 0.842; P = 0.108). When used as an assistive tool, the model significantly improved radiologists' diagnostic performance (all P < 0.01), with AUC improvements of 0.095-0.072, balanced accuracy gains of 8.6-11.7%, and specificity increases of 18.7-22.6%. CONCLUSIONS: The proposed model demonstrated promising diagnostic performance in identifying irreducible intussusception and may serve as an effective decision-support tool to improve radiologists' diagnostic accuracy during air enema procedure.
Zhou et al. (Thu,) studied this question.