Convolutional neural networks using preoperative CT to predict short-term recurrence after incisional hernia repair

Convolutional neural networks (CNNs) are deep neural networks that use convolutional layers to extract spatial features from data. The application of CNNs in assessment of postoperative outcomes of repair of incisional hernias demands further investigation. The aim of this study was to compare the ability of various CNNs to predict recurrence after incisional hernia repair. This was a single-center, retrospective study of patients who had undergone preoperative computed tomography (CT) scans prior to repair of incisional hernias. We trained three CNNs (ResNet-50, ResNet-152, and VGG16) to predict hernia recurrence after surgery. Images were batched into training versus test sets (training: test 4:1). The performance of the CNNs was assessed using conventional receiver operating characteristics curves and the areas under the curves. Gradient-weighted class activation maps were used to identify which regions were actively used in predicting hernia recurrence. The study cohort comprised 528 patients and 44,380 CT images. Among 528 patients (mean age 61.8 years, 62.9% female), 73 (13.8%) experienced recurrence at a mean follow-up of 17.3 months. The area under the curve was 0.840 (0.809–0.871) for ResNet-50; 0.743 (0.706–0.780) for ResNet-152; and 0.693 (0.654–0.732) for VGG16. ResNet-50 predicted incisional hernia recurrence better than ResNet-152 or VGG16 (P < 0.001, respectively). Image-based CNNs using routine, preoperative CT images were successful in predicting incisional hernia recurrence after repair. However, the clinical efficacies of the three CNNs varied. These differences require further clinical investigation.