Oc-008 neural networks predicting hernia recurrence after abdominal wall reconstruction (awr) perform better without preoperative imaging

Abstract Introduction Image-based deep learning models (DLMs) have shown promise in predicting surgical complexity and wound complications for AWR with the potential to augment other clinical information in surgical decision-making. The purpose of this study is to evaluate a preoperative image-based DLM in predicting hernia recurrence. Methods A prospective single-institution database was queried for open AWR patients with preoperative CT imaging. Patient demographics and hernia recurrence were collected. Patient data were batched into training (80%) and test (20%) sets. A convolutional neural network was trained to evaluate image characteristics alone. A feedforward neural network (FNN) was also trained to evaluate clinical data alone. Another FNN was designed to incorporate the outputs from both models. The primary outcome was the ability to accurately predict hernia recurrence, as assessed by the area under the curve (AUC) for each model. Results There were 190 patients in this dataset who underwent AWR. Overall, 28 (14.7%) had a recurrence with a median follow up of 85 [56, 113] months. Preoperative CT imaging alone returned an AUC = 0.500. Clinical data alone performed better with an AUC = 0.667. Incorporating both models resulted in an AUC = 0.604. Conclusions In this study, the DLM with clinical data alone outperformed an image-based DLM in predicting recurrence. Clinical data alone also outperformed the model incorporating both clinical data and preoperative imaging. DLMs have shown significant potential to predict AWR outcomes and may be used in the future to guide care and aid in patient consent for surgery.