Predicting rare outcomes in abdominal wall reconstruction using image-based deep learning models

Abstract

Background: Deep learning models with imbalanced data sets are a challenge in the fields of artificial intelligence and surgery. The aim of this study was to develop and compare deep learning models that predict rare but devastating postoperative complications after abdominal wall reconstruction.

Methods: A prospectively maintained institutional database was used to identify abdominal wall reconstruction patients with preoperative computed tomography scans. Conventional deep learning models were developed using an 8-layer convolutional neural network and a 2-class training system (ie, learns negative and positive outcomes). Conventional deep learning models were compared to deep learning models that were developed using a generative adversarial network anomaly framework, which uses image augmentation and anomaly detection. The primary outcomes were receiver operating characteristic values for predicting mesh infection and pulmonary failure.

Results: Computed tomography scans from 510 patients were used with a total of 10,004 images. Mesh infection and pulmonary failure occurred in 3.7% and 5.6% of patients, respectively. The conventional deep learning models were less effective than generative adversarial network anomaly for predicting mesh infection (receiver operating characteristic 0.61 vs 0.73, P < .01) and pulmonary failure (receiver operating characteristic 0.59 vs 0.70, P < .01). Although the conventional deep learning models had higher accuracies/specificities for predicting mesh infection (0.93 vs 0.78, P < .01/.96 vs .78, P < .01) and pulmonary failure (0.88 vs 0.68, P < .01/.92 vs .67, P < .01), they were substantially compromised by decreased model sensitivity (0.25 vs 0.68, P < .01/.27 vs .73, P < .01).

Conclusion: Compared to conventional deep learning models, generative adversarial network anomaly deep learning models showed improved performance on imbalanced data sets, predominantly by increasing model sensitivity. Understanding patients who are at risk for rare but devastating postoperative complications can improve risk stratification, resource utilization, and the consent process.