Combining optimal biomarkers and deep learning for multimodal presurgical prediction of incisional hernia

Despite its significant clinical and economic burden, the underlying pathophysiology of incisional hernia (IH) remains poorly understood. Imaging-derived features have shown promise in differentiating patients with and without IH. Yet, earlier studies primarily relied on conventional statistical analyses or basic machine learning models applied to relatively small, matched case-control cohorts. In this study, we substantially strengthen and expand upon prior research by analyzing abdominopelvic CT images from a larger cohort of 420 patients (157 IH, 263 non-IH). We introduce a hybrid intelligence (HI) framework that integrates natural intelligence (NI) and artificial intelligence (AI). This approach enables the embedding of hand-crafted features into deep learning (DL) models for prediction, referred to as hybrid deep learning (h-DL). The OBM approach utilizes 34 clinically related parameters alongside 1,065 image-based features. The primary objective of this study is to develop an HI strategy that combines OBM's efficient feature selection with DL's predictive power for improved IH prediction. While the DL model alone outperformed OBM, hybrid models-particularly h-DL-achieved even higher accuracy. This versatile methodology is broadly applicable and can be readily adapted to other biomarker-driven clinical prediction tasks.