A Deep-Learning Model for Predicting the Efficacy of Non-vascularized Fibular Grafting Using Digital Radiography

Abstract

To develop a fully automated deep-learning (DL) model using digital radiography (DR) with relatively high accuracy for predicting the efficacy of non-vascularized fibular grafting (NVFG) and identifying suitable patients for this procedure. A retrospective analysis was conducted on osteonecrosis of femoral head patients who underwent NVFG between June 2009 and June 2021. All patients underwent standard preoperative anteroposterior (AP) and frog-lateral (FL) DR. Subsequently, the radiographs were pre-processed and labeled based on the follow-up results. The dataset was randomly divided into training and testing datasets. The DL-based prediction model was developed in the training dataset and its diagnostic performance was evaluated using the testing dataset. A total of 339 patients with 432 hips were included in this study, with a hip preservation success rate of 71.52% as of June 2023. The hips were randomly divided into a training dataset (n=324) and a testing dataset (n=108). The ensemble model in predicting the efficacy of NVFG, reaching an accuracy of 78.9%, a precision of 78.7%, a recall of 96.0%, a F1-score of 86.5%, and an area under the curve (AUC) of 0.780. FL views (AUC, 0.71) exhibited better performance compared to AP views (AUC, 0.66). The proposed DL model using DR enables automatic and efficient prediction of NVFG efficacy without additional clinical and financial burden. It can be seamlessly integrated into various clinical scenarios, serving as a practical tool to identify suitable patients for NVFG.