A hybrid self-supervised teacher–student model for predicting neurovascular bundle preservation in prostatectomy videos

PURPOSE: Preserving neurovascular bundles (NVB) during robot-assisted radical prostatectomy (RARP) is vital for reducing postoperative complications such as urinary incontinence and erectile dysfunction. Building on our previous work in ensemble-based NVB classification, we propose the hybrid self-supervised teacher-student model (Hybrid T-S model) that leverages multi-task learning to predict NVB preservation in prostatectomy videos. METHODS: Our approach integrates a self-supervised framework (DINO) as an online self-distillation objective on multi-crop views to learn robust embeddings in a limited data setting, rather than as a stand-alone large-scale pretraining. A teacher encoder, which is an exponential moving average (EMA) of the student encoder, and a reconstruction decoder are trained jointly with a classification head in a single end-to-end framework. This model is evaluated on single frames from patients who underwent RARP surgery. RESULTS: Our experimental evaluation shows that the Hybrid T-S model outperforms previous NVB classification methods. This highlights the benefits of integrating self-supervised learning and multi-task objectives in this surgical context. We achieved an average accuracy of 86.55%, precision of 83.93%, recall of 90.73%, F1-score of 87%, and AUROC of 88.35%, based on fivefold cross-validation. CONCLUSION: Incorporating representation learning through self-distillation, classification, and reconstruction provides complementary signals that enhance the prediction of NVB preservation. Our Hybrid T-S model can assist surgeons in real decision-making and improve patient recovery.