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Purpose <p>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 <i>hybrid self-supervised teacher–student model</i> (Hybrid T–S model) that leverages <i>multi-task learning</i> to predict NVB preservation in prostatectomy videos.</p> Methods <p>Our approach integrates a <i>self-supervised framework (DINO)</i> 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 <i>teacher encoder</i>, which is an exponential moving average (EMA) of the <i>student encoder</i>, and a <i>reconstruction decoder</i> are trained jointly with a <i>classification head</i> in a single end-to-end framework. This model is evaluated on single frames from patients who underwent RARP surgery.</p> Results <p>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.</p> Conclusion <p>Incorporating representation learning through self-distillation, classification, and reconstruction provides complementary signals that enhance the prediction of NVB preservation. Our <i>Hybrid T–S model</i> can assist surgeons in real decision-making and improve patient recovery.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月12日

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  • Diego Andrés del Aguila Moraga,
  • Huitao Wang,
  • Satoshi Ando,
  • Hayato Hoshina,
  • Hiroshi Kawahira,
  • Yukihiro Nomura,
  • Toshiya Nakaguchi

摘要

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.