Deep learning-based recognition model for surgical phases of minimally invasive hysterectomy: A multicentre retrospective study

Objective: To develop and validate a robust deep-learning model capable of fine-grained phase recognition in total hysterectomy, particularly the complex periuterine dissection phase. Design: Multicentre retrospective observational study. Setting: Japan. Sample: Surgical videos (n = 764) from 43 institutions. Methods: We developed a robust and generalisable deep-learning model for surgical phase recognition in total hysterectomy, applicable to laparoscopic and robot-assisted procedures. Overall, 1,591,334 still images were annotated across nine surgical phases. A convolutional neural network (Xception architecture) was trained on 200 cases using four-fold cross-validation, with institutional separation between training and testing sets. Main outcome measures: Model performance was assessed using accuracy, precision, recall, and F1 score. Subgroup analysis and logistic regression evaluated the association between background clinical factors and recognition accuracy. Results: The model achieved an overall phase recognition accuracy of 0.78 (95% CI: 0.74--0.80), with a precision of 0.75 (95% CI: 0.72--0.78) and a recall of 0.76 (95% CI: 0.74--0.78). Performance was consistent across laparoscopic and robot-assisted procedures and across most surgical phases. Accuracy plateaued after training on 120 cases. No clinical factors significantly impacted performance. Trends toward lower accuracy were observed for cases with cervical myoma and pouch of Douglas adhesions. Conclusions: This model demonstrated high accuracy across diverse institutions and patient backgrounds. Its potential applications include surgical education, real-time intraoperative support, and training efficiency enhancement.