Deep learning for intraoperative recognition of critical structures in total hysterectomy

This study aimed to develop and evaluate a deep learning-based surgical navigation system capable of recognizing the ureter, uterine artery, and bladder-uterine dissection plane during minimally invasive gynecologic surgery.

An artificial intelligence (AI) model was developed at the University of Tokyo Hospital using videos of prior surgeries. Surgical videos of 27 laparoscopic or robot-assisted total hysterectomies were used to create training and validation datasets, with an additional set of cases serving as an independent test set. Key frames were manually annotated to train segmentation models for the ureter and uterine artery. A separate model visualized loose connective tissue fibers (LCTF) to aid in recognizing the bladder-uterine peritoneal dissection plane. Quantitative performance was assessed using standard segmentation metrics, and a qualitative evaluation was conducted by nine gynecologic surgeons using predefined scoring criteria.

The segmentation models achieved moderate quantitative performance, with Dice similarity coefficients of approximately 0.51 for the ureter and 0.45 for the uterine artery. In contrast, qualitative evaluation demonstrated favorable clinical interpretability. The mean recognition scores assigned by nine expert surgeons were 4.12 for the ureter and 3.45 for the uterine artery on a five-point scale, indicating that most structures were recognized clearly with only minor misrecognition. For bladder dissection, visualization of connective tissue fibers enabled identification of the correct dissection plane in the majority of evaluated frames; more than 70-80% of connective tissue was recognizable in most frames, and substantial misrecognition was uncommon.

This study demonstrates that a deep learning-based system can recognize three key elements of a total hysterectomy: the ureter, the uterine artery, and the bladder-uterine dissection plane. Despite modest quantitative metrics, qualitative assessments indicated strong clinical utility. These findings establish a foundation for an integrated AI-assisted surgical navigation platform to enhance the safety and standardization of minimally invasive gynecologic surgery.