Deep learning-based intraoperative visual guidance model for ureter identification in laparoscopic sigmoidectomy.

BACKGROUND

Identifying the left ureter is a key step while performing laparoscopic sigmoid resection to prevent intraoperative injury and postoperative complications.

METHODS

This feasibility study aims to evaluate the real-time performance of a deep learning-based computer vision model in identifying the left ureter during laparoscopic sigmoid resection. A deep learning model for ureteral identification was developed using a semantic segmentation algorithm trained from intraoperative images of ureteral dissection in videos depicted from laparoscopic sigmoid resection. We used 86 laparoscopic sigmoid resection recordings performed at King Abdulaziz University Hospital (KAUH), which were further processed with manual annotation. A total of 1237 images were extracted and annotated by three colorectal surgeons. Deep learning You Only Look Once (YOLO) versions 8 and 11 models were applied to the video recording of ureteral identification. Per-frame five-fold cross-validation was used to evaluate model performance.

RESULTS

Experiments showed high results with a mean Average Precision (mAP50) of 0.92 for the Intersection over Union (IoU) threshold greater than or equal to 0.5. The precision, recall, and Dice Coefficient (DC) evaluation metrics are 0.94, 0.88, and 0.90, respectively. The highest DC result is 0.95, achieved through the fourth-fold cross-validation. The stricter IoU threshold between 0.5 and 0.95 is represented by mAP50-95, which is 0.53. The model operated at a speed of 32 Frames Per Second (FPS), indicating it can work in real-time.

CONCLUSION

Deep learning YOLO 8 and 10 for semantic segmentation demonstrates accurate real-time identification of the left ureter in selected videos. A deep learning model could be used to project high-accuracy identification of real-time left ureter during laparoscopic sigmoidectomy using surgeons' expertise in intraoperative image navigation. Limitations included the sample size, lack of diversity in the methods of surgery, incomplete surgical processes, and lack of external validation.