Abstract
BACKGROUND: Artificial intelligence (AI) has the potential to enhance surgical practice by predicting anatomical structures within the surgical field, thereby supporting surgeons' experiences and cognitive skills. Preserving and utilising nerves as critical guiding structures is paramount in rectal cancer surgery. Hence, we developed a deep learning model based on U-Net to automatically segment nerves. METHODS: The model performance was evaluated using 60 randomly selected frames, and the Dice and Intersection over Union (IoU) scores were quantitatively assessed by comparing them with ground truth data. Additionally, a questionnaire was administered to five colorectal surgeons to gauge the extent of underdetection, overdetection, and the practical utility of the model in rectal cancer surgery. Furthermore, we conducted an educational assessment of non-colorectal surgeons, trainees, physicians, and medical students. We evaluated their ability to recognise nerves in mesorectal dissection scenes, scored them on a 12-point scale, and examined the score changes before and after exposure to the AI analysis videos. RESULTS: The mean Dice and IoU scores for the 60 test frames were 0.442 (range 0.0465-0.639) and 0.292 (range 0.0238-0.469), respectively. The colorectal surgeons revealed an under-detection score of 0.80 (± 0.47), an over-detection score of 0.58 (± 0.41), and a usefulness evaluation score of 3.38 (± 0.43). The nerve recognition scores of non-colorectal surgeons, rotating residents, and medical students significantly improved by simply watching the AI nerve recognition videos for 1 min. Notably, medical students showed a more substantial increase in nerve recognition scores when exposed to AI nerve analysis videos than when exposed to traditional lectures on nerves. CONCLUSIONS: In laparoscopic and robot-assisted rectal cancer surgeries, the AI-based nerve recognition model achieved satisfactory recognition levels for expert surgeons and demonstrated effectiveness in educating junior surgeons and medical students on nerve recognition.