Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan.
Department of General Surgery, Kumagaya General Hospital, Saitama, Japan.
Surg Endosc. 2024 Sep;38(9):5394-5404. doi: 10.1007/s00464-024-10939-z. Epub 2024 Jul 29.
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.
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.
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.
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.
人工智能（AI）有可能通过预测手术领域内的解剖结构来增强外科实践，从而支持外科医生的经验和认知技能。保护和利用神经作为关键的引导结构对于直肠癌手术至关重要。因此，我们开发了一种基于 U-Net 的深度学习模型，用于自动分割神经。
使用 60 个随机选择的帧评估模型性能，并通过与地面真实数据进行比较来定量评估 Dice 和交并比（IoU）得分。此外，我们向五名结直肠外科医生发放了一份问卷，以评估模型在直肠癌手术中神经的漏检、过检程度和实际应用价值。此外，我们还对非结直肠外科医生、住院医师、医生和医学生进行了教育评估。我们评估了他们在直肠系膜解剖场景中识别神经的能力，对他们进行了 12 分制评分，并检查了在观看 AI 分析视频前后的分数变化。
60 个测试帧的平均 Dice 和 IoU 得分分别为 0.442（范围为 0.0465-0.639）和 0.292（范围为 0.0238-0.469）。结直肠外科医生的漏检评分为 0.80（±0.47），过检评分为 0.58（±0.41），有用性评分为 3.38（±0.43）。非结直肠外科医生、轮转住院医师和医学生的神经识别评分在观看 AI 神经识别视频 1 分钟后显著提高。值得注意的是，与传统的神经讲座相比，医学生在观看 AI 神经分析视频后，神经识别评分的提高更为显著。
在腹腔镜和机器人辅助直肠癌手术中，基于 AI 的神经识别模型达到了专家外科医生的满意识别水平，并在神经识别方面对初级外科医生和医学生的教育具有有效性。
