Nakajima Kei, Takenaka Shin, Kitaguchi Daichi, Tanaka Atsuki, Ryu Kyoko, Takeshita Nobuyoshi, Kinugasa Yusuke, Ito Masaaki
Surgical Device Innovation Office, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Department of Gastrointestinal Surgery, Graduate School of Medicine, Tokyo Medical and Dental University, 1-5-45, Yushima, Bunkyo-Ku, Tokyo, 113-8510, Japan.
Langenbecks Arch Surg. 2025 Feb 22;410(1):80. doi: 10.1007/s00423-025-03641-8.
Several surgical-skill assessment tools emphasize the importance of efficient tissue-dissection, whose assessment relies on human judgment and is thus subject to bias. Automated assessment may help solve this problem. This study aimed to verify the feasibility of surgical-skill assessment using a deep learning-based recognition model.
This retrospective study used multicenter intraoperative videos of laparoscopic colorectal surgery (sigmoidectomy or high anterior resection) for colorectal cancer obtained from 766 cases across Japan. Three groups with different skill levels were distinguished: high-, intermediate-, and low-skill. We developed a model to recognize tissue dissection by the monopolar device using deep learning-based computer-vision technology. Tissue-dissection time per monopolar device appearance time (efficient-dissection time ratio) was extracted as a quantitative parameter describing efficient dissection. We automatically measured the efficient-dissection time ratio using the recognition model of 8 surgical instruments and tissue-dissection on/off classification model. The efficient-dissection time ratio was compared among groups; the feasibility of distinguishing them was explored using the model. The model-calculated parameters were evaluated to determine whether they could differentiate high-, intermediate-, and low-skill groups.
The tissue-dissection recognition model had an overall accuracy of 0.91. There was a moderate correlation (0.542; 95% confidence interval, 0.288-0.724; P < 0.001) between manually and automatically measured efficient-dissection time ratios. Efficient-dissection time ratios by this model were significantly higher in the high-skill than in intermediate-skill (P = 0.0081) and low-skill (P = 0.0249) groups.
An automated efficient-dissection assessment model using a monopolar device was constructed with a feasible automated skill-assessment method.
几种手术技能评估工具强调了高效组织解剖的重要性，其评估依赖于人为判断，因此容易产生偏差。自动评估可能有助于解决这一问题。本研究旨在验证使用基于深度学习的识别模型进行手术技能评估的可行性。
这项回顾性研究使用了从日本各地766例结肠癌腹腔镜结直肠手术（乙状结肠切除术或高位前切除术）中获取的多中心术中视频。区分了三组不同技能水平的人群：高技能、中等技能和低技能。我们使用基于深度学习的计算机视觉技术开发了一个模型，用于识别单极设备的组织解剖情况。将每个单极设备出现时间的组织解剖时间（高效解剖时间比）提取为描述高效解剖的定量参数。我们使用8种手术器械的识别模型和组织解剖开/关分类模型自动测量高效解剖时间比。比较了各组之间的高效解剖时间比；使用该模型探索区分它们的可行性。对模型计算的参数进行评估，以确定它们是否能够区分高技能、中等技能和低技能组。
组织解剖识别模型的总体准确率为0.91。手动测量和自动测量的高效解剖时间比之间存在中等程度的相关性（0.542；95%置信区间，0.288 - 0.724；P < 0.001）。该模型得出的高技能组的高效解剖时间比显著高于中等技能组（P = 0.0081）和低技能组（P = 0.0249）。
构建了一种使用单极设备的自动高效解剖评估模型，并采用了可行的自动技能评估方法。
