Nakajima Kei, Takenaka Shin, Kitaguchi Daichi, Tanaka Atsuki, Ryu Kyoko, Takeshita Nobuyoshi, Kinugasa Yusuke, Ito Masaaki
Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan; Department of Gastrointestinal Surgery, Graduate School of Medicine, Institute of Science Tokyo, 1-5-45, Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan.
Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Eur J Surg Oncol. 2025 Jun 17;51(9):110260. doi: 10.1016/j.ejso.2025.110260.
Poor instrument handling, such as "repeatedly makes tentative or awkward moves" and "grasper (frequently) slip," is associated with poor surgical skills. We constructed and applied an automated recognition model of tissue grasping during laparoscopic surgery using computer vision technology to clarify whether automated surgical-skill assessment using the number of tissue grasps could be feasible.
The number of tissue grasps and classification of success/failure were manually and automatically counted. Intraoperative videos of three groups with obviously different surgical levels (the high-, intermediate-, and low-skill groups) were prepared; an automated distinction between these groups was attempted using the models.
The number of manually counted tissue grasps was significantly higher in the low-skill group than in the other groups, while the number of failed tissue grasps was significantly lower in the high-skill group than in the other groups. The number of automatically counted tissue grasps showed strong correlations with the manually counted ones, whereas the other parameters showed only moderate correlations. The number of automatically counted tissue grasps was significantly higher in the low-skill group than in the other groups, similar to that noted with manual counting. The other automatic parameters showed no results similar to the manual ones.
We successfully constructed automated recognition models of tissue grasping during laparoscopic surgery and found that automated surgical-skill assessment based on the number of tissue grasps could be feasible. However, the results were insufficient for automatically distinguishing between successful/failed tissue grasps. Further improvements in recognition accuracy are required for this model.
器械操作不佳，如“反复进行试探性或笨拙动作”以及“抓钳（频繁）滑脱”，与手术技能欠佳相关。我们利用计算机视觉技术构建并应用了一种腹腔镜手术中组织抓取的自动识别模型，以阐明基于组织抓取次数的自动手术技能评估是否可行。
人工和自动统计组织抓取次数以及成功/失败分类。准备了三组手术水平明显不同（高技能组、中等技能组和低技能组）的术中视频；尝试使用模型对这些组进行自动区分。
低技能组人工统计的组织抓取次数显著高于其他组，而高技能组失败的组织抓取次数显著低于其他组。自动统计的组织抓取次数与人工统计的次数显示出强相关性，而其他参数仅显示出中等相关性。低技能组自动统计的组织抓取次数显著高于其他组，与人工统计结果相似。其他自动参数未显示出与人工参数相似的结果。
我们成功构建了腹腔镜手术中组织抓取的自动识别模型，并发现基于组织抓取次数的自动手术技能评估可能可行。然而，结果不足以自动区分组织抓取的成功/失败。该模型需要进一步提高识别准确性。
