Wei Huanyu, Deng Li, Wu Xueju, Tan Wenwei, Wu Yi, Yi Bin, Li Yudi, Wang Ruiwei, Liang Xiaolong, Chen Yin, Wang Hui, Tang Shuai, Wang Yanzhou
Department of Obstetrics and Gynecology, The First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing, China.
Department of Obstetrics and Gynecology, No. 960 Hospital of the Joint Service Support Force of the Chinese People's Liberation Army, Jinan, China.
Int J Surg. 2025 Sep 1;111(9):5882-5892. doi: 10.1097/JS9.0000000000002699. Epub 2025 Jun 12.
Automatic detection of surgical instruments is essential for artificial intelligence surgery. This study aimed to construct a large-scale dataset of gynecological laparoscopic surgical instruments based on real surgical scenarios, achieve high-precision real-time detection of surgical instruments, and explore their potential application in surgical skill evaluation.
This cross-sectional study collected 265 gynecological laparoscopic surgical videos from two medical centers for instrument detection. Videos were divided into training and testing sets in a 4:1 ratio, with 161 348 instrument instances extracted. The instruments were detected using Real-Time Models for Object Detection (RTMDet). The mean average precision, sensitivity, and F1 score served as evaluation metrics. External validation was conducted on an independent dataset from a third medical center. Additionally, we further compared the RTMDet with the state-of-the-art PP-YOLOE model on the same dataset. Furthermore, this study performed real-time tracking of instruments during the vaginal cuff suturing step of laparoscopic hysterectomy and compared the differences in kinematic data between proficient and non-proficient videos.
The mean average precision, sensitivity, and F1 score for nine types of surgical instruments were 91.75%, 94.29%, and 93.00%, respectively. External validation on the independent dataset demonstrated robust performance. In the comparison with PP-YOLOE, RTMDet demonstrated superior performance in all metrics. In the comparative analysis of kinematic data, the proficient group demonstrated significantly lesser path lengths and inter-quartile range, shorter moving times, and higher movement velocities for instruments used by both hands compared to the non-proficient group.
This study established a large-scale, real scenario-based database of gynecological laparoscopic instruments. Using the RTMDet model, high-precision real-time detection and tracking of multiple instruments were achieved. Furthermore, this study identified several instrument kinematic metrics that can be used for surgical skill assessment, providing a reference for the objective quantification of the subjective Global Operative Assessment of Laparoscopic Skills.
手术器械的自动检测对人工智能手术至关重要。本研究旨在基于真实手术场景构建一个大规模的妇科腹腔镜手术器械数据集，实现手术器械的高精度实时检测，并探索其在手术技能评估中的潜在应用。
本横断面研究从两个医疗中心收集了265个妇科腹腔镜手术视频用于器械检测。视频按4:1的比例分为训练集和测试集，共提取了161348个器械实例。使用实时目标检测模型（RTMDet）对器械进行检测。平均精度均值、灵敏度和F1分数作为评估指标。在来自第三个医疗中心的独立数据集上进行外部验证。此外，我们在同一数据集上进一步将RTMDet与最先进的PP-YOLOE模型进行比较。此外，本研究在腹腔镜子宫切除术的阴道残端缝合步骤中对器械进行实时跟踪，并比较熟练和不熟练视频之间运动学数据的差异。
九种手术器械的平均精度均值、灵敏度和F1分数分别为91.75%、94.29%和93.00%。在独立数据集上的外部验证显示出稳健的性能。与PP-YOLOE相比，RTMDet在所有指标上均表现出卓越性能。在运动学数据的比较分析中，与不熟练组相比，熟练组双手使用器械的路径长度和四分位间距明显更小，移动时间更短，运动速度更高。
本研究建立了一个大规模的、基于真实场景的妇科腹腔镜器械数据库。使用RTMDet模型，实现了对多种器械的高精度实时检测和跟踪。此外，本研究确定了几个可用于手术技能评估的器械运动学指标，为腹腔镜技能主观整体手术评估的客观量化提供了参考。
