Ribbens Vincent J, Baltus Simon C, Tan Can Ozan, Broeders Ivo A M J
Surgery Department, Meander Medical Centre, Maatweg, 3818 TZ, Amersfoort, Utrecht, The Netherlands.
Robotics and Mechatronics, University of Twente, Drienerlolaan, 5722 NB, Enschede, Overijssel, The Netherlands.
Surg Endosc. 2025 Aug 18. doi: 10.1007/s00464-025-12078-5.
Electrosurgical devices provide significant advantages for tissue dissection in laparoscopic procedures. However, achieving optimal hemostasis while minimizing tissue coagulation is challenging. Monitoring device-induced bleeding will provide viable information for surgical skills assessment. We aimed to automatically detect bleeding induced by electrosurgical device use in laparoscopic videos using machine learning.
We present a two-step methodology for the automated detection of device-induced bleeding. First, based on the color representation, a random forest classifier (RFC) detects blood pixels in the frames before and after the electrosurgical device activation. Subsequently, a logistic regression (LR) model decides whether bleeding has occurred based on the change in blood pixels. The moments of device activations during surgery can be extracted automatically by a synchronized recording of the laparoscopic video and energy generator data. The RFC and LR were developed on the manual annotation of 34 images and 2678 video fragments from forty-five patients who underwent diaphragmatic hernia repair between May 2023 and October 2024. The performance of the RFC was evaluated by an 80/20 split for training and testing, while a stratified threefold cross-validation assessed the LR performance.
The blood pixel detection showed an accuracy of 94% and a Dice score of 0.472. The classification of automatically extracted video fragments showed that device-induced bleeding can be detected with a 78.2% accuracy, 4.6% precision, 78.1% specificity, and 81.0% sensitivity.
The presented work on device-induced bleeding detection is a step toward quantifying the effect of electrosurgery use. We showed a machine learning-based methodology that accurately identifies video fragments of device activations without bleeding but struggles to identify bleeding precisely. Future work should focus on developing device-induced bleeding detection in a larger, more diverse dataset.
电外科设备在腹腔镜手术中的组织切割方面具有显著优势。然而，在使组织凝固最小化的同时实现最佳止血具有挑战性。监测设备引起的出血将为手术技能评估提供可行信息。我们旨在使用机器学习自动检测腹腔镜视频中电外科设备使用引起的出血。
我们提出了一种用于自动检测设备引起的出血的两步法。首先，基于颜色表示，随机森林分类器（RFC）检测电外科设备激活前后帧中的血液像素。随后，逻辑回归（LR）模型根据血液像素的变化来判定是否发生了出血。手术期间设备激活的时刻可以通过腹腔镜视频和能量发生器数据的同步记录自动提取。RFC和LR是基于对2023年5月至2024年10月期间接受膈疝修补术的45名患者的34张图像和2678个视频片段的手动标注开发的。RFC的性能通过80/20分割进行训练和测试来评估，而分层三倍交叉验证评估LR的性能。
血液像素检测的准确率为94%，Dice评分为0.472。对自动提取的视频片段进行分类表明，设备引起的出血检测准确率为78.2%，精确率为4.6%，特异性为78.1%，灵敏度为81.0%。
所呈现的关于设备引起的出血检测的工作是朝着量化电外科使用效果迈出的一步。我们展示了一种基于机器学习的方法，该方法能够准确识别无出血的设备激活视频片段，但难以精确识别出血情况。未来的工作应专注于在更大、更多样化的数据集中开发设备引起的出血检测方法。
