Sunakawa Taiki, Kitaguchi Daichi, Kobayashi Shin, Aoki Keishiro, Kujiraoka Manabu, Sasaki Kimimasa, Azuma Lena, Yamada Atsushi, Kudo Masashi, Sugimoto Motokazu, Hasegawa Hiro, Takeshita Nobuyoshi, Gotohda Naoto, Ito Masaaki
Department of Hepatobiliary and Pancreatic Surgery, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Course of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan.
Surg Endosc. 2024 Dec;38(12):7656-7662. doi: 10.1007/s00464-024-11331-7. Epub 2024 Nov 18.
Intraoperative hemorrhage during laparoscopic hepatectomy (LH) is a risk factor for negative postoperative outcomes. Ensuring appropriate hemostasis enhances the safety of surgical procedures. An automatic bleeding recognition system based on deep learning can lead to safer surgeries; however, deep learning models that are useful for detecting and stopping bleeding in LH have not yet been reported. In this study, we aimed to develop a deep learning model to automatically recognize bleeding regions during liver transection in LH.
In this retrospective feasibility study, bleeding scenes were randomly selected from LH videos, and the videos were divided into frames at 30 frames per second. Bleeding regions within the images were annotated by pixels, and subsequently, all images were assigned to the training, validation, and test datasets to develop the deep learning model. A convolutional neural network algorithm was used to perform semantic segmentation. After training and validation, the model was evaluated using images from the test dataset. Precision, recall, and Dice coefficients served as the evaluation metrics for the model.
In total, 2203 annotated images from 44 LH videos were utilized and divided into 1500, 400, and 303 frames for the training, validation, and test datasets, respectively. The precision, recall, and Dice coefficient values of the model were 0.76, 0.79, and 0.77, respectively.
We developed an automatic bleeding recognition model based on semantic segmentation and verified its performance. The proposed model is potentially useful for intraoperative alerting or evaluating surgical skills in the future.
腹腔镜肝切除术（LH）术中出血是术后不良结局的一个危险因素。确保适当的止血可提高手术安全性。基于深度学习的自动出血识别系统可使手术更安全；然而，尚未有可用于检测和停止LH术中出血的深度学习模型的相关报道。在本研究中，我们旨在开发一种深度学习模型，以自动识别LH肝实质离断过程中的出血区域。
在这项回顾性可行性研究中，从LH视频中随机选取出血场景，并以每秒30帧的速度将视频分割成帧。图像中的出血区域通过像素进行标注，随后，将所有图像分配到训练集、验证集和测试集中，以开发深度学习模型。使用卷积神经网络算法进行语义分割。经过训练和验证后，使用测试集的图像对模型进行评估。精确率、召回率和Dice系数作为模型的评估指标。
总共使用了来自44个LH视频的2203张标注图像，分别分为1500帧、400帧和303帧用于训练集、验证集和测试集。该模型的精确率、召回率和Dice系数值分别为0.76、0.79和0.77。
我们开发了一种基于语义分割的自动出血识别模型，并验证了其性能。所提出的模型在未来可能有助于术中警报或评估手术技能。
