Abstract：
OBJECTIVE: Laparoscopic distal gastrectomy (LDG) is a difficult procedure for early career surgeons. Artificial intelligence (AI)-based surgical step recognition is crucial for establishing context-aware computer-aided surgery systems. In this study, we aimed to develop an automatic recognition model for LDG using AI and evaluate its performance.
METHODS: Patients who underwent LDG at our institution in 2019 were included in this study. Surgical video data were classified into the following nine steps: (1) Port insertion; (2) Lymphadenectomy on the left side of the greater curvature; (3) Lymphadenectomy on the right side of the greater curvature; (4) Division of the duodenum; (5) Lymphadenectomy of the suprapancreatic area; (6) Lymphadenectomy on the lesser curvature; (7) Division of the stomach; (8) Reconstruction; and (9) From reconstruction to completion of surgery. Two gastric surgeons manually assigned all annotation labels. Convolutional neural network (CNN)-based image classification was further employed to identify surgical steps.
RESULTS: The dataset comprised 40 LDG videos. Over 1,000,000 frames with annotated labels of the LDG steps were used to train the deep-learning model, with 30 and 10 surgical videos for training and validation, respectively. The classification accuracies of the developed models were precision, 0.88; recall, 0.87; F1 score, 0.88; and overall accuracy, 0.89. The inference speed of the proposed model was 32 ps.
CONCLUSIONS: The developed CNN model automatically recognized the LDG surgical process with relatively high accuracy. Adding more data to this model could provide a fundamental technology that could be used in the development of future surgical instruments.
METHODS: Patients who underwent LDG at our institution in 2019 were included in this study. Surgical video data were classified into the following nine steps: (1) Port insertion; (2) Lymphadenectomy on the left side of the greater curvature; (3) Lymphadenectomy on the right side of the greater curvature; (4) Division of the duodenum; (5) Lymphadenectomy of the suprapancreatic area; (6) Lymphadenectomy on the lesser curvature; (7) Division of the stomach; (8) Reconstruction; and (9) From reconstruction to completion of surgery. Two gastric surgeons manually assigned all annotation labels. Convolutional neural network (CNN)-based image classification was further employed to identify surgical steps.
RESULTS: The dataset comprised 40 LDG videos. Over 1,000,000 frames with annotated labels of the LDG steps were used to train the deep-learning model, with 30 and 10 surgical videos for training and validation, respectively. The classification accuracies of the developed models were precision, 0.88; recall, 0.87; F1 score, 0.88; and overall accuracy, 0.89. The inference speed of the proposed model was 32 ps.
CONCLUSIONS: The developed CNN model automatically recognized the LDG surgical process with relatively high accuracy. Adding more data to this model could provide a fundamental technology that could be used in the development of future surgical instruments.
摘要：
目的：腹腔镜远端胃切除术（LDG）对于早期职业外科医生来说是一项困难的手术。基于人工智能（AI）的手术步骤识别对于建立上下文感知的计算机辅助手术系统至关重要。在这项研究中,我们旨在使用AI开发LDG的自动识别模型并评估其性能。
方法：2019年在我们机构接受LDG的患者被纳入本研究。手术视频数据分为以下九个步骤：（1）端口插入；（2）较大曲率的左侧淋巴结清扫术；（3）较大曲率的右侧淋巴结清扫术；（4）十二指肠分区；（5）胰上区淋巴结清扫术；（6）小曲率淋巴结清扫术；（7）胃部重建术；（8）重建术。两名胃外科医生手动分配所有注释标签。进一步采用基于卷积神经网络(CNN)的图像分类来识别手术步骤。
结果：数据集包含40个LDG视频。使用了超过1,000,000个带有LDG步骤注释标签的框架来训练深度学习模型，有30和10个手术视频进行培训和验证，分别。所开发模型的分类精度是精确的，0.88；召回，0.87；F1得分，0.88;和整体精度，0.89.该模型的推理速度为32ps。
结论：开发的CNN模型以相对较高的准确性自动识别LDG手术过程。向该模型添加更多数据可以提供可用于开发未来手术器械的基本技术。
方法：2019年在我们机构接受LDG的患者被纳入本研究。手术视频数据分为以下九个步骤：（1）端口插入；（2）较大曲率的左侧淋巴结清扫术；（3）较大曲率的右侧淋巴结清扫术；（4）十二指肠分区；（5）胰上区淋巴结清扫术；（6）小曲率淋巴结清扫术；（7）胃部重建术；（8）重建术。两名胃外科医生手动分配所有注释标签。进一步采用基于卷积神经网络(CNN)的图像分类来识别手术步骤。
结果：数据集包含40个LDG视频。使用了超过1,000,000个带有LDG步骤注释标签的框架来训练深度学习模型，有30和10个手术视频进行培训和验证，分别。所开发模型的分类精度是精确的，0.88；召回，0.87；F1得分，0.88;和整体精度，0.89.该模型的推理速度为32ps。
结论：开发的CNN模型以相对较高的准确性自动识别LDG手术过程。向该模型添加更多数据可以提供可用于开发未来手术器械的基本技术。
