%0 Journal Article
%T Surgical step recognition in laparoscopic distal gastrectomy using artificial intelligence: a proof-of-concept study.
%A Yoshida M
%A Kitaguchi D
%A Takeshita N
%A Matsuzaki H
%A Ishikawa Y
%A Yura M
%A Akimoto T
%A Kinoshita T
%A Ito M
%J Langenbecks Arch Surg
%V 409
%N 1
%D 2024 Jul 12
%M 38995411
%F 2.895
%R 10.1007/s00423-024-03411-y
%X <B>OBJECTIVE: </B>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.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>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.