Yang Hye Yeon, Hong Seung Soo, Yoon Jihun, Park Bokyung, Yoon Youngno, Han Dai Hoon, Choi Gi Hong, Choi Min-Kook, Kim Sung Hyun
Department of Liver Transplantation and Hepatobiliary and Pancreatic Surgery, Ajou University School of Medicine, Suwon, Korea.
Department of Hepatobiliary and Pancreatic Surgery, Yonsei University College of Medicine, Seoul, Korea.
Ann Hepatobiliary Pancreat Surg. 2024 Nov 30;28(4):466-473. doi: 10.14701/ahbps.24-091. Epub 2024 Jul 29.
BACKGROUNDS/AIMS: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases.
One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training.
A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot's triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score: 0.7761).
Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.
背景/目的：在微创手术时代，人工智能（AI）技术已被用于利用视频记录评估手术质量、进行教育以及评估手术表现。为了实现有效的评估以达成评估和评价，人们对从手术视频中自动化手术工作流程分析给予了诸多关注。本研究旨在设计一种深度学习模型，以使用腹腔镜胆囊切除术视频自动识别手术阶段，并自动评估识别手术阶段的准确性。
收集了来自公共数据集（Cholec80）的120个胆囊切除术视频以及2022年7月至2022年12月在单一机构录制的40个腹腔镜胆囊切除术视频。这些数据集以2:1的比例划分为AI模型的训练集和测试集。根据训练模型的结构特征构建测试场景。未对输入数据或推理输出进行预处理或后处理，以准确分析标签对模型训练的影响。
从40例病例中提取了总共98234帧作为测试数据。模型的总体准确率为91.2%。最准确的阶段是胆囊三角解剖（F1分数：0.9421），而最不准确的阶段是夹闭和切断（F1分数：0.7761）。
我们的AI模型能够高精度地识别腹腔镜胆囊切除术的各个阶段。
