人工智能评估腹腔镜结直肠手术的组织切除效率。

IF 2.1 3区 医学 Q2 SURGERY
Kei Nakajima, Shin Takenaka, Daichi Kitaguchi, Atsuki Tanaka, Kyoko Ryu, Nobuyoshi Takeshita, Yusuke Kinugasa, Masaaki Ito
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引用次数: 0

摘要

目的:一些外科技能评估工具强调了有效组织解剖的重要性,其评估依赖于人的判断,因此容易产生偏差。自动评估可能有助于解决这个问题。本研究旨在验证使用基于深度学习的识别模型进行外科技能评估的可行性。方法:本回顾性研究使用日本766例腹腔镜结直肠癌手术(乙状结肠切除术或高位前切除术)的多中心术中视频。三组不同的技能水平被区分:高、中、低技能。我们利用基于深度学习的计算机视觉技术开发了一个单极装置识别组织解剖的模型。提取单极器件出现时间的组织解剖时间(有效解剖时间比)作为描述有效解剖的定量参数。采用8种手术器械识别模型和组织解剖开/关分类模型自动测量有效-解剖时间比。比较各组有效解剖时间比;探讨了利用该模型对其进行区分的可行性。评估模型计算的参数,以确定它们是否可以区分高、中、低技能组。结果:组织解剖识别模型的总体准确率为0.91。中度相关(0.542;95%置信区间为0.288-0.724;结论:建立了一种可行的单极器高效解剖自动化评估模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence assessment of tissue-dissection efficiency in laparoscopic colorectal surgery.

Purpose: Several surgical-skill assessment tools emphasize the importance of efficient tissue-dissection, whose assessment relies on human judgment and is thus subject to bias. Automated assessment may help solve this problem. This study aimed to verify the feasibility of surgical-skill assessment using a deep learning-based recognition model.

Methods: This retrospective study used multicenter intraoperative videos of laparoscopic colorectal surgery (sigmoidectomy or high anterior resection) for colorectal cancer obtained from 766 cases across Japan. Three groups with different skill levels were distinguished: high-, intermediate-, and low-skill. We developed a model to recognize tissue dissection by the monopolar device using deep learning-based computer-vision technology. Tissue-dissection time per monopolar device appearance time (efficient-dissection time ratio) was extracted as a quantitative parameter describing efficient dissection. We automatically measured the efficient-dissection time ratio using the recognition model of 8 surgical instruments and tissue-dissection on/off classification model. The efficient-dissection time ratio was compared among groups; the feasibility of distinguishing them was explored using the model. The model-calculated parameters were evaluated to determine whether they could differentiate high-, intermediate-, and low-skill groups.

Results: The tissue-dissection recognition model had an overall accuracy of 0.91. There was a moderate correlation (0.542; 95% confidence interval, 0.288-0.724; P < 0.001) between manually and automatically measured efficient-dissection time ratios. Efficient-dissection time ratios by this model were significantly higher in the high-skill than in intermediate-skill (P = 0.0081) and low-skill (P = 0.0249) groups.

Conclusion: An automated efficient-dissection assessment model using a monopolar device was constructed with a feasible automated skill-assessment method.

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来源期刊
CiteScore
3.30
自引率
8.70%
发文量
342
审稿时长
4-8 weeks
期刊介绍: Langenbeck''s Archives of Surgery aims to publish the best results in the field of clinical surgery and basic surgical research. The main focus is on providing the highest level of clinical research and clinically relevant basic research. The journal, published exclusively in English, will provide an international discussion forum for the controlled results of clinical surgery. The majority of published contributions will be original articles reporting on clinical data from general and visceral surgery, while endocrine surgery will also be covered. Papers on basic surgical principles from the fields of traumatology, vascular and thoracic surgery are also welcome. Evidence-based medicine is an important criterion for the acceptance of papers.
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