基于深度学习的腹腔镜右侧结肠切除术手术步骤识别。

IF 2.1 3区 医学 Q2 SURGERY
Ryoya Honda, Daichi Kitaguchi, Yuto Ishikawa, Norihito Kosugi, Kazuyuki Hayashi, Hiro Hasegawa, Nobuyoshi Takeshita, Masaaki Ito
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引用次数: 0

摘要

目的:了解腹腔镜右侧结肠切除术(LAP-RC)所涉及的复杂解剖结构和手术步骤对于规范手术过程至关重要。基于深度学习(DL)的计算机视觉可以实现这一目标。本研究的目的是利用带有注释步骤信息的手术视频数据集开发 LAP-RC 的步骤识别模型,并评估其识别性能:这项单中心回顾性研究使用了 2018 年 1 月至 2022 年 3 月期间进行的腹腔镜回盲部切除术(LAP-ICR)和腹腔镜右侧结肠癌切除术(LAP-RHC)的视频数据集。视频被分割成静态图像,分别使用 66%、17% 和 17% 的数据将静态图像分为训练集、验证集和测试集。视频被人工分为八个主要步骤:1)内侧移动;2)中央血管结扎;3)肠系膜上静脉剥离;4)腹膜后移动;5)外侧移动;6)头颅移动;7)系膜切除;8)体腔内吻合。在更简单的版本中,连续的手术步骤被合并为五个步骤。对精确度、召回率、F1 分数和总体准确度进行了评估,以评价模型在手术步骤分类任务中的表现:结果:共纳入 78 例患者;分别有 35 例(44%)和 44 例(56%)患者进行了 LAP-ICR 和 LAP-RHC 手术。八步分类任务和综合五步分类任务的总体准确率分别为 72.1%和 82.9%:使用 DL 算法开发的 LAP-RC 自动手术步骤识别模型表现出相当高的分类性能。能够理解 LAP-RC 复杂步骤的模型将有助于手术过程的标准化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning-based surgical step recognition for laparoscopic right-sided colectomy.

Purpose: Understanding the complex anatomy and surgical steps involved in laparoscopic right-sided colectomy (LAP-RC) is essential for standardizing the surgical procedure. Deep-learning (DL)-based computer vision can achieve this. This study aimed to develop a step recognition model for LAP-RC using a dataset of surgical videos with annotated step information and evaluate its recognition performance.

Methods: This single-center retrospective study utilized a video dataset of laparoscopic ileocecal resection (LAP-ICR) and laparoscopic right-sided hemicolectomy (LAP-RHC) for right-sided colon cancer performed between January 2018 and March 2022. The videos were split into still images, which were divided into training, validation, and test sets using 66%, 17%, and 17% of the data, respectively. Videos were manually classified into eight main steps: 1) medial mobilization, 2) central vascular ligation, 3) dissection of the superior mesenteric vein, 4) retroperitoneal mobilization, 5) lateral mobilization, 6) cranial mobilization, 7) mesocolon resection, and 8) intracorporeal anastomosis. In a simpler version, consecutive surgical steps were combined, resulting in five steps. Precision, recall, F1 scores, and overall accuracy were assessed to evaluate the model's performance in the surgical step classification task.

Results: Seventy-eight patients were included; LAP-ICR and LAP-RHC were performed in 35 (44%) and 44 (56%) patients, respectively. The overall accuracy was 72.1% and 82.9% for the eight-step and combined five-step classification tasks, respectively.

Conclusions: The automatic surgical step-recognition model for LAP-RCs, developed using a DL algorithm, exhibited a fairly high classification performance. A model that understands the complex steps of LAP-RC will aid the standardization of the surgical procedure.

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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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