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人工智能辅助与近红外荧光导航同步用于腹腔镜结直肠癌手术解剖识别的可行性

Feasibility of Simultaneous Artificial Intelligence-Assisted and NIR Fluorescence Navigation for Anatomical Recognition in Laparoscopic Colorectal Surgery.

作者信息

Ryu Shunjin, Imaizumi Yuta, Goto Keisuke, Iwauchi Sotaro, Kobayashi Takehiro, Ito Ryusuke, Nakabayashi Yukio

机构信息

Kawaguchi Municipal Medical Center, 180, Nishiaraijuku, Kawaguchi City, Saitama, 333-0833, Japan.

出版信息

J Fluoresc. 2024 Nov 22. doi: 10.1007/s10895-024-04030-y.

Abstract

Ureters can be visualized on a monitor via fluorescence observation technology and an near-infrared (NIR) fluorescent ureteral catheter (NIRFUC). Eureka, an artificial intelligence (AI) platform, can be used to analyze surgical videos and highlight nerves and loose connective tissue (LCT) in the dissection layer. In this study, we aimed to evaluate the feasibility of using simultaneous NIRFUC and AI assistance for anatomical recognition during laparoscopic surgery. The research target was video recordings of laparoscopic colorectal surgery in which the ureters were visualized using an NIRFUC (n = 56, November 2022 to May 2024). Eureka was used to analyze the nerves and LCTs in these videos. Three physicians reviewed and analyzed the videos, scoring the fluorescence visualization of the ureters and LCT by Eureka, the fluorescence visualization of the ureters and hypogastric nerve by Eureka, and the fluorescence visualization of the ureters and lumbar splanchnic nerves by Eureka, using a Likert scale. The scoring system was as follows: 0, very poor; 1, poor; 2, acceptable; 3, good; and 4, very good. The mean Likert scale score was 3.99 for the ureters and LCT, 3.11 for the ureters and hypogastric nerve, and 3.53 for the ureters and lumbar splanchnic nerves. The training data used for this AI model did not include NIR fluorescence image observations. Anatomical highlighting with AI and fluorescence visualization of the ureters were possible in the images analyzed by Eureka. These findings suggest that both AI and NIR can be used simultaneously for real-time navigation in the future.

摘要

通过荧光观察技术和近红外(NIR)荧光输尿管导管(NIRFUC),可在监视器上可视化输尿管。Eureka是一个人工智能(AI)平台,可用于分析手术视频,并突出显示解剖层面中的神经和疏松结缔组织(LCT)。在本研究中,我们旨在评估在腹腔镜手术中同时使用NIRFUC和AI辅助进行解剖识别的可行性。研究对象是使用NIRFUC可视化输尿管的腹腔镜结直肠手术视频记录(n = 56,2022年11月至2024年5月)。使用Eureka分析这些视频中的神经和LCT。三位医生对视频进行了审查和分析,使用李克特量表对Eureka对输尿管和LCT的荧光可视化、Eureka对输尿管和腹下神经的荧光可视化以及Eureka对输尿管和腰内脏神经的荧光可视化进行评分。评分系统如下:0,非常差;1,差;2,可接受;3,好;4,非常好。输尿管和LCT的李克特量表平均评分为3.99,输尿管和腹下神经为3.11,输尿管和腰内脏神经为3.53。该AI模型使用的训练数据不包括NIR荧光图像观察。在Eureka分析的图像中,AI进行的解剖突出显示和输尿管的荧光可视化是可行的。这些发现表明,未来AI和NIR可同时用于实时导航。

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