基于深度学习的定位和病变检测在囊内镜中,用于疑似小肠出血的患者
Yeong Seok Kwon1, Tae Yong Park2, So Eui Kim2
1Department of Internal Medicine, Hallym University College of Medicine, Chuncheon-si 24253, South Korea.
World journal of gastroenterology
|July 31, 2025
概括
这项研究介绍了用于小肠囊内镜 (SBCE) 的人工智能 (AI) 模型,该模型显著减少了阅读时间,并准确检测异常. 人工智能辅助的方法为胃肠道出血评估提供了SBCE图像的高效和可靠的解释.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 小肠囊内镜 (SBCE) 对于诊断不清楚的胃肠道出血至关重要,但是劳动密集和主观的.
- 当前的人工智能 (AI) 解决方案往往缺乏同时小肠 (SB) 定位和异常检测能力.
研究的目的:
- 开发和验证人工智能模型,用于在胃肠道内自动定位SB.
- 创建一个能够准确地从囊内镜图像中检测各种SB异常的AI模型.
主要方法:
- 在一个大数据集上训练了一个AI模型,其中包括87,005个囊内镜图像,用于器官定位 (胃,SB,结肠).
- 一个单独的28,405张SBCE图像的数据集被用来训练人工智能检测SB异常,如侵蚀,,血管扩张和出血.
- 通过比较人工智能辅助阅读与32个SBCE视频的传统阅读来评估诊断性能.
主要成果:
- 人工智能模型在SB定位方面表现出高精度 (>97%) 和AUC (>0.99).
- 在检测SB异常方面取得了出色的表现,准确度从99.4%到99.9%不等,AUC高达0.99.
- 人工智能辅助阅读显著减少了解释时间 (8.7分钟与53.9分钟相比),诊断准确度与传统方法相比相当.
结论:
- 开发的AI模型有效地减少了SBCE的阅读时间.
- 人工智能集成到SBCE读数中,可以有效和可靠地检测小肠异常.
- 这种人工智能工具显示出增强囊内镜诊断工作流程的前景.
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