基于蓝光成像和联动成像的人工智能检测非息肉样结直肠病变及假阳性检测
Non-polypoid Colorectal Lesions Detection and False Positive Detection by Artificial Intelligence under Blue Laser Imaging and Linked Color Imaging.
作者信息
Sugino Satoshi, Yoshida Naohisa, Guo Zhe, Zhang Ruiyao, Inoue Ken, Hirose Ryohei, Dohi Osamu, Itoh Yoshito, Nemoto Daiki, Togashi Kazutomo, Yamamoto Hironori, Zhu Xin
机构信息
Department of Gastroenterology, Asahi University Hospital, Gifu, Japan.
Department of Gastroenterology, Kyoto Prefectural University of Medicine, Kyoto, Japan.
出版信息
J Anus Rectum Colon. 2024 Jul 30;8(3):212-220. doi: 10.23922/jarc.2023-070. eCollection 2024.
OBJECTIVES
Artificial intelligence (AI) with white light imaging (WLI) is not enough for detecting non-polypoid colorectal polyps and it still has high false positive rate (FPR). We developed AIs using blue laser imaging (BLI) and linked color imaging (LCI) to detect them with specific learning sets (LS).
METHODS
The contents of LS were as follows, LS (WLI): 1991 WLI images of lesion of 2-10 mm, LS (IEE): 5920 WLI, BLI, and LCI images of non-polypoid and small lesions of 2-20 mm. LS (IEE) was extracted from videos and included both in-focus and out-of-focus images. We designed three AIs as follows: AI (WLI) finetuned by LS (WLI), AI (IEE) finetuned by LS (WLI)+LS (IEE), and AI (HQ) finetuned by LS (WLI)+LS (IEE) only with images in focus. Polyp detection using a test set of WLI, BLI, and LCI videos of 100 non-polypoid or non-reddish lesions of 2-20 mm and FPR using movies of 15 total colonoscopy were analyzed, compared to 2 experts and 2 trainees.
RESULTS
The sensitivity for LCI in AI (IEE) (83%) was compared to that for WLI in AI (IEE) (76%: p=0.02), WLI in AI (WLI) (57%: p<0.01), BLI in AI (IEE) (78%: p=0.14), and LCI in trainees (74%: p<0.01). The sensitivity for LCI in AI (IEE) (83%) was significantly higher than that in AI (HQ) (78%: p<0.01). The FPR for LCI (6.5%) in AI (IEE) were significantly lower than that in AI (HQ) (17.3%: p<0.01).
CONCLUSIONS
AI finetuned by appropriate LS detected non-reddish and non-polypoid polyps under LCI.
目的
人工智能（AI）结合白光成像（WLI）在检测非息肉样结直肠息肉方面仍存在不足，且假阳性率（FPR）较高。我们开发了使用蓝光激光成像（BLI）和联动彩色成像（LCI）的人工智能，并通过特定学习集（LS）对其进行训练以检测此类息肉。
方法
学习集的内容如下，LS（WLI）：1991张2 - 10毫米病变的WLI图像，LS（IEE）：5920张2 - 20毫米非息肉样及小病变的WLI、BLI和LCI图像。LS（IEE）从视频中提取，包括聚焦和散焦图像。我们设计了三种人工智能如下：通过LS（WLI）微调的AI（WLI），通过LS（WLI） + LS（IEE）微调的AI（IEE），以及仅使用聚焦图像通过LS（WLI） + LS（IEE）微调的AI（HQ）。使用包含100个2 - 20毫米非息肉样或非红色病变的WLI、BLI和LCI视频测试集进行息肉检测，并使用15次全结肠镜检查的视频分析FPR，与2名专家和2名实习医生进行比较。
结果
AI（IEE）中LCI的敏感度（83%）与AI（IEE）中WLI的敏感度（76%：p = 0.02）、AI（WLI）中WLI的敏感度（57%：p < 0.01）、AI（IEE）中BLI的敏感度（78%：p = 0.14）以及实习医生中LCI的敏感度（74%：p < 0.01）进行比较。AI（IEE）中LCI的敏感度（83%）显著高于AI（HQ）中的敏感度（78%：p < 0.01）。AI（IEE）中LCI的FPR（6.5%）显著低于AI（HQ）中的FPR（17.3%：p < 0.01）。
结论
通过适当学习集微调的人工智能在LCI下可检测出非红色和非息肉样息肉。
Zotero 插件