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一种用于结直肠息肉实时光学表征的新型人工智能设备。

A novel AI device for real-time optical characterization of colorectal polyps.

作者信息

Biffi Carlo, Salvagnini Pietro, Dinh Nhan Ngo, Hassan Cesare, Sharma Prateek, Cherubini Andrea

机构信息

Artificial Intelligence Group, Cosmo AI/Linkverse, Lainate/Rome, Italy.

Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy.

出版信息

NPJ Digit Med. 2022 Jun 30;5(1):84. doi: 10.1038/s41746-022-00633-6.

Abstract

Accurate in-vivo optical characterization of colorectal polyps is key to select the optimal treatment regimen during colonoscopy. However, reported accuracies vary widely among endoscopists. We developed a novel intelligent medical device able to seamlessly operate in real-time using conventional white light (WL) endoscopy video stream without virtual chromoendoscopy (blue light, BL). In this work, we evaluated the standalone performance of this computer-aided diagnosis device (CADx) on a prospectively acquired dataset of unaltered colonoscopy videos. An international group of endoscopists performed optical characterization of each polyp acquired in a prospective study, blinded to both histology and CADx result, by means of an online platform enabling careful video assessment. Colorectal polyps were categorized by reviewers, subdivided into 10 experts and 11 non-experts endoscopists, and by the CADx as either "adenoma" or "non-adenoma". A total of 513 polyps from 165 patients were assessed. CADx accuracy in WL was found comparable to the accuracy of expert endoscopists (CADx/Exp; OR 1.211 [0.766-1.915]) using histopathology as the reference standard. Moreover, CADx accuracy in WL was found superior to the accuracy of non-expert endoscopists (CADx/NonExp; OR 1.875 [1.191-2.953]), and CADx accuracy in BL was found comparable to it (CADx/CADx; OR 0.886 [0.612-1.282]). The proposed intelligent device shows the potential to support non-expert endoscopists in systematically reaching the performances of expert endoscopists in optical characterization.

摘要

结直肠息肉的准确体内光学特征对于在结肠镜检查期间选择最佳治疗方案至关重要。然而,据报道,内镜医师之间的诊断准确率差异很大。我们开发了一种新型智能医疗设备,该设备能够使用传统白光(WL)内镜视频流进行无缝实时操作,无需虚拟色素内镜检查(蓝光,BL)。在这项工作中,我们在前瞻性获取的未改变的结肠镜检查视频数据集上评估了这种计算机辅助诊断设备(CADx)的独立性能。一组国际内镜医师通过一个能够进行仔细视频评估的在线平台,对前瞻性研究中获取的每个息肉进行光学特征分析,对组织学和CADx结果均不知情。结直肠息肉由评审人员进行分类,分为10名专家内镜医师和11名非专家内镜医师,CADx将息肉分类为“腺瘤”或“非腺瘤”。共评估了来自165名患者的513个息肉。以组织病理学作为参考标准,发现CADx在白光下的准确率与专家内镜医师的准确率相当(CADx/专家;OR 1.211 [0.766 - 1.915])。此外,发现CADx在白光下的准确率优于非专家内镜医师的准确率(CADx/非专家;OR 1.875 [1.191 - 2.953]),并且发现CADx在蓝光下的准确率与之相当(CADx/CADx;OR 0.886 [0.612 - 1.282])。所提出的智能设备显示出有潜力支持非专家内镜医师在光学特征分析方面系统地达到专家内镜医师的表现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dc9/9247164/a7f7eae10918/41746_2022_633_Fig1_HTML.jpg

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