Real-World Application of Artificial Intelligence for Detecting Pathologic Gastric Atypia and Neoplastic Lesions.

IF 3.2 4区医学 Q2 GASTROENTEROLOGY & HEPATOLOGY

Journal of Gastric Cancer Pub Date : 2024-07-01 DOI:10.5230/jgc.2024.24.e28

Young Hoon Chang, Cheol Min Shin, Hae Dong Lee, Jinbae Park, Jiwoon Jeon, Soo-Jeong Cho, Seung Joo Kang, Jae-Yong Chung, Yu Kyung Jun, Yonghoon Choi, Hyuk Yoon, Young Soo Park, Nayoung Kim, Dong Ho Lee

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引用次数: 0

Abstract

Purpose: Results of initial endoscopic biopsy of gastric lesions often differ from those of the final pathological diagnosis. We evaluated whether an artificial intelligence-based gastric lesion detection and diagnostic system, ENdoscopy as AI-powered Device Computer Aided Diagnosis for Gastroscopy (ENAD CAD-G), could reduce this discrepancy.

Materials and methods: We retrospectively collected 24,948 endoscopic images of early gastric cancers (EGCs), dysplasia, and benign lesions from 9,892 patients who underwent esophagogastroduodenoscopy between 2011 and 2021. The diagnostic performance of ENAD CAD-G was evaluated using the following real-world datasets: patients referred from community clinics with initial biopsy results of atypia (n=154), participants who underwent endoscopic resection for neoplasms (Internal video set, n=140), and participants who underwent endoscopy for screening or suspicion of gastric neoplasm referred from community clinics (External video set, n=296).

Results: ENAD CAD-G classified the referred gastric lesions of atypia into EGC (accuracy, 82.47%; 95% confidence interval [CI], 76.46%-88.47%), dysplasia (88.31%; 83.24%-93.39%), and benign lesions (83.12%; 77.20%-89.03%). In the Internal video set, ENAD CAD-G identified dysplasia and EGC with diagnostic accuracies of 88.57% (95% CI, 83.30%-93.84%) and 91.43% (86.79%-96.07%), respectively, compared with an accuracy of 60.71% (52.62%-68.80%) for the initial biopsy results (P<0.001). In the External video set, ENAD CAD-G classified EGC, dysplasia, and benign lesions with diagnostic accuracies of 87.50% (83.73%-91.27%), 90.54% (87.21%-93.87%), and 88.85% (85.27%-92.44%), respectively.

Conclusions: ENAD CAD-G is superior to initial biopsy for the detection and diagnosis of gastric lesions that require endoscopic resection. ENAD CAD-G can assist community endoscopists in identifying gastric lesions that require endoscopic resection.

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人工智能在检测病理性胃不典型性和肿瘤病变中的实际应用。

目的：胃部病变的初步内镜活检结果往往与最终病理诊断结果不同。我们评估了基于人工智能的胃病变检测和诊断系统--ENdoscopy as AI-powered Device Computer Aided Diagnosis for Gastroscopy（ENAD CAD-G）--能否减少这种差异：我们回顾性地收集了2011年至2021年间9892名接受食管胃十二指肠镜检查的患者的24948张早期胃癌（EGC）、发育不良和良性病变的内镜图像。我们使用以下真实世界数据集对ENAD CAD-G的诊断性能进行了评估：由社区诊所转诊且初步活检结果为非典型的患者（n=154）、因肿瘤而接受内镜切除术的参与者（内部视频集，n=140），以及由社区诊所转诊的因筛查或怀疑胃肿瘤而接受内镜检查的参与者（外部视频集，n=296）：结果：ENAD CAD-G 将转诊的非典型胃病变分为 EGC（准确率为 82.47%；95% 置信区间 [CI]，76.46%-88.47%）、发育不良（88.31%；83.24%-93.39%）和良性病变（83.12%；77.20%-89.03%）。在内部视频集中，ENAD CAD-G 对发育不良和 EGC 的诊断准确率分别为 88.57% (95% CI, 83.30%-93.84%) 和 91.43% (86.79%-96.07%)，而初始活检结果的准确率为 60.71% (52.62%-68.80%)（结论：ENAD CAD-G 的诊断准确率优于初始活检结果）：在检测和诊断需要内镜切除的胃部病变方面，ENAD CAD-G优于初始活检。ENAD CAD-G可帮助社区内镜医师识别需要内镜切除的胃部病变。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Gastric Cancer Biochemistry, Genetics and Molecular Biology-Cancer Research

CiteScore

4.30

自引率

12.00%

发文量

期刊介绍： The Journal of Gastric Cancer (J Gastric Cancer) is an international peer-reviewed journal. Each issue carries high quality clinical and translational researches on gastric neoplasms. Editorial Board of J Gastric Cancer publishes original articles on pathophysiology, molecular oncology, diagnosis, treatment, and prevention of gastric cancer as well as articles on dietary control and improving the quality of life for gastric cancer patients. J Gastric Cancer includes case reports, review articles, how I do it articles, editorials, and letters to the editor.