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Abstract：
OBJECTIVE: 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.
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.
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.
摘要：
目的：胃病变的初次内镜活检结果通常与最终病理诊断结果不同。我们评估了基于人工智能的胃部病变检测和诊断系统，胃镜检查的计算机辅助诊断（ENADCAD-G），可以减少这种差异。
方法：我们回顾性收集了24,948例早期胃癌（EGC）的内镜图像，发育不良,2011年至2021年间接受食管胃十二指肠镜检查的9,892例患者的良性病变。使用以下真实世界数据集评估了ENADCAD-G的诊断性能：从社区诊所转诊的患者，最初的活检结果为非典型性（n=154），接受肿瘤内镜切除术的参与者(内部视频集，n=140),以及从社区诊所转诊的接受内窥镜检查以筛查或怀疑胃肿瘤的参与者（外部视频集，n=296）。
结果：ENADCAD-G将异型性的转诊胃部病变分为EGC（准确性，82.47%；95%置信区间[CI]，76.46%-88.47%），发育不良（88.31%;83.24%-93.39%），良性病变（83.12%；77.20%-89.03%）。在内部视频集中，ENADCAD-G识别发育不良和EGC，诊断准确率为88.57％（95％CI，83.30％-93.84％）和91.43％（86.79％-96.07％），分别,与初始活检结果的60.71％（52.62％-68.80％）相比（P<0.001）。在外部视频集中，ENADCAD-G分类EGC，发育不良,良性病变的诊断准确率为87.50%（83.73%-91.27%），90.54%（87.21%-93.87%），和88.85%（85.27%-92.44%），分别。
结论：ENADCAD-G在检测和诊断需要内镜切除的胃部病变方面优于初次活检。ENADCAD-G可以帮助社区内窥镜医师识别需要内窥镜切除的胃部病变。
方法：我们回顾性收集了24,948例早期胃癌（EGC）的内镜图像，发育不良,2011年至2021年间接受食管胃十二指肠镜检查的9,892例患者的良性病变。使用以下真实世界数据集评估了ENADCAD-G的诊断性能：从社区诊所转诊的患者，最初的活检结果为非典型性（n=154），接受肿瘤内镜切除术的参与者(内部视频集，n=140),以及从社区诊所转诊的接受内窥镜检查以筛查或怀疑胃肿瘤的参与者（外部视频集，n=296）。
结果：ENADCAD-G将异型性的转诊胃部病变分为EGC（准确性，82.47%；95%置信区间[CI]，76.46%-88.47%），发育不良（88.31%;83.24%-93.39%），良性病变（83.12%；77.20%-89.03%）。在内部视频集中，ENADCAD-G识别发育不良和EGC，诊断准确率为88.57％（95％CI，83.30％-93.84％）和91.43％（86.79％-96.07％），分别,与初始活检结果的60.71％（52.62％-68.80％）相比（P<0.001）。在外部视频集中，ENADCAD-G分类EGC，发育不良,良性病变的诊断准确率为87.50%（83.73%-91.27%），90.54%（87.21%-93.87%），和88.85%（85.27%-92.44%），分别。
结论：ENADCAD-G在检测和诊断需要内镜切除的胃部病变方面优于初次活检。ENADCAD-G可以帮助社区内窥镜医师识别需要内窥镜切除的胃部病变。
