BACKGROUND Helicobacter pylori (H. pylori) eradication is required to reduce incidence related to gastric cancer. Recently, it was found that even after the successful eradication of H. pylori, an increased, i.e., moderate, risk of gastric cancer persists in patients with advanced mucosal atrophy and/or intestinal metaplasia. This study aimed to develop a computer-aided diagnosis (CAD) system to classify the status of H. pylori infection of patients into three categories: uninfected (with no history of H. pylori infection), currently infected, and post-eradication. METHODS The CAD system was based on linked color imaging (LCI) combined with deep learning (DL). First, a validation dataset was formed for the CAD systems by recording endoscopic movies of 120 subjects. Next, a training dataset of 395 subjects was prepared to enable DL. All endoscopic examinations were recorded using both LCI and white-light imaging (WLI). These endoscopic data were used to develop two different CAD systems, one for LCI (LCI-CAD) and one for WLI (WLI-CAD) images. RESULTS The diagnostic accuracy of the LCI-CAD system was 84.2% for uninfected, 82.5% for currently infected, and 79.2% for post-eradication status. Comparisons revealed superior accuracy of diagnoses based on LCI-CAD data relative based on WLI-CAD for uninfected, currently infected, and post-eradication cases. Furthermore, the LCI-CAD system demonstrated comparable diagnostic accuracy to that of experienced endoscopists with the validation data set of LCI. CONCLUSIONS The results of this study suggest the feasibility of an innovative gastric cancer screening program to determine cancer risk in individual subjects based on LCI-CAD.

中文翻译：

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使用链接彩色成像和深度学习的幽门螺杆菌感染的内镜三类诊断：单中心前瞻性研究（带视频）。

背景 需要根除幽门螺杆菌 (H. pylori) 以降低与胃癌相关的发病率。最近，发现即使在成功根除幽门螺杆菌之后，在患有晚期粘膜萎缩和/或肠化生的患者中胃癌的风险仍然增加，即中度。本研究旨在开发计算机辅助诊断（CAD）系统，将患者的H. pylori感染状况分为三类：未感染（无H. pylori感染史）、目前感染和根除后。方法 CAD 系统基于链接彩色成像 (LCI) 与深度学习 (DL) 相结合。首先，通过记录 120 名受试者的内窥镜电影，为 CAD 系统形成了一个验证数据集。接下来，准备了一个包含 395 名受试者的训练数据集以启用深度学习。所有内窥镜检查均使用 LCI 和白光成像 (WLI) 进行记录。这些内窥镜数据用于开发两种不同的 CAD 系统，一种用于 LCI (LCI-CAD)，一种用于 WLI (WLI-CAD) 图像。结果 LCI-CAD 系统对未感染的诊断准确率为 84.2%，当前感染的准确率为 82.5%，根除后状态的准确率为 79.2%。比较显示，基于 LCI-CAD 数据的诊断准确性高于基于 WLI-CAD 的未感染、当前感染和根除后病例。此外，LCI-CAD 系统通过 LCI 的验证数据集展示了与经验丰富的内窥镜医师相当的诊断准确性。结论 本研究的结果表明，基于 LCI-CAD 确定个体受试者癌症风险的创新胃癌筛查计划的可行性。