Endoscopy · 2025
A rural-to-center artificial intelligence model for diagnosing Helicobacter pylori infection and premalignant gastric conditions using endoscopy images captured in routine practice.
Клиническое исследованиеСтатьяНазвание и аннотация: Перевод пока не подготовлен
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Chiang Tsung-Hsien, Hsu Yen-Ning, Chen Min-Han, Chen Yi-Ru, Cheng Hsiu-Chi, Chen Mei-Jin, Lee Fu-Jen, Chang Chi-Yang, Chang Chun-Chao, Bair Ming-Jong, Liou Jyh-Ming, Chen Chiuan-Jung, Chen Yen-Chung, Chiang Hung, Shun Chia-Tung, Liu Jui-Hsuan, Chiu Han-Mo, Wu Ming-Shiang, Yu Jiun-Yu, Guo Ruey-Shan, Lin Jaw-Town, Lee Yi-Chia, Chen Chu-Song
Аннотация
BACKGROUND: Diagnosing Helicobacter pylori infection and premalignant gastric conditions typically requires 13C urea breath testing or histological assessment, which are often unavailable in remote areas. A rural-to-center artificial intelligence (AI) model was developed and implemented to automatically evaluate upper endoscopy images from routine clinical practice.
METHODS: Endoscopic images were collected from a rural hospital on Matsu Islands and a tertiary center across Taiwan Strait. During model development (2020–2022), AI algorithms were trained, validated, and tested to exclude low-quality and non-gastric images, segment gastric regions, and enhance mucosal features for detecting H. pylori infection and premalignant conditions. During model implementation (2023–2024), endoscopic images from a rural hospital were transmitted to the medical center for AI analyses, with results promptly returned.
RESULTS: In the development phase, diagnostic accuracies were 92.8% (95%CI 88.9%–96.6%) for H. pylori, 88.6% (95%CI 87.2%–90.0%) for atrophic gastritis, and 88.0% (95%CI 86.5%–89.5%) for intestinal metaplasia. In the implementation phase, 3518 rural residents underwent 13C urea breath testing or pepsinogen testing; 421 with positive results underwent endoscopy. No significant differences were observed between AI-predicted and clinically observed prevalence: H. pylori (13.9% vs. 12.9%; P = 0.55), atrophic gastritis (15.7% vs. 11.9%; P = 0.34), and intestinal metaplasia (27.6% vs. 22.4%; P = 0.32). Implementation-phase diagnostic accuracies were 91.3% (95%CI 88.0%–94.6%), 79.9% (95%CI 72.1%–86.3%), and 63.4% (95%CI 54.7%–71.6%), respectively.
CONCLUSIONS: AI enabled physicians in resource-limited settings to rapidly assess gastric health using routinely captured endoscopic images, bridging gaps in access and expertise.
METHODS: Endoscopic images were collected from a rural hospital on Matsu Islands and a tertiary center across Taiwan Strait. During model development (2020–2022), AI algorithms were trained, validated, and tested to exclude low-quality and non-gastric images, segment gastric regions, and enhance mucosal features for detecting H. pylori infection and premalignant conditions. During model implementation (2023–2024), endoscopic images from a rural hospital were transmitted to the medical center for AI analyses, with results promptly returned.
RESULTS: In the development phase, diagnostic accuracies were 92.8% (95%CI 88.9%–96.6%) for H. pylori, 88.6% (95%CI 87.2%–90.0%) for atrophic gastritis, and 88.0% (95%CI 86.5%–89.5%) for intestinal metaplasia. In the implementation phase, 3518 rural residents underwent 13C urea breath testing or pepsinogen testing; 421 with positive results underwent endoscopy. No significant differences were observed between AI-predicted and clinically observed prevalence: H. pylori (13.9% vs. 12.9%; P = 0.55), atrophic gastritis (15.7% vs. 11.9%; P = 0.34), and intestinal metaplasia (27.6% vs. 22.4%; P = 0.32). Implementation-phase diagnostic accuracies were 91.3% (95%CI 88.0%–94.6%), 79.9% (95%CI 72.1%–86.3%), and 63.4% (95%CI 54.7%–71.6%), respectively.
CONCLUSIONS: AI enabled physicians in resource-limited settings to rapidly assess gastric health using routinely captured endoscopic images, bridging gaps in access and expertise.