Linked Color Imaging for Stomach

Risk Factors for Mucosal Redness in the Duodenal Bulb as Detected via Linked Color Imaging

Linked color imaging (LCI) for image-enhanced endoscopy (IEE) highlights mucosal color differences. We investigated risk factors associated with mucosal redness of the duodenal bulb using LCI. Consecutive patients were retrospectively selected after their duodenal bulbs were observed via LCI. A symptom questionnaire (Izumo scale) was completed. The LCI of the duodenal bulb was subjectively evaluated on whether redness was present and objectively evaluated based on L* a* b* color values. The clinical characteristics of the 302 study participants were: male/female, 120/182; mean age, 70.9 years. Twenty-one cases (7.0%) were in the redness (+) group. After multiple regression analysis, independent predictors for the red component (a*) of the duodenal bulb using LCI were: age (β = -0.154, p < 0.01), female (β = -0.129, p < 0.05), body mass index (BMI; β = -0.136, p < 0.05), Helicobacter pylori eradication (β = 0.137, p < 0.05), endoscopic gastric mucosal atrophy score (EGAS; β = -0.149, p < 0.05), and constipation-related quality of life (QOL) (β = -0.122, p < 0.05) scores. Lower age, lower BMI, lower EGAS, a constipation-related QOL score, post-H. pylori eradication, and being male were associated with mucosal redness in the duodenal bulb with IEE using LCI.

Current status of quality control in screening esophagogastroduodenoscopy and the emerging role of artificial intelligence

Esophagogastroduodenoscopy (EGD) screening is being implemented in countries with a high incidence of upper gastrointestinal (UGI) cancer. High-quality EGD screening ensures the yield of early diagnosis and prevents suffering from advanced UGI cancer and minimal operational-related discomfort. However, performance varied dramatically among endoscopists, and quality control for EGD screening remains suboptimal. Guidelines have recommended potential measures for endoscopy quality improvement and research has been conducted for evidence. Moreover, artificial intelligence offers a promising solution for computer-aided diagnosis and quality control during EGD examinations. In this review, we summarized the key points for quality assurance in EGD screening based on current guidelines and evidence. We also outline the latest evidence, limitations, and future prospects of the emerging role of artificial intelligence in EGD quality control, aiming to provide a foundation for improving the quality of EGD screening.