Background/Aims: Diagnosis of Helicobacter pylori-naïve gastric cancer (HPNGC) is becoming increasingly important. This study aimed to explore the quality indicators for HPNGC detection. Methods: We conducted a cross-sectional, nationwide, web-based survey of gastrointestinal endoscopists in Japan. In addition to questions about the number of HPNGC cases detected in a year and basic information, the questionnaire also consisted of 28 questions: (1) 18 about HPNGC awareness, (2) six about diagnostic proactiveness, and (3) four about interest in HPNGC. Results: Valid responses were obtained from 712 endoscopists. The Japan Gastroenterological Endoscopy Society-certified endoscopists had a significantly higher HPNGC detection rate than the nonspecialists (0.42% vs. 0.32%, respectively; p=0.008). The results of the multiple regression analysis showed that Japan Gastroenterological Endoscopy Society certification and high awareness and interest scores were independent predictors of the HPNGC detection rate (p=0.012, p<0.001, p=0.024, respectively). Principal component analysis showed that the endoscopists who attended conferences for collecting information on HPNGC had a higher level of awareness. Conclusions To improve the detection of HPNGC, it is necessary to increase the awareness of the disease. It is hoped that relevant societies will play an important role in endoscopists’ education.

Colonoscopy plays an important role in reducing the incidence and mortality of colorectal cancer by detecting adenomas and other precancerous lesions. Image-enhanced endoscopy (IEE) increases lesion visibility by enhancing the microstructure, blood vessels, and mucosal surface color, resulting in the detection of colorectal lesions. In recent years, various IEE techniques have been used in clinical practice, each with its unique characteristics. Numerous studies have reported the effectiveness of IEE in the detection of colorectal lesions. IEEs can be divided into two broad categories according to the nature of the image: images constructed using narrowband wavelength light, such as narrowband imaging and blue laser imaging/blue light imaging, or color images based on white light, such as linked color imaging, texture and color enhancement imaging, and i-scan. Conversely, artificial intelligence (AI) systems, such as computer-aided diagnosis systems, have recently been developed to assist endoscopists in detecting colorectal lesions during colonoscopy. To better understand the features of each IEE, this review presents the effectiveness of each type of IEE and their combination with AI for colorectal lesion detection by referencing the latest research data.