Endoscopy international open · 2026
Development and validation of an artificial intelligence classifier for histologic grading of superficial non-ampullary duodenal epithelial tumors.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Yamamoto Kazuhiro, Goto Atsushi, Ito Shunsuke, Hamabe Kouichi, Hashimoto Shinichi, Nishikawa Jun, Nakamura Katsuhiko, Kiyotoki Shu, Shirai Yasuyuki, Kaino Seiji, Yanai Hideo, Ogihara Hiroyuki, Hamamoto Yoshihiko, Takami Taro
Аннотация
BACKGROUND AND STUDY AIMS: Superficial non-ampullary duodenal epithelial tumors (SNADETs) are being increasingly diagnosed, and accurate endoscopic diagnosis is essential for determining appropriate treatment strategies. However, no standardized diagnostic method currently exists. We developed a deep learning-based artificial intelligence (AI) classifier to distinguish between low-grade and high-grade neoplasia and evaluated its diagnostic performance.
PATIENTS AND METHODS: The training set of 85 SNADET cases (low-grade neoplasia, n = 41; high-grade neoplasia, n = 44; white-light images, n = 317) from Yamaguchi University were used to train the AI classifier. Validation was performed using a test set of 69 lesions (low-grade neoplasia, n = 31; high-grade neoplasia, n =38; images, n = 229) from three institutions. Three expert endoscopists also evaluated the test set using an endoscopic scoring system for comparison.
RESULTS: The AI classifier achieved 78.3% accuracy, 92.1% sensitivity, 61.3% specificity, 74.5% positive predictive value (PPV), 86.4% negative predictive value (NPV), and an F1 measure of 0.824. In contrast, endoscopist diagnosis yielded 58.0% accuracy, 50.0% sensitivity, 67.7% specificity, 65.5% PPV, 52.5% NPV, and an F1 measure of 0.567. Diagnostic accuracy and sensitivity of the AI was significantly higher than that of the endoscopists ( P <0.05).
CONCLUSIONS: The AI classifier demonstrated higher sensitivity and overall diagnostic performance in comparison to endoscopists in differentiating high-grade neoplasia, suggesting its potential utility in guiding treatment decisions.
PATIENTS AND METHODS: The training set of 85 SNADET cases (low-grade neoplasia, n = 41; high-grade neoplasia, n = 44; white-light images, n = 317) from Yamaguchi University were used to train the AI classifier. Validation was performed using a test set of 69 lesions (low-grade neoplasia, n = 31; high-grade neoplasia, n =38; images, n = 229) from three institutions. Three expert endoscopists also evaluated the test set using an endoscopic scoring system for comparison.
RESULTS: The AI classifier achieved 78.3% accuracy, 92.1% sensitivity, 61.3% specificity, 74.5% positive predictive value (PPV), 86.4% negative predictive value (NPV), and an F1 measure of 0.824. In contrast, endoscopist diagnosis yielded 58.0% accuracy, 50.0% sensitivity, 67.7% specificity, 65.5% PPV, 52.5% NPV, and an F1 measure of 0.567. Diagnostic accuracy and sensitivity of the AI was significantly higher than that of the endoscopists ( P <0.05).
CONCLUSIONS: The AI classifier demonstrated higher sensitivity and overall diagnostic performance in comparison to endoscopists in differentiating high-grade neoplasia, suggesting its potential utility in guiding treatment decisions.