BMC gastroenterology · 2026
Development and validation of a real-time AI model for differentiating benign and malignant gastric ulcers : a multicenter retrospective study.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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.
Tan Yibo, Wu Yongjun, Yang Mei, Li Yan, Bi Xiaofei, He Song, Zhou Zhihang, Lu Junyu
Аннотация
AIM: To develop and validate a deep learning-based AI system for the dynamic, real-time differentiation of benign and malignant gastric ulcers during endoscopy, with the goal of enhancing diagnostic precision.
METHODS: This was a multicenter, retrospective study collecting endoscopic images and videos from four tertiary hospitals in China. An improved YOLOv8 model, incorporating an illumination attention module, was developed for real-time instance segmentation and classification. The dataset comprised 9,820 benign ulcer images, 1,727 malignant ulcer images, and 15,791 normal mucosa images, split into training, testing, and validation sets at an 8:1:1 ratio. Performance was evaluated based on precision, recall, specificity, and processing latency.
RESULTS: On the validation set, the AI model achieved an overall precision, recall, and specificity of 0.91, 0.91, and 0.95, respectively. For malignant ulcer recognition specifically, the precision, recall, and specificity were 0.90, 0.91, and 0.99. The model demonstrated strong real-time performance with a latency of 8.84 ms per frame and a processing speed of 113 frames per second.
CONCLUSION: The developed AI model enables accurate, real-time discrimination between benign and malignant gastric ulcers during endoscopy. It holds potential to augment clinical decision-making, standardize diagnostic quality, and optimize biopsy strategies.
METHODS: This was a multicenter, retrospective study collecting endoscopic images and videos from four tertiary hospitals in China. An improved YOLOv8 model, incorporating an illumination attention module, was developed for real-time instance segmentation and classification. The dataset comprised 9,820 benign ulcer images, 1,727 malignant ulcer images, and 15,791 normal mucosa images, split into training, testing, and validation sets at an 8:1:1 ratio. Performance was evaluated based on precision, recall, specificity, and processing latency.
RESULTS: On the validation set, the AI model achieved an overall precision, recall, and specificity of 0.91, 0.91, and 0.95, respectively. For malignant ulcer recognition specifically, the precision, recall, and specificity were 0.90, 0.91, and 0.99. The model demonstrated strong real-time performance with a latency of 8.84 ms per frame and a processing speed of 113 frames per second.
CONCLUSION: The developed AI model enables accurate, real-time discrimination between benign and malignant gastric ulcers during endoscopy. It holds potential to augment clinical decision-making, standardize diagnostic quality, and optimize biopsy strategies.