Objective:To construct and validate an artificial intelligence diagnostic system based on multi-feature integration for diagnosing gastric whitish neoplastic lesions under white-light endoscopy.Methods:Gastroscopic images from Renmin Hospital of Wuhan University and the Seventh Medical Center of Chinese PLA General Hospital were collected from November 2012 to July 2021. A total of 823 images of gastric whitish lesions from 267 patients were finally selected. Five white-light endoscopic features associated with gastric whitish lesions were selected through a literature search, including lesion location, boundary clarity, surface texture, roundness, and depression status. Images with manually annotated features were used to train machine learning models, with the optimal model selected as the multi-feature fitting diagnostic system, which assigned diagnostic weights to each feature. A conventional deep learning model was trained with the same dataset. The diagnostic performance of the two models were compared, and eight endoscopists of varying expertise were invited to participate in human-machine comparisons.Results:Accuracy, sensitivity, and specificity of the multi-feature fitting diagnostic system were 82.11% (101/123), 78.43% (40/51), and 84.72% (61/72), respectively. Feature weights in descending order were depression (0.71), lesion location (0.11), surface roughness (0.08), boundary clarity (0.06), and subcircular shape (0.04). The diagnostic accuracy of the system was significantly higher than that of non-expert endoscopists (82.11% VS 74.31%, Z=-2.785, P=0.008) and comparable to that of expert endoscopists (82.11% VS 83.20%, Z=-0.696, P=0.700). There was no significant difference in accuracy between the multi-feature fitting diagnostic system and the traditional deep learning model [82.11% (101/123) VS 82.93% (102/123), P=1.000]. Conclusion:The feature-weighted artificial intelligence diagnostic system for gastric whitish neoplastic lesions demonstrates clinically relevant diagnostic accuracy under white-light endoscopy.

Objective:To construct and validate an artificial intelligence diagnostic system based on multi-feature integration for diagnosing gastric whitish neoplastic lesions under white-light endoscopy.Methods:Gastroscopic images from Renmin Hospital of Wuhan University and the Seventh Medical Center of Chinese PLA General Hospital were collected from November 2012 to July 2021. A total of 823 images of gastric whitish lesions from 267 patients were finally selected. Five white-light endoscopic features associated with gastric whitish lesions were selected through a literature search, including lesion location, boundary clarity, surface texture, roundness, and depression status. Images with manually annotated features were used to train machine learning models, with the optimal model selected as the multi-feature fitting diagnostic system, which assigned diagnostic weights to each feature. A conventional deep learning model was trained with the same dataset. The diagnostic performance of the two models were compared, and eight endoscopists of varying expertise were invited to participate in human-machine comparisons.Results:Accuracy, sensitivity, and specificity of the multi-feature fitting diagnostic system were 82.11% (101/123), 78.43% (40/51), and 84.72% (61/72), respectively. Feature weights in descending order were depression (0.71), lesion location (0.11), surface roughness (0.08), boundary clarity (0.06), and subcircular shape (0.04). The diagnostic accuracy of the system was significantly higher than that of non-expert endoscopists (82.11% VS 74.31%, Z=-2.785, P=0.008) and comparable to that of expert endoscopists (82.11% VS 83.20%, Z=-0.696, P=0.700). There was no significant difference in accuracy between the multi-feature fitting diagnostic system and the traditional deep learning model [82.11% (101/123) VS 82.93% (102/123), P=1.000]. Conclusion:The feature-weighted artificial intelligence diagnostic system for gastric whitish neoplastic lesions demonstrates clinically relevant diagnostic accuracy under white-light endoscopy.

Objective:To construct an intelligent endoscopic image analysis system that could monitor the blind spot of the upper gastrointestinal tract, and to test its performance.Methods:A total of 87 167 upper gastrointestinal endoscopy images (dataset 1) including 75 551 for training and 11 616 for testing, and a total of 2 414 pharyngeal images (dataset 2) including 2 233 for training and 181 for testing were retrospectively collected from the Digestive Endoscopy Center of Renmin Hospital of Wuhan University between 2016 to 2020. A 27-category-classification model for blind spot monitoring in the upper gastrointestinal tract (model 1, which distinguished 27 anatomical sites such as the pharynx, esophagus, and stomach) and a 5-category-classification model for blind spot monitoring in the pharynx (model 2, which distinguished palate, posterior pharyngeal wall, larynx, left and right pyriform sinuses) were constructed. The above models were trained and tested based on dataset 1 and 2, respectively, and trained based on the EfficientNet-B4, ResNet50 and VGG16 models of the keras framework. Thirty complete upper gastrointestinal endoscopy videos were retrospectively collected from the Digestive Endoscopy Center of Renmin Hospital of Wuhan University in 2021 to test model 2 blind spot monitoring performance.Results:The cross-sectional comparison results of the accuracy of model 1 in identifying 27 anatomical sites of the upper gastrointestinal tract in images showed that the mean accuracy of EfficientNet-B4, ResNet50, and VGG16 were 90.90%, 90.24%, and 89.22%, respectively, with the EfficientNet-B4 model performance the best, and the accuracy of EfficientNet-B4 model for each site ranged from 80.49% to 97.80%. The cross-sectional comparison results of the accuracy of model 2 in identifying the 5 anatomical sites of the pharynx in the images showed that the mean accuracy of EfficientNet-B4, ResNet50, and VGG16 were 99.40%, 98.56%, and 97.01%, respectively, in which the EfficientNet-B4 model had the best performance, and the accuracy of EfficientNet-B4 model for each site ranged from 96.15% to 100.00%. The overall accuracy of model 2 in identifying the 5 anatomical sites of the pharynx in the video was 97.33% (146/150).Conclusion:The intelligent endoscopic image analysis system based on deep learning can monitor blind spots in the upper gastrointestinal tract, coupled with pharyngeal blind spot monitoring and esophagogastroduodenal blind spot monitoring functions. The system shows high accuracy in both images and videos, which is expected to have a potential role in clinical practice and assisting endoscopists to achieve full observation of the upper gastrointestinal tract.