Tu1107 COMPARISON BETWEEN LIGHT BLUE CREST AND MARGINAL TURBID BAND FOR DIAGNOSIS OF GASTRIC INTESTINAL METAPLASIA USING NARROW BAND IMAGING AND MAGNIFYING ENDOSCOPY

Diagnosis of gastric intestinal metaplasia (GIM) under digital chromoendoscopy presents various markers. The diagnostic yield of each marker has shown variable results. A systematic review and meta-analysis were performed to evaluate the following markers: light blue crest (LBC), marginal turbid band (MTB), groove type (GT), white opaque substance (WOS). Discussion of their joint diagnosis, reliability, assessment of local GIM severity and influencing factors was also provided. PubMed, EMBASE, Cochrane Library and Web of science were searched. The Meta-DiSc and Stata software were used to analyze the data. Fourteen studies were included with a total of 3081 lesions. There were 12 studies for LBC, six for GT, four for MTB and one for WOS, respectively. The pooled results showed that the diagnostic sensitivity and specificity of LBC were 0.79 [95% confidence interval (CI) 0.76–0.81] and 0.95 (95% CI 0.94–0.96). The sensitivity of GT and MTB were 0.49 (95% CI 0.43–0.54) and 0.47 (95% CI 0.40–0.53), and the specificity were 0.92 (95% CI 0.89–0.94) and 0.92 (95% CI 0.89–0.95). The area under the summary receiver operating characteristic curve was 0.9532, 0.7791 and 0.9553 for LBC, GT and MTB, respectively. LBC resulted the most classic marker. Proper combined diagnosis can improve the diagnostic efficiency. Reliability of these markers was acceptable. These markers can help endoscopic to assess the severity of local GIM. Besides intestinal metaplasia, many factors can potentially interfere with the diagnosis.

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Lightweight deep learning model incorporating an attention mechanism and feature fusion for automatic classification of gastric lesions in gastroscopic images

Wang

Chen

Yang

et al. 2023

Biomed. Opt. Express

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Accurate diagnosis of various lesions in the formation stage of gastric cancer is an important problem for doctors. Automatic diagnosis tools based on deep learning can help doctors improve the accuracy of gastric lesion diagnosis. Most of the existing deep learning-based methods have been used to detect a limited number of lesions in the formation stage of gastric cancer, and the classification accuracy needs to be improved. To this end, this study proposed an attention mechanism feature fusion deep learning model with only 14 million (M) parameters. Based on that model, the automatic classification of a wide range of lesions covering the stage of gastric cancer formation was investigated, including non-neoplasm(including gastritis and intestinal metaplasia), low-grade intraepithelial neoplasia, and early gastric cancer (including high-grade intraepithelial neoplasia and early gastric cancer). 4455 magnification endoscopy with narrow-band imaging(ME-NBI) images from 1188 patients were collected to train and test the proposed method. The results of the test dataset showed that compared with the advanced gastric lesions classification method with the best performance (overall accuracy = 94.3%, parameters = 23.9 M), the proposed method achieved both higher overall accuracy and a relatively lightweight model (overall accuracy =95.6%, parameter = 14 M). The accuracy, sensitivity, and specificity of low-grade intraepithelial neoplasia were 94.5%, 93.0%, and 96.5%, respectively, achieving state-of-the-art classification performance. In conclusion, our method has demonstrated its potential in diagnosing various lesions at the stage of gastric cancer formation.

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Tu1107 COMPARISON BETWEEN LIGHT BLUE CREST AND MARGINAL TURBID BAND FOR DIAGNOSIS OF GASTRIC INTESTINAL METAPLASIA USING NARROW BAND IMAGING AND MAGNIFYING ENDOSCOPY

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Intelligent diagnosis of gastric intestinal metaplasia based on convolutional neural network and limited number of endoscopic images

Intelligent diagnosis of gastric intestinal metaplasia based on convolutional neural network and limited number of endoscopic images

Markers of gastric intestinal metaplasia under digital chromoendoscopy: systematic review and meta-analysis

Lightweight deep learning model incorporating an attention mechanism and feature fusion for automatic classification of gastric lesions in gastroscopic images

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