Development and Validation of an Automatic Image-Recognition Endoscopic Report Generation System: A Multicenter Study. Clin Transl Gastroenterol. 2020;12:e00282. [PMID: 33395075 PMCID: PMC7771723 DOI: 10.14309/ctg.0000000000000282]

Diagnostic value of artificial intelligence-assisted endoscopy for chronic atrophic gastritis: a systematic review and meta-analysis

Background and aims

The diagnosis of chronic atrophic gastritis (CAG) under normal white-light endoscopy depends on the endoscopist's experience and is not ideal. Artificial intelligence (AI) is increasingly used to diagnose diseases with good results. This review aimed to evaluate the accuracy of AI-assisted diagnosis of CAG through a meta-analysis.

Methods

We conducted a comprehensive literature search of four databases: PubMed, Embase, Web of Science, and the Cochrane Library. Studies published by November 21, 2022, on AI diagnosis CAG with endoscopic images or videos were included. We assessed the diagnostic performance of AI using meta-analysis, explored the sources of heterogeneity through subgroup analysis and meta-regression, and compared the accuracy of AI and endoscopists in diagnosing CAG.

Results

Eight studies that included a total of 25,216 patients of interest, 84,678 image training set images, and 10,937 test set images/videos were included. The results of the meta-analysis showed that the sensitivity of AI in identifying CAG was 94% (95% confidence interval [CI]: 0.88-0.97, I² = 96.2%), the specificity was 96% (95% CI: 0.88-0.98, I² = 98.04%), and the area under the summary receiver operating characteristic curve was 0.98 (95% CI: 0.96-0.99). The accuracy of AI in diagnosing CAG was significantly higher than that of endoscopists.

Conclusions

AI-assisted diagnosis of CAG in endoscopy has high accuracy and clinical diagnostic value.

Systematic review registration

http://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42023391853.

Application of deep learning in the real-time diagnosis of gastric lesion based on magnifying optical enhancement videos

Background and aim

Magnifying image-enhanced endoscopy was demonstrated to have higher diagnostic accuracy than white-light endoscopy. However, differentiating early gastric cancers (EGCs) from benign lesions is difficult for beginners. We aimed to determine whether the computer-aided model for the diagnosis of gastric lesions can be applied to videos rather than still images.

Methods

A total of 719 magnifying optical enhancement images of EGCs, 1,490 optical enhancement images of the benign gastric lesions, and 1,514 images of background mucosa were retrospectively collected to train and develop a computer-aided diagnostic model. Subsequently, 101 video segments and 671 independent images were used for validation, and error frames were labeled to retrain the model. Finally, a total of 117 unaltered full-length videos were utilized to test the model and compared with those diagnostic results made by independent endoscopists.

Results

Except for atrophy combined with intestinal metaplasia (IM) and low-grade neoplasia, the diagnostic accuracy was 0.90 (85/94). The sensitivity, specificity, PLR, NLR, and overall accuracy of the model to distinguish EGC from non-cancerous lesions were 0.91 (48/53), 0.78 (50/64), 4.14, 0.12, and 0.84 (98/117), respectively. No significant difference was observed in the overall diagnostic accuracy between the computer-aided model and experts. A good level of kappa values was found between the model and experts, which meant that the kappa value was 0.63.

Conclusions

The performance of the computer-aided model for the diagnosis of EGC is comparable to that of experts. Magnifying the optical enhancement model alone may not be able to deal with all lesions in the stomach, especially when near the focus on severe atrophy with IM. These results warrant further validation in prospective studies with more patients. A ClinicalTrials.gov registration was obtained (identifier number: NCT04563416).

Clinical Trial Registration

ClinicalTrials.gov, identifier NCT04563416.

Diagnostic accuracy of convolutional neural network-based endoscopic image analysis in diagnosing gastric cancer and predicting its invasion depth: a systematic review and meta-analysis

BACKGROUND AND AIMS

This study aimed to evaluate the accuracy and effectiveness of the convolutional neural network (CNN) in diagnosing gastric cancer and predicting the invasion depth of gastric cancer and to compare the performance of the CNN with that of endoscopists.

METHODS

PubMed, Embase, Web of Science, and gray literature were searched until July 23, 2021 for studies that assessed the diagnostic accuracy of CNN-assisted examinations for gastric cancer or the invasion depth of gastric cancer. Studies meeting inclusion criteria were included in the systematic review and meta-analysis.

RESULTS

Seventeen studies comprising 51,446 images and 174 videos of 5539 patients were included. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), and area under the curve (AUC) of the CNN for diagnosing gastric cancer were 89% (95% confidence interval [CI], 85-93), 93% (95% CI, 88-97), 13.4 (95% CI, 7.3-25.5), .11 (95% CI, .07-.17), and .94 (95% CI, .91-.98), respectively. The performance of the CNN in diagnosing gastric cancer was not significantly different from that of expert endoscopists (.95 vs .90, P > .05) and was better than that of overall endoscopists (experts and nonexperts) (.95 vs .87, P < .05). The pooled sensitivity, specificity, LR+, LR-, and AUC of the CNN for predicting the invasion depth of gastric cancer were 82% (95% CI, 78-85), 90% (95% CI, 82-95), 8.4 (95% CI, 4.2-16.8), .20 (95% CI, .16-.26), and .90 (95% CI, .87-.93), respectively.

CONCLUSIONS

The CNN is highly accurate in diagnosing gastric cancer and predicting the invasion depth of gastric cancer. The performance of the CNN in diagnosing gastric cancer is not significantly different from that of expert endoscopists. Studies of the real-time performance of the CNN for gastric cancer diagnosis are needed to confirm these findings.

Recognition and Optimization Analysis of Urban Public Sports Facilities Based on Intelligent Image Processing

In the utilization of urban public facilities, it is found that the number of people under 18 years who exercise accounts for 29.5% of the total number of people surveyed, 32.8% between 18 and 65 years, and 37.7% over 65 years. The elderly have become the main population of public facilities, and the aging of cities is becoming more and more obvious. Strengthening the construction and development of urban public facilities has become the main work of current urban construction, and planning public facilities can effectively alleviate the pressure of urban public facilities. Through image recognition to promote urban sports public service, we improve the management efficiency of urban sports public service, facilitate residents' sports, and improve residents' satisfaction and happiness index. Through image recognition to manage portraits and objects, the safety of residents' sports and sports facilities is guaranteed, and the management efficiency is improved. The experimental results show that R-CNN, FAST R-CNN, and Faster R-CNN in urban public facilities can be intelligently recognized by image recognition technology for comparison. Faster R-CNN has good accuracy and low average time. Finally, the study analyzes the service cost of public facilities, compared with traditional public services, with the application of public services under image recognition, so as to guide different groups of people to make full use of public service facilities to improve their quality of life and realize the good behavior of the national movement.

Application of deep learning in image recognition and diagnosis of gastric cancer

In recent years, artificial intelligence has been extensively applied in the diagnosis of gastric cancer based on medical imaging. In particular, using deep learning as one of the mainstream approaches in image processing has made remarkable progress. In this paper, we also provide a comprehensive literature survey using four electronic databases, PubMed, EMBASE, Web of Science, and Cochrane. The literature search is performed until November 2020. This article provides a summary of the existing algorithm of image recognition, reviews the available datasets used in gastric cancer diagnosis and the current trends in applications of deep learning theory in image recognition of gastric cancer. covers the theory of deep learning on endoscopic image recognition. We further evaluate the advantages and disadvantages of the current algorithms and summarize the characteristics of the existing image datasets, then combined with the latest progress in deep learning theory, and propose suggestions on the applications of optimization algorithms. Based on the existing research and application, the label, quantity, size, resolutions, and other aspects of the image dataset are also discussed. The future developments of this field are analyzed from two perspectives including algorithm optimization and data support, aiming to improve the diagnosis accuracy and reduce the risk of misdiagnosis.