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

Shi, Yanting; Wei, Ning; Wang, Kunhong; Tao, Tao; Feng, Yu

doi:10.3389/fmed.2023.1134980

Cited by 7 publications

(3 citation statements)

References 51 publications

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“…algorithms for Barrett's esophagus [15], chronic atrophic gastritis [16], esophageal and gastric cancer [17], and for the small bowel, there is AI assistance for the detection of bleeding on capsule endoscopy software. Not least, we have significant data on AI for liver disease-hepatocellular carcinoma, liver fibrosis, and pancreatic pathology-both benign (acute pancreatitis) and malignant (pancreatic cancer) [17].…”

Section: Methodsmentioning

confidence: 99%

“…In addition to the use in colorectal cancer screening programs, in the lower gastrointestinal tract, there are also AI applications for inflammatory bowel disease, both Crohn's disease and ulcerative colitis. Concerning the upper gastrointestinal tract, there are validated algorithms for Barrett's esophagus [15], chronic atrophic gastritis [16], esophageal and gastric cancer [17], and for the small bowel, there is AI assistance for the detection of bleeding on capsule endoscopy software. Not least, we have significant data on AI for liver disease-hepatocellular carcinoma, liver fibrosis, and pancreatic pathology-both benign (acute pancreatitis) and malignant (pancreatic cancer) [17].…”

Section: Introductionmentioning

confidence: 99%

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Computer-Based Diagnosis of Celiac Disease by Quantitative Processing of Duodenal Endoscopy Images

Molder,

Balaban,

Molder

et al. 2023

Diagnostics

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Celiac disease (CD) is a lifelong chronic autoimmune systemic disease that primarily affects the small bowel of genetically susceptible individuals. The diagnostics of adult CD currently rely on specific serology and the histological assessment of duodenal mucosa on samples taken by upper digestive endoscopy. Because of several pitfalls associated with duodenal biopsy sampling and histopathology, and considering the pediatric no-biopsy diagnostic criteria, a biopsy-avoiding strategy has been proposed for adult CD diagnosis also. Several endoscopic changes have been reported in the duodenum of CD patients, as markers of villous atrophy (VA), with good correlation with serology. In this setting, an opportunity lies in the automated detection of these endoscopic markers, during routine endoscopy examinations, as potential case-finding of unsuspected CD. We collected duodenal endoscopy images from 18 CD newly diagnosed CD patients and 16 non-CD controls and applied machine learning (ML) and deep learning (DL) algorithms on image patches for the detection of VA. Using histology as standard, high diagnostic accuracy was seen for all algorithms tested, with the layered convolutional neural network (CNN) having the best performance, with 99.67% sensitivity and 98.07% positive predictive value. In this pilot study, we provide an accurate algorithm for automated detection of mucosal changes associated with VA in CD patients, compared to normally appearing non-atrophic mucosa in non-CD controls, using histology as a reference.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computer-Based Diagnosis of Celiac Disease by Quantitative Processing of Duodenal Endoscopy Images

Molder,

Balaban,

Molder

et al. 2023

Diagnostics

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“…Bang et al [ 30 ] performed a meta-analysis including eight studies that specifically examined the accuracy of AI-assisted endoscopy in the diagnosis of Helicobacter pylori infection. Our previous study [ 31 ] conducted a meta-analysis on the accuracy of AI-assisted endoscopy in diagnosing chronic atrophic gastritis. In this study, we utilized meta-analysis to evaluate the accuracy of AI in diagnosing GIM, explored the main factors affecting AI’s ability, and compared AI performance with that of endoscopists, thereby providing an objective basis for applying AI in clinical diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of artificial intelligence-assisted endoscopy in the diagnosis of gastric intestinal metaplasia: A systematic review and meta-analysis

Li,

Yang,

et al. 2024

PLoS ONE

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Background and aims Gastric intestinal metaplasia is a precancerous disease, and a timely diagnosis is essential to delay or halt cancer progression. Artificial intelligence (AI) has found widespread application in the field of disease diagnosis. This study aimed to conduct a comprehensive evaluation of AI’s diagnostic accuracy in detecting gastric intestinal metaplasia in endoscopy, compare it to endoscopists’ ability, and explore the main factors affecting AI’s performance. Methods The study followed the PRISMA-DTA guidelines, and the PubMed, Embase, Web of Science, Cochrane, and IEEE Xplore databases were searched to include relevant studies published by October 2023. We extracted the key features and experimental data of each study and combined the sensitivity and specificity metrics by meta-analysis. We then compared the diagnostic ability of the AI versus the endoscopists using the same test data. Results Twelve studies with 11,173 patients were included, demonstrating AI models’ efficacy in diagnosing gastric intestinal metaplasia. The meta-analysis yielded a pooled sensitivity of 94% (95% confidence interval: 0.92–0.96) and specificity of 93% (95% confidence interval: 0.89–0.95). The combined area under the receiver operating characteristics curve was 0.97. The results of meta-regression and subgroup analysis showed that factors such as study design, endoscopy type, number of training images, and algorithm had a significant effect on the diagnostic performance of AI. The AI exhibited a higher diagnostic capacity than endoscopists (sensitivity: 95% vs. 79%). Conclusions AI-aided diagnosis of gastric intestinal metaplasia using endoscopy showed high performance and clinical diagnostic value. However, further prospective studies are required to validate these findings.

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Research on drug treatment and the novel signaling pathway of chronic atrophic gastritis

Jia,

Zhao,

et al. 2024

Biomedicine & Pharmacotherapy

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

Cited by 7 publications

References 51 publications

Computer-Based Diagnosis of Celiac Disease by Quantitative Processing of Duodenal Endoscopy Images

Computer-Based Diagnosis of Celiac Disease by Quantitative Processing of Duodenal Endoscopy Images

Accuracy of artificial intelligence-assisted endoscopy in the diagnosis of gastric intestinal metaplasia: A systematic review and meta-analysis

Research on drug treatment and the novel signaling pathway of chronic atrophic gastritis

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