Deep learning model can improve the diagnosis rate of endoscopic chronic atrophic gastritis: a prospective cohort study

Update understanding on diagnosis and histopathological examination of atrophic gastritis: A review

Chronic atrophic gastritis (CAG) is a complex syndrome in which long-term chronic inflammatory stimulation causes gland atrophy in the gastric mucosa, reducing the stomach's ability to secrete gastric juice and pepsin, and interfering with its normal physiological function. Multiple pathogenic factors contribute to CAG incidence, the most common being Helicobacter pylori infection and the immune reactions resulting from gastric autoimmunity. Furthermore, CAG has a broad spectrum of clinical manifestations, including gastroenterology and extra-intestinal symptoms and signs, such as hematology, neurology, and oncology. Therefore, the initial CAG evaluation should involve the examination of clinical and serological indicators, as well as diagnosis confirmation via gastroscopy and histopathology if necessary. Depending on the severity and scope of atrophy affecting the gastric mucosa, a histologic staging system (Operative Link for Gastritis Assessment or Operative Link on Gastritis intestinal metaplasia) could also be employed. Moreover, chronic gastritis has a higher risk of progressing to gastric cancer (GC). In this regard, early diagnosis, treatment, and regular testing could reduce the risk of GC in CAG patients. However, the optimal interval for endoscopic monitoring in CAG patients remains uncertain, and it should ideally be tailored based on individual risk evaluations and shared decision-making processes. Although there have been many reports on CAG, the precise etiology and histopathological features of the disease, as well as the diagnosis of CAG patients, are yet to be fully elucidated. Consequently, this review offers a detailed account of CAG, including its key clinical aspects, aiming to enhance the overall understanding of the disease.

Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review

Background and Objective: Gastritis represents one of the most prevalent gastrointestinal diseases and has a multifactorial etiology, many forms of manifestation, and various symptoms. Diagnosis of gastritis is made based on clinical, endoscopic, and histological criteria, and although it is a thorough process, many cases are misdiagnosed or overlooked. This systematic review aims to provide an extensive overview of current artificial intelligence (AI) applications in gastritis diagnosis and evaluate the precision of these systems. This evaluation could highlight the role of AI as a helpful and useful tool in facilitating timely and accurate diagnoses, which in turn could improve patient outcomes. Methods: We have conducted an extensive and comprehensive literature search of PubMed, Scopus, and Web of Science, including studies published until July 2024. Results: Despite variations in study design, participant numbers and characteristics, and outcome measures, our observations suggest that implementing an AI automatic diagnostic tool into clinical practice is currently feasible, with the current systems achieving high levels of accuracy, sensitivity, and specificity. Our findings indicate that AI outperformed human experts in most studies, with multiple studies exhibiting an accuracy of over 90% for AI compared to human experts. These results highlight the significant potential of AI to enhance diagnostic accuracy and efficiency in gastroenterology. Conclusions: AI-based technologies can now automatically diagnose using images provided by gastroscopy, digital pathology, and radiology imaging. Deep learning models exhibited high levels of accuracy, sensitivity, and specificity while assessing the diagnosis, staging, and risk of neoplasia for different types of gastritis, results that are superior to those of human experts in most studies.

An artificial intelligence system for chronic atrophic gastritis diagnosis and risk stratification under white light endoscopy

BACKGROUND AND AIMS

The diagnosis and stratification of gastric atrophy (GA) predict patients' gastric cancer progression risk and determine endoscopy surveillance interval. We aimed to construct an artificial intelligence (AI) system for GA endoscopic identification and risk stratification based on the Kimura-Takemoto classification.

METHODS

We constructed the system using two trained models and verified its performance. First, we retrospectively collected 869 images and 119 videos to compare its performance with that of endoscopists in identifying GA. Then, we included original image cases of 102 patients to validate the system for stratifying GA and comparing it with endoscopists with different experiences.

RESULTS

The sensitivity of model 1 was higher than that of endoscopists (92.72% vs. 76.85 %) at image level and also higher than that of experts (94.87% vs. 85.90 %) at video level. The system outperformed experts in stratifying GA (overall accuracy: 81.37 %, 73.04 %, p = 0.045). The accuracy of this system in classifying non-GA, mild GA, moderate GA, and severe GA was 80.00 %, 77.42 %, 83.33 %, and 85.71 %, comparable to that of experts and better than that of seniors and novices.

CONCLUSIONS

We established an expert-level system for GA endoscopic identification and risk stratification. It has great potential for endoscopic assessment and surveillance determinations.

Endoscopic Grading and Sampling of Gastric Precancerous Lesions: A Comprehensive Literature Review

Gastric cancer remains a disease with an ominous prognosis, while early gastric cancer has a good-to-excellent prognosis, with 5-year survival rates of up to 92.6% after successful endoscopic resection. In this context, the accurate identification of patients with established gastric precancerous lesions, namely chronic atrophic gastritis and intestinal metaplasia, is the first step in a stepwise approach to minimize cancer risk. Although current guidelines advocate for the execution of random biopsies to stage the extent and severity of gastritis/intestinal metaplasia, modern biopsy protocols are still imperfect as they have limited reproducibility and are susceptible to sampling error. The advent of novel imaging-enhancing modalities, i.e., high-definition with virtual chromoendoscopy (CE), has revolutionized the inspection of gastric mucosa, leading to an endoscopy-based staging strategy for the management of these premalignant changes in the stomach. Nowadays, the incorporation of CE-targeted biopsies in everyday clinical practice offers not only the robust detection of premalignant lesions but also an improvement in quality, by reducing missed diagnoses along with mean biopsies and, thus, the procedural costs and the environmental footprint. In this review, we summarize the recent evidence regarding the endoscopic grading and sampling of gastric precancerous lesions.

Artificial intelligence for gastric cancer in endoscopy: From diagnostic reasoning to market

Recognition of gastric conditions during endoscopy exams, including gastric cancer, usually requires specialized training and a long learning curve. Besides that, the interobserver variability is frequently high due to the different morphological characteristics of the lesions and grades of mucosal inflammation. In this sense, artificial intelligence tools based on deep learning models have been developed to support physicians to detect, classify, and predict gastric lesions more efficiently. Even though a growing number of studies exists in the literature, there are multiple challenges to bring a model to practice in this field, such as the need for more robust validation studies and regulatory hurdles. Therefore, the aim of this review is to provide a comprehensive assessment of the current use of artificial intelligence applied to endoscopic imaging to evaluate gastric precancerous and cancerous lesions and the barriers to widespread implementation of this technology in clinical routine.

U-Net deep learning model for endoscopic diagnosis of chronic atrophic gastritis and operative link for gastritis assessment staging: a prospective nested case-control study

Background

The operative link for the gastritis assessment (OLGA) system can objectively reflect the stratification of gastric cancer risk in patients with chronic atrophic gastritis (CAG).

Objectives

We developed a real-time video monitoring model for the endoscopic diagnosis of CAG and OLGA staging based on U-Net deep learning (DL). To further validate and improve its performance, we designed a study to evaluate the diagnostic evaluation indices.

Design

A prospective nested case-control study.

Methods

Our cohort consisted of 1306 patients from 31 July 2021 to 31 January 2022. According to the pathological results, patients in the cohort were divided into the CAG group and the chronic non-atrophic gastritis group to evaluate the diagnostic evaluation indices. Each atrophy lesion was automatically labeled and the atrophy severity was assessed by the model. Propensity score matching was used to minimize selection bias.

Results

The diagnostic evaluation indices and the consistency between OLGA staging and pathological diagnosis of the model were superior to those of endoscopists [sensitivity (89.31% versus 67.56%), specificity (90.46% versus 70.23%), positive predictive value (90.35% versus 69.41%), negative predictive value (89.43% versus 68.40%), accuracy rate (89.89% versus 68.89%), Youden index (79.77% versus 37.79%), odd product (79.23 versus 4.91), positive likelihood ratio (9.36 versus 2.27), negative likelihood ratio (0.12 versus 0.46)], areas under the curves (AUC) (95% CI) (0.919 (0.893-0.945) versus 0.749 (0.707-0.792), p < 0.001) and kappa (0.816 versus 0.291)].

Conclusion

Our study demonstrated that the DL model can assist endoscopists in real-time diagnosis of CAG during gastroscopy and synchronous identification of high-risk OLGA stage (OLGA stages III and IV) patients.

Trial registration

ChiCTR2100044458.

Clinical Decision Support System for All Stages of Gastric Carcinogenesis in Real-Time Endoscopy: Model Establishment and Validation Study

BACKGROUND

Our research group previously established a deep-learning-based clinical decision support system (CDSS) for real-time endoscopy-based detection and classification of gastric neoplasms. However, preneoplastic conditions, such as atrophy and intestinal metaplasia (IM) were not taken into account, and there is no established model that classifies all stages of gastric carcinogenesis.

OBJECTIVE

This study aims to build and validate a CDSS for real-time endoscopy for all stages of gastric carcinogenesis, including atrophy and IM.

METHODS

A total of 11,868 endoscopic images were used for training and internal testing. The primary outcomes were lesion classification accuracy (6 classes: advanced gastric cancer, early gastric cancer, dysplasia, atrophy, IM, and normal) and atrophy and IM lesion segmentation rates for the segmentation model. The following tests were carried out to validate the performance of lesion classification accuracy: (1) external testing using 1282 images from another institution and (2) evaluation of the classification accuracy of atrophy and IM in real-world procedures in a prospective manner. To estimate the clinical utility, 2 experienced endoscopists were invited to perform a blind test with the same data set. A CDSS was constructed by combining the established 6-class lesion classification model and the preneoplastic lesion segmentation model with the previously established lesion detection model.

RESULTS

The overall lesion classification accuracy (95% CI) was 90.3% (89%-91.6%) in the internal test. For the performance validation, the CDSS achieved 85.3% (83.4%-97.2%) overall accuracy. The per-class external test accuracies for atrophy and IM were 95.3% (92.6%-98%) and 89.3% (85.4%-93.2%), respectively. CDSS-assisted endoscopy showed an accuracy of 92.1% (88.8%-95.4%) for atrophy and 95.5% (92%-99%) for IM in the real-world application of 522 consecutive screening endoscopies. There was no significant difference in the overall accuracy between the invited endoscopists and established CDSS in the prospective real-clinic evaluation (P=.23). The CDSS demonstrated a segmentation rate of 93.4% (95% CI 92.4%-94.4%) for atrophy or IM lesion segmentation in the internal testing.

CONCLUSIONS

The CDSS achieved high performance in terms of computer-aided diagnosis of all stages of gastric carcinogenesis and demonstrated real-world application potential.

Focusing on Helicobacter pylori infection in the elderly

As a confirmed carcinogen, Helicobacter pylori (H. pylori) is the main cause of inflammatory diseases of the upper digestive tract and even gastric cancer. There is a high prevalence of H. pylori infection among the elderly population, which may cause adverse clinical outcomes. Particularly noteworthy is that guidelines or expert consensus presently available on H. pylori infection overlook the management of the elderly population as a special group. A brief overview of H. pylori in the elderly is as follows. The detection of H. pylori infection can be divided into invasive and non-invasive techniques, and each technique has its advantages and shortcomings. There may be more side effects associated with eradication treatment in elderly individuals, especially for the frail population. Physical conditions and risk-benefit assessments of the elderly should be considered when selecting therapeutic strategies for H. pylori eradication. Unless there are competing factors, elderly patients should receive H. pylori eradication regimens to finally reduce the formation of gastric cancer. In this review, we summarize the latest understanding of H. pylori in the elderly population to provide effective managements and treatment measures.

Kyoto classification of gastritis, virtual chromoendoscopy and artificial intelligence: Where are we going? What do we need?

Chronic gastritis (CG) is a widespread and frequent disease, mainly caused by Helicobacter pylori infection, which is associated with an increased risk of gastric cancer. Virtual chromoendoscopy improves the endoscopic diagnostic efficacy, which is essential to establish the most appropriate therapy and to enable cancer prevention. Artificial intelligence provides algorithms for the diagnosis of gastritis and, in particular, early gastric cancer, but it is not yet used in practice. Thus, technological innovation, through image resolution and processing, optimizes the diagnosis and management of CG and gastric cancer. The endoscopic Kyoto classification of gastritis improves the diagnosis and management of this disease, but through the analysis of the most recent literature, new algorithms can be proposed.

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.

Clinical efficacy of moluodan in the treatment of chronic atrophic gastritis: A protocol for systematic review and meta-analysis

BACKGROUND

Chronic atrophic gastritis (CAG) is an important stage of precancerous lesions of gastric cancer, and also a key period of drug intervention. However, there is still a lack of drugs to maintain the treatment of CAG until the advent of moluodan.

OBJECTIVE

This study was conducted to assess the clinical efficacy of moluodan in the treatment of CAG by meta-analysis and trial sequential analysis.

METHODS

China National Knowledge Infrastructure, China Biology Medicine, VIP, Wanfang, Embase, PubMed, the Cochrane Library, and Web of Science databases were searched, all with the time limit from database establishment to July 2022. The published randomized controlled trials of moluodan for CAG were conducted for meta-analysis and trial sequential analysis.

RESULTS

7 studies with a total sample size of 1143 cases were included. Compared to folic acid/vitamins, moluodan alone significantly increased the effective rate of pathological detection (relative risk [RR] = 1.73, 95% confidence interval [95%CI] = [1.48,2.02], P < .00001), and moluodan in combination with folic acid/vitamins significantly increased the effective rates of pathological detection (RR = 1.37, 95%CI = [1.23,1.52], P < .00001), gastroscopy (RR = 1.37, 95%CI = [1.18,1.60], P < .0001) and symptoms (RR = 1.25, 95%CI = [1.13,1.38], P < .0001). Harbord regression showed no publication bias (P = .22). Quality of evidence evaluation demonstrated moderate quality of evidence for all indicators.

CONCLUSIONS

Moluodan can improve the effective rates of pathological examination, gastroscopy and symptoms in patients with CAG, and play a role in slowing down the disease progression and reducing clinical symptoms. It may be a potential drug for the treatment of CAG and has the value of further exploration.

Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions

Recent developments in deep learning have enabled data-driven algorithms that can reach human-level performance and beyond. The development and deployment of medical image analysis methods have several challenges, including data heterogeneity due to population diversity and different device manufacturers. In addition, more input from experts is required for a reliable method development process. While the exponential growth in clinical imaging data has enabled deep learning to flourish, data heterogeneity, multi-modality, and rare or inconspicuous disease cases still need to be explored. Endoscopy being highly operator-dependent with grim clinical outcomes in some disease cases, reliable and accurate automated system guidance can improve patient care. Most designed methods must be more generalisable to the unseen target data, patient population variability, and variable disease appearances. The paper reviews recent works on endoscopic image analysis with artificial intelligence (AI) and emphasises the current unmatched needs in this field. Finally, it outlines the future directions for clinically relevant complex AI solutions to improve patient outcomes.

Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis: a prospective nested case-control study

Background and aims

Chronic atrophic gastritis (CAG) is a precancerous disease that often leads to the development of gastric cancer (GC) and is positively correlated with GC morbidity. However, the sensitivity of the endoscopic diagnosis of CAG is only 42%. Therefore, we developed a real-time video monitoring model for endoscopic diagnosis of CAG based on U-Net deep learning (DL) and conducted a prospective nested case–control study to evaluate the diagnostic evaluation indices of the model and its consistency with pathological diagnosis.

Methods

Our cohort consisted of 1539 patients undergoing gastroscopy from December 1, 2020, to July 1, 2021. Based on pathological diagnosis, patients in the cohort were divided into the CAG group or the chronic nonatrophic gastritis (CNAG) group, and we assessed the diagnostic evaluation indices of this model and its consistency with pathological diagnosis after propensity score matching (PSM) to minimize selection bias in the study.

Results

After matching, the diagnostic evaluation indices and consistency evaluation of the model were better than those of endoscopists [sensitivity (84.02% vs. 62.72%), specificity (97.04% vs. 81.95%), positive predictive value (96.60% vs. 77.66%), negative predictive value (85.86% vs. 68.73%), accuracy rate (90.53% vs. 72.34%), Youden index (81.06% vs. 44.67%), odd product (172.5 vs. 7.64), positive likelihood ratio (28.39 vs. 3.47), negative likelihood ratio (0.16 vs. 0.45), AUC (95% CI) [0.909 (0.884–0.934) vs. 0.740 (0.702–0.778)] and Kappa (0.852 vs. 0.558)].

Conclusions

Our prospective nested case–control study proved that the diagnostic evaluation indices and consistency evaluation of the real-time video monitoring model for endoscopic diagnosis of CAG based on U-Net DL were superior to those of endoscopists.

Trial registrationChiCTR2100044458, 18/03/2020.