Application of artificial intelligence in endoscopic image analysis for the diagnosis of a gastric cancer pathogen-Helicobacter pylori infection

Combination of artificial intelligence endoscopic diagnosis and Kimura-Takemoto classification determined by endoscopic experts may effectively evaluate the stratification of gastric atrophy in post-eradication status

Background

Since it is difficult for expert endoscopists to diagnose early gastric cancer in post-eradication status, it may be critical to evaluate the stratification of high-risk groups using the advance of gastric atrophy or intestinal metaplasia. We tried to determine whether the combination of endoscopic artificial intelligence (AI) diagnosis for the evaluation of gastric atrophy could be a useful tool in both pre- and post-eradication status.

Methods

270 Helicobacter pylori-positive outpatients (Study I) were enrolled and Study II was planned to compare patients (n = 72) with pre-eradication therapy with post-eradication therapy. Assessment of endoscopic appearance was evaluated by the Kyoto classification and Kimura-Takemoto classification. The trained neural network generated a continuous number between 0 and 1 for gastric atrophy.

Results

There were significant associations between the severity of gastric atrophy determined by AI endoscopic diagnosis and not having a regular arrangement of collecting venules in angle, visibility of vascular pattern, and mucus using Kyoto classification in H. pylori-positive gastritis. There were significant differences (p = 0.037 and p = 0.014) in the severity of gastric atrophy between the high-risk group and low-risk group based on the combination of Kimura-Takemoto classification and endoscopic AI diagnosis in pre- and post-eradication status. The area under the curve values of the severity of gastric atrophy (0.674) determined by the combination of Kimura-Takemoto classification and gastric atrophy determined by AI diagnosis was higher than that determined by Kimura-Takemoto classification alone in post-eradication status.

Conclusion

A combination of gastric atrophy determined by AI endoscopic diagnosis and Kimura-Takemoto classification may be a useful tool for the prediction of high-risk groups in post-eradication status.

The artificial intelligence revolution in gastric cancer management: clinical applications

Nowadays, gastric cancer has become a significant issue in the global cancer burden, and its impact cannot be ignored. The rapid development of artificial intelligence technology is attempting to address this situation, aiming to change the clinical management landscape of gastric cancer fundamentally. In this transformative change, machine learning and deep learning, as two core technologies, play a pivotal role, bringing unprecedented innovations and breakthroughs in the diagnosis, treatment, and prognosis evaluation of gastric cancer. This article comprehensively reviews the latest research status and application of artificial intelligence algorithms in gastric cancer, covering multiple dimensions such as image recognition, pathological analysis, personalized treatment, and prognosis risk assessment. These applications not only significantly improve the sensitivity of gastric cancer risk monitoring, the accuracy of diagnosis, and the precision of survival prognosis but also provide robust data support and a scientific basis for clinical decision-making. The integration of artificial intelligence, from optimizing the diagnosis process and enhancing diagnostic efficiency to promoting the practice of precision medicine, demonstrates its promising prospects for reshaping the treatment model of gastric cancer. Although most of the current AI-based models have not been widely used in clinical practice, with the continuous deepening and expansion of precision medicine, we have reason to believe that a new era of AI-driven gastric cancer care is approaching.

Artificial Intelligence in Endoscopy for Predicting Helicobacter pylori Infection: A Systematic Review and Meta-Analysis

PURPOSE

This meta-analysis aimed to assess the diagnostic performance of artificial intelligence (AI) based on endoscopy for detecting Helicobacter pylori (H. pylori) infection.

METHODS

A comprehensive literature search was conducted across PubMed, Embase, and Web of Science to identify relevant studies published up to January 10, 2025. The selected studies focused on the diagnostic accuracy of AI in detecting H. pylori. A bivariate random-effects model was employed to calculate pooled sensitivity and specificity, both presented with 95% confidence intervals (CIs). Study heterogeneity was assessed using the I2 statistic.

RESULTS

Of 604 studies identified, 16 studies (25,002 images or patients) were included. For the internal validation set, the pooled sensitivity, specificity, and area under the curve (AUC) for detecting H. pylori were 0.91 (95% CI: 0.84-0.95), 0.91 (95% CI: 0.86-0.94), and 0.96 (95% CI: 0.94-0.97), respectively. For the external validation set, the pooled sensitivity, specificity, and AUC were 0.91 (95% CI: 0.86-0.95), 0.94 (95% CI: 0.90-0.97), and 0.98 (95% CI: 0.96-0.99). For junior clinicians, the pooled sensitivity, specificity, and AUC were 0.76 (95% CI: 0.66-0.83), 0.75 (95% CI: 0.70-0.80), and 0.81 (95% CI: 0.77-0.84). For senior clinicians, the pooled sensitivity, specificity, and AUC were 0.81 (95% CI: 0.74-0.86), 0.89 (95% CI: 0.86-0.91), and 0.92 (95% CI: 0.90-0.94).

CONCLUSIONS

Endoscopy-based AI demonstrates higher diagnostic performance compared to both junior and senior endoscopists. However, the high heterogeneity among studies limits the strength of these findings, and further research with external validation datasets is necessary to confirm the results.

Prevalence of Helicobacter pylori infection in the general population in Wuzhou, China: a cross-sectional study

BACKGROUND

Helicobacter pylori (H. pylori) is a global infectious carcinogen. We aimed to evaluate the prevalence of H. pylori infection in the healthcare-utilizing population undergoing physical examinations at a tertiary hospital in Guangxi, China. Furthermore, gastroscopies were performed on selected participants to scrutinize the endoscopic features of H. pylori infection among asymptomatic individuals.

SUBJECTS AND METHODS

This study involved 22,769 participants who underwent H. pylori antibody serology screenings at the hospital between 2020 and 2023. The 14C-urea breath test was employed to determine the current H. pylori infection status of 19,307 individuals. Concurrently, 293 participants underwent gastroscopy to evaluate their endoscopic mucosal abnormalities. The risk correlation and predictive value of endoscopic mucosal traits, Hp infection status, and 14C-urea breath test(14C-UBT) outcomes were investigated in subsequent analyses.

RESULTS

Serum Ure, CagA, and VacA antibodies were detected in 43.3%, 27.4%, and 23.6% of the 22,769 subjects that were screened, respectively. The population exhibited 27.5% and 17.2% positive rates for immune type I and II, respectively. Male participants exhibited lower positive rates of serum antibodies than females. The positive rates and predictive risks of the antibodies increased with age, and the highest positive rates were observed in the 50-60 age subgroup. Based on the outcomes of serological diagnostic techniques, it was observed that the positive rate was significantly higher compared to that of non-serological diagnostic methods, specifically the 14C-UBT results (43.3% versus 14.97%). Among the other cohort (n = 19,307), the 14C-UBT revealed a 14.97% positivity rate correlated with age. The 293 individuals who underwent gastroscopy from 14C-UBT Cohort were found to be at an increased risk of a positive breath test if they exhibited duodenal bulb inflammation, diffuse redness, or mucosal edema during the gastroscopy visit.

CONCLUSION

The prevalence of Helicobacter pylori infection is high among the population of Wuzhou, Guangxi, China. Type I H. pylori strains, distinguished by their enhanced virulence, are predominant in this region. In the framework of this population-based study, age has been identified as an independent risk factor for H. pylori infection. Additionally, distinct mucosal manifestations observed during gastroscopy can facilitate the identification of healthcare-utilizing individuals with active H. pylori infections.

Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging

Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic and disease management capabilities. This narrative review seeks to evaluate the current standing of AI in abdominal imaging, with a focus on recent literature contributions. This work explores the diagnosis and characterization of hepatobiliary, pancreatic, gastric, colonic, and other pathologies. In addition, the role of AI has been observed to help differentiate renal, adrenal, and splenic disorders. Furthermore, workflow optimization strategies and quantitative imaging techniques used for the measurement and characterization of tissue properties, including radiomics and deep learning, are highlighted. An assessment of how these advancements enable more precise diagnosis, tumor description, and body composition evaluation is presented, which ultimately advances the clinical effectiveness and productivity of radiology. Despite the advancements of AI in abdominal imaging, technical, ethical, and legal challenges persist, and these challenges, as well as opportunities for future development, are highlighted.

Exploring the immune escape mechanisms in gastric cancer patients based on the deep AI algorithms and single-cell sequencing analysis

Gastric cancer is a prevalent and deadly malignancy, and the response to immunotherapy varies among patients. This study aimed to develop a prognostic model for gastric cancer patients and investigate immune escape mechanisms using deep machine learning and single-cell sequencing analysis. Data from public databases were analysed, and a prediction model was constructed using 101 algorithms. The high-AIDPS group, characterized by increased AIDPS expression, exhibited worse survival, genomic variations and immune cell infiltration. These patients also showed immunotherapy tolerance. Treatment strategies targeting the high-AIDPS group identified three potential drugs. Additionally, distinct cluster groups and upregulated AIDPS-associated genes were observed in gastric adenocarcinoma cell lines. Inhibition of GHRL expression suppressed cancer cell activity, inhibited M2 polarization in macrophages and reduced invasiveness. Overall, AIDPS plays a critical role in gastric cancer prognosis, genomic variations, immune cell infiltration and immunotherapy response, and targeting GHRL expression holds promise for personalized treatment. These findings contribute to improved clinical management in gastric cancer.

RE.GA.IN.: the Real-world Gastritis Initiative-updating the updates

At the end of the last century, a far-sighted 'working party' held in Sydney, Australia addressed the clinicopathological issues related to gastric inflammatory diseases. A few years later, an international conference held in Houston, Texas, USA critically updated the seminal Sydney classification. In line with these initiatives, Kyoto Global Consensus Report, flanked by the Maastricht-Florence conferences, added new clinical evidence to the gastritis clinicopathological puzzle.The most relevant topics related to the gastric inflammatory diseases have been addressed by the Real-world Gastritis Initiative (RE.GA.IN.), from disease definitions to the clinical diagnosis and prognosis. This paper reports the conclusions of the RE.GA.IN. consensus process, which culminated in Venice in November 2022 after more than 8 months of intense global scientific deliberations. A forum of gastritis scholars from five continents participated in the multidisciplinary RE.GA.IN. consensus. After lively debates on the most controversial aspects of the gastritis spectrum, the RE.GA.IN. Faculty amalgamated complementary knowledge to distil patient-centred, evidence-based statements to assist health professionals in their real-world clinical practice. The sections of this report focus on: the epidemiology of gastritis; Helicobacter pylori as dominant aetiology of environmental gastritis and as the most important determinant of the gastric oncogenetic field; the evolving knowledge on gastric autoimmunity; the clinicopathological relevance of gastric microbiota; the new diagnostic horizons of endoscopy; and the clinical priority of histologically reporting gastritis in terms of staging. The ultimate goal of RE.GA.IN. was and remains the promotion of further improvement in the clinical management of patients with gastritis.

Applications of artificial intelligence in gastroscopy: a narrative review

Gastroscopy, a critical tool for the diagnosis of upper gastrointestinal diseases, has recently incorporated artificial intelligence (AI) technology to alleviate the challenges involved in endoscopic diagnosis of some lesions, thereby enhancing diagnostic accuracy. This narrative review covers the current status of research concerning various applications of AI technology to gastroscopy, then discusses future research directions. By providing this review, we hope to promote the integration of gastroscopy and AI technology, with long-term clinical applications that can assist patients.