Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection

Linked color imaging and upper gastrointestinal neoplasia

White light imaging (WLI) can sometimes miss early upper gastrointestinal (UGI) neoplasms, particularly minimal changes and flat lesions. Moreover, endoscopic diagnosis of UGI neoplasia is strongly influenced by the condition of the surrounding mucosa. Recently, image-enhanced endoscopy techniques have been developed and used in clinical practice; one of which is linked color imaging (LCI), which has an expanded color range for better recognition of slight differences in mucosal color and enables easy diagnosis and differentiation of noncancerous mucosa from carcinoma. LCI does not require magnified observation and can clearly visualize structures using an ultrathin scope; therefore, it is useful for screening and surveillance endoscopy. LCI is particularly useful for detecting gastric cancer after Helicobacter pylori eradication, which accounts for most gastric cancers currently discovered, and displays malignant areas in orange or orange-red surrounded by intestinal metaplasia in lavender. Data on the use of convolutional neural network and computer-aided diagnosis with LCI for UGI neoplasm detection are currently being collected. Further studies are needed to determine the clinical role of LCI and whether it can replace WLI.

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.

Development and application of an artificial intelligence-assisted endoscopy system for diagnosis of Helicobacter pylori infection: a multicenter randomized controlled study

BACKGROUND

The early diagnosis and treatment of Heliobacter pylori (H.pylori) gastrointestinal infection provide significant benefits to patients. We constructed a convolutional neural network (CNN) model based on an endoscopic system to diagnose H. pylori infection, and then examined the potential benefit of this model to endoscopists in their diagnosis of H. pylori infection.

MATERIALS AND METHODS

A CNN neural network system for endoscopic diagnosis of H.pylori infection was established by collecting 7377 endoscopic images from 639 patients. The accuracy, sensitivity, and specificity were determined. Then, a randomized controlled study was used to compare the accuracy of diagnosis of H. pylori infection by endoscopists who were assisted or unassisted by this CNN model.

RESULTS

The deep CNN model for diagnosis of H. pylori infection had an accuracy of 89.6%, a sensitivity of 90.9%, and a specificity of 88.9%. Relative to the group of endoscopists unassisted by AI, the AI-assisted group had better accuracy (92.8% [194/209; 95%CI: 89.3%, 96.4%] vs. 75.6% [158/209; 95%CI: 69.7%, 81.5%]), sensitivity (91.8% [67/73; 95%CI: 85.3%, 98.2%] vs. 78.6% [44/56; 95%CI: 67.5%, 89.7%]), and specificity (93.4% [127/136; 95%CI: 89.2%, 97.6%] vs. 74.5% [114/153; 95%CI: 67.5%, 81.5%]). All of these differences were statistically significant (P < 0.05).

CONCLUSION

Our AI-assisted system for diagnosis of H. pylori infection has significant ability for diagnostic, and can improve the accuracy of endoscopists in gastroscopic diagnosis.

TRIAL REGISTRATION

This study was approved by the Ethics Committee of Daping Hospital (10/07/2020) (No.89,2020) and was registered with the Chinese Clinical Trial Registration Center (02/09/2020)   ( www.chictr.org.cn ; registration number: ChiCTR2000037801).

Novel endoscopic techniques for the diagnosis of gastric Helicobacter pylori infection: a systematic review and network meta-analysis

Objective

This study aimed to conduct a network meta-analysis to compare the diagnostic efficacy of diverse novel endoscopic techniques for detecting gastric Helicobacter pylori infection.

Methods

From inception to August 2023, literature was systematically searched across Pubmed, Embase, and Web of Science databases. Cochrane's risk of bias tool assessed the methodological quality of the included studies. Data analysis was conducted using the R software, employing a ranking chart to determine the most effective diagnostic method comprehensively. Convergence analysis was performed to assess the stability of the results.

Results

The study encompassed 36 articles comprising 54 observational studies, investigating 14 novel endoscopic techniques and involving 7,230 patients diagnosed with gastric H. pylori infection. Compared with the gold standard, the comprehensive network meta-analysis revealed the superior diagnostic performance of two new endoscopic techniques, Magnifying blue laser imaging endoscopy (M-BLI) and high-definition magnifying endoscopy with i-scan (M-I-SCAN). Specifically, M-BLI demonstrated the highest ranking in both sensitivity (SE) and positive predictive value (PPV), ranking second in negative predictive value (NPV) and fourth in specificity (SP). M-I-SCAN secured the top position in NPV, third in SE and SP, and fifth in PPV.

Conclusion

After thoroughly analyzing the ranking chart, we conclude that M-BLI and M-I-SCAN stand out as the most suitable new endoscopic techniques for diagnosing gastric H. pylori infection.

Systematic review registration

https://inplasy.com/inplasy-2023-11-0051/, identifier INPLASY2023110051.

The Diagnostic Performance of Linked Color Imaging Compared to White Light Imaging in Endoscopic Diagnosis of Helicobacter pylori Infection: A Systematic Review and Meta-Analysis

Background/Aims

Recognizing Helicobacter pylori infection during endoscopy is important because it can lead to the performance of confirmatory testing. Linked color imaging (LCI) is an image enhancement technique that can improve the detection of gastrointestinal lesions. The purpose of this study was to compare LCI to conventional white light imaging (WLI) in the endoscopic diagnosis of H. pylori infection.

Methods

We conducted a comprehensive literature search using PubMed, Embase, and the Cochrane Library. All studies evaluating the diagnostic performance of LCI or WLI in the endoscopic diagnosis of H. pylori were eligible. Studies on magnifying endoscopy, chromoendoscopy, and artificial intelligence were excluded.

Results

Thirty-four studies were included in this meta-analysis, of which 32 reported the performance of WLI and eight reported the performance of LCI in diagnosing H. pylori infection. The pooled sensitivity and specificity of WLI in the diagnosis of H. pylori infection were 0.528 (95% confidence interval [CI], 0.517 to 0.540) and 0.821 (95% CI, 0.811 to 0.830), respectively. The pooled sensitivity and specificity of LCI in the diagnosis of H. pylori were 0.816 (95% CI, 0.790 to 0.841) and 0.868 (95% CI, 0.850 to 0.884), respectively. The pooled diagnostic odds ratios of WLI and LCI were 15.447 (95% CI, 8.225 to 29.013) and 31.838 (95% CI, 15.576 to 65.078), respectively. The areas under the summary receiver operating characteristic curves of WLI and LCI were 0.870 and 0.911, respectively.

Conclusions

LCI showed higher sensitivity in the endoscopic diagnosis of H. pylori infection than standard WLI.

Development of an Artificial Intelligence Diagnostic System Using Linked Color Imaging for Barrett's Esophagus

Background: Barrett's esophagus and esophageal adenocarcinoma cases are increasing as gastroesophageal reflux disease increases. Using artificial intelligence (AI) and linked color imaging (LCI), our aim was to establish a method of diagnosis for short-segment Barrett's esophagus (SSBE). Methods: We retrospectively selected 624 consecutive patients in total at our hospital, treated between May 2017 and March 2020, who experienced an esophagogastroduodenoscopy with white light imaging (WLI) and LCI. Images were randomly chosen as data for learning from WLI: 542 (SSBE+/- 348/194) of 696 (SSBE+/- 444/252); and LCI: 643 (SSBE+/- 446/197) of 805 (SSBE+/- 543/262). Using a Vision Transformer (Vit-B/16-384) to diagnose SSBE, we established two AI systems for WLI and LCI. Finally, 126 WLI (SSBE+/- 77/49) and 137 LCI (SSBE+/- 81/56) images were used for verification purposes. The accuracy of six endoscopists in making diagnoses was compared to that of AI. Results: Study participants were 68.2 ± 12.3 years, M/F 330/294, SSBE+/- 409/215. The accuracy/sensitivity/specificity (%) of AI were 84.1/89.6/75.5 for WLI and 90.5/90.1/91.1/for LCI, and those of experts and trainees were 88.6/88.7/88.4, 85.7/87.0/83.7 for WLI and 93.4/92.6/94.6, 84.7/88.1/79.8 for LCI, respectively. Conclusions: Using AI to diagnose SSBE was similar in accuracy to using a specialist. Our finding may aid the diagnosis of SSBE in the clinic.

L-Menthol for Color Difference Change Between Early Gastric Cancer and Surrounding Mucosa: A Prospective Study

BACKGROUND

L-Menthol sprayed on early gastric cancer (EGC) has been reported to improve the visibility of the lesion. However, its impact when used in combination with novel image-enhanced endoscopy has not been investigated.

AIM

This study aimed to evaluate the visual effect of spraying L-menthol on EGC under linked color imaging (LCI).

METHODS

This open-label, single-arm, prospective study investigated the color difference between EGC and the surrounding mucosa (ΔEG) before and after spraying L-menthol. The primary endpoint was the percentage of lesions with ΔEG ≥ 5 on LCI. The percentage of lesions with ΔEG  ≥ 5 on white light imaging (WLI) and blue laser imaging (BLI), ΔEG before and after spraying L-menthol, and percentage of lesions with increased ΔEG after spraying L-menthol constituted the secondary endpoints.

RESULTS

Sixty patients were included in the final analysis. 100% lesions had ΔEG ≥ 5, both before and after spraying L-menthol on LCI, with similar results observed in WLI as well as BLI. The median ΔEG on LCI, WLI, and BLI increased after spraying L-menthol (LCI: 16.9 vs. 21.5, p < 0.01; WLI: 10.4 vs. 13.4, p < 0.01; BLI; 12.1 vs. 15.7, before and after, respectively, p < 0.01); and LCI demonstrated the highest percentage of lesions with increased ΔEG (LCI, WLI, and BLI: 98.3%, 81.7%, and 76.7%, respectively, p < 0.01).

CONCLUSION

Although spraying L-menthol did not improve the visibility of EGC under LCI observation, a significant increase in ΔEG was observed in LCI (jRCTs 021200027).

Diagnostic usefulness of deep learning methods for Helicobacter pylori infection using esophagogastroduodenoscopy images

Background and Aims

We aimed to assess the diagnostic potential of deep convolutional neural networks (DCNNs) for detecting Helicobacter pylori infection in patients who underwent esophagogastroduodenoscopy and Campylobacter-like organism tests.

Methods

We categorized a total of 13,071 images of various gastric sub-areas and employed five pretrained DCNN architectures: ResNet-101, Xception, Inception-v3, InceptionResnet-v2, and DenseNet-201. Additionally, we created an ensemble model by combining the output probabilities of the best models. We used images of different sub-areas of the stomach for training and evaluated the performance of our models. The diagnostic metrics assessed included area under the curve (AUC), specificity, accuracy, positive predictive value, and negative predictive value.

Results

When training included images from all sub-areas of the stomach, our ensemble model demonstrated the highest AUC (0.867), with specificity at 78.44%, accuracy at 80.28%, positive predictive value at 82.66%, and negative predictive value at 77.37%. Significant differences were observed in AUC between the ensemble model and the individual DCNN models. When training utilized images from each sub-area separately, the AUC values for the antrum, cardia and fundus, lower body greater curvature and lesser curvature, and upper body greater curvature and lesser curvature regions were 0.842, 0.826, 0.718, and 0.858, respectively, when the ensemble model was used.

Conclusions

Our study demonstrates that the DCNN model, designed for automated image analysis, holds promise for the evaluation and diagnosis of Helicobacter pylori infection.

Advances on diagnosis of Helicobacter pylori infections

The association of Helicobacter pylori to several gastric diseases, such as chronic gastritis, peptic ulcer disease, and gastric cancer, and its high prevalence worldwide, raised the necessity to use methods for a proper and fast diagnosis and monitoring the pathogen eradication. Available diagnostic methods can be classified as invasive or non-invasive, and the selection of the best relies on the clinical condition of the patient, as well as on the sensitivity, specificity, and accessibility of the diagnostic test. This review summarises all diagnostic methods currently available, including the invasive methods: endoscopy, histology, culture, and molecular methods, and the rapid urease test (RUT), as well as the non-invasive methods urea breath test (UBT), serological assays, biosensors, and microfluidic devices and the stool antigen test (SAT). Moreover, it lists the diagnostic advantages and limitations, as well as the main advances for each methodology. In the end, research on the development of new diagnostic methods, such as bacteriophage-based H. pylori diagnostic tools, is also discussed.

The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy

Recently, there has been a growing interest in the application of artificial intelligence (AI) in medicine, especially in specialties where visualization methods are applied. AI is defined as a computer's ability to achieve human cognitive performance, which is accomplished through enabling computer "learning". This can be conducted in two ways, as machine learning and deep learning. Deep learning is a complex learning system involving the application of artificial neural networks, whose algorithms imitate the human form of learning. Upper gastrointestinal endoscopy allows examination of the esophagus, stomach and duodenum. In addition to the quality of endoscopic equipment and patient preparation, the performance of upper endoscopy depends on the experience and knowledge of the endoscopist. The application of artificial intelligence in endoscopy refers to computer-aided detection and the more complex computer-aided diagnosis. The application of AI in upper endoscopy is aimed at improving the detection of premalignant and malignant lesions, with special attention on the early detection of dysplasia in Barrett's esophagus, the early detection of esophageal and stomach cancer and the detection of H. pylori infection. Artificial intelligence reduces the workload of endoscopists, is not influenced by human factors and increases the diagnostic accuracy and quality of endoscopic methods.

Role of linked color imaging for upper gastrointestinal disease: present and future

Techniques for upper gastrointestinal endoscopy are advancing to facilitate lesion detection and improve prognosis. However, most early tumors in the upper gastrointestinal tract exhibit subtle color changes or morphological features that are difficult to detect using white light imaging. Linked color imaging (LCI) has been developed to overcome these shortcomings; it expands or reduces color information to clarify color differences, thereby facilitating the detection and observation of lesions. This article summarizes the characteristics of LCI and advances in LCI-related research in the upper gastrointestinal tract field.

Development and validation of a convolutional neural network model for diagnosing Helicobacter pylori infections with endoscopic images: a multicenter study

BACKGROUND AND AIMS

Insufficient validation limits the generalizability of deep learning in diagnosing Helicobacter pylori infection with endoscopic images. The aim of this study was to develop a deep learning model for the diagnosis of H pylori infection using endoscopic images and validate the model with internal and external datasets.

METHODS

A convolutional neural network (CNN) model was developed based on a training dataset comprising 13,403 endoscopic images from 952 patients who underwent endoscopy at Seoul National University Hospital Gangnam Center. Internal validation was performed using a separate dataset comprised of images of 411 individuals of Korean descent and 131 of non-Korean descent. External validation was performed with the images of 160 patients in Gangnam Severance Hospital. Gradient-weighted class activation mapping was performed to visually explain the model.

RESULTS

In predicting H pylori ever-infected status, the sensitivity, specificity, and accuracy of internal validation for people of Korean descent were .96 (95% confidence interval [CI], .93-.98), .90 (95% CI, .85-.95), and .94 (95% CI, .91-.96), respectively. In the internal validation for people of non-Korean descent, the sensitivity, specificity, and accuracy in predicting H pylori ever-infected status were .92 (95% CI, .86-.98), .79 (95% CI, .67-.91), and .88 (95% CI, .82-.93), respectively. In the external validation cohort, sensitivity, specificity, and accuracy were .86 (95% CI, .80-.93), .88 (95% CI, .79-.96), and .87 (95% CI, .82-.92), respectively, when performing 2-group categorization. Gradient-weighted class activation mapping showed that the CNN model captured the characteristic findings of each group.

CONCLUSIONS

This CNN model for diagnosing H pylori infection showed good overall performance in internal and external validation datasets, particularly in categorizing patients into the never- versus ever-infected groups.

Assessment of Helicobacter pylori infection by deep learning based on endoscopic videos in real time

BACKGROUND AND AIMS

Endoscopic assessment of Helicobacter pylori infection is a simple and effective method. Here, we aimed to develop a deep learning-based system named Intelligent Detection Endoscopic Assistant-Helicobacter pylori (IDEA-HP) to assess H. pylori infection by using endoscopic videos in real time.

METHODS

Endoscopic data were retrospectively obtained from Zhejiang Cancer Hospital (ZJCH) for the development, validation, and testing of the system. Stored videos from ZJCH were used for assessing and comparing the performance of IDEA-HP with that of endoscopists. Prospective consecutive patients undergoing esophagogastroduodenoscopy were enrolled to assess the applicability of clinical practice. The urea breath test was used as the gold standard for diagnosing H. pylori infection.

RESULTS

In 100 videos, IDEA-HP achieved a similar overall accuracy of assessing H. pylori infection to that of experts (84.0% vs. 83.6% [P = 0.729]). Nevertheless, the diagnostic accuracy (84.0% vs. 74.0% [P<0.001]) and sensitivity (82.0% vs. 67.2% [P<0.001]) of IDEA-HP were significantly higher than those of the beginners. In 191 prospective consecutive patients, IDEA-HP achieved accuracy, sensitivity, and specificity of 85.3% (95% CI: 79.0%-89.3%), 83.3% (95% CI: 72.8%-90.5%), and 85.8% (95% CI: 77.7%-91.4%), respectively.

CONCLUSIONS

Our results show that IDEA-HP has great potential for assisting endoscopists in assessing H. pylori infection status during actual clinical work.

Linked Color Imaging for Stomach

Image-enhanced endoscopy (IEE) plays an important role in the detection and further examination of gastritis and early gastric cancer (EGC). Linked color imaging (LCI) is also useful for detecting and evaluating gastritis, gastric intestinal metaplasia as a pre-cancerous lesion, and EGC. LCI provides a clear excellent endoscopic view of the atrophic border and the demarcation line under various conditions of gastritis. We could recognize gastritis as the lesions of the diffuse redness to purple color area with LCI. On the other hand, EGCs are recognized as the lesions of the orange-red, orange, or orange-white color area in the lesion of the purple color area, which is the surround atrophic mucosa with LCI. With further prospective randomized studies, we will be able to evaluate the diagnosis ability for EGC by IEE, and it will be necessary to evaluate the role of WLI/IEE and the additional effects of the diagnostic ability by adding IEE to WLI in future.

H. pylori Related Atrophic Gastritis Detection Using Enhanced Convolution Neural Network (CNN) Learner

Atrophic gastritis (AG) is commonly caused by the infection of the Helicobacter pylori (H. pylori) bacteria. If untreated, AG may develop into a chronic condition leading to gastric cancer, which is deemed to be the third primary cause of cancer-related deaths worldwide. Precursory detection of AG is crucial to avoid such cases. This work focuses on H. pylori-associated infection located at the gastric antrum, where the classification is of binary classes of normal versus atrophic gastritis. Existing work developed the Deep Convolution Neural Network (DCNN) of GoogLeNet with 22 layers of the pre-trained model. Another study employed GoogLeNet based on the Inception Module, fast and robust fuzzy C-means (FRFCM), and simple linear iterative clustering (SLIC) superpixel algorithms to identify gastric disease. GoogLeNet with Caffe framework and ResNet-50 are machine learners that detect H. pylori infection. Nonetheless, the accuracy may become abundant as the network depth increases. An upgrade to the current standards method is highly anticipated to avoid untreated and inaccurate diagnoses that may lead to chronic AG. The proposed work incorporates improved techniques revolving within DCNN with pooling as pre-trained models and channel shuffle to assist streams of information across feature channels to ease the training of networks for deeper CNN. In addition, Canonical Correlation Analysis (CCA) feature fusion method and ReliefF feature selection approaches are intended to revamp the combined techniques. CCA models the relationship between the two data sets of significant features generated by pre-trained ShuffleNet. ReliefF reduces and selects essential features from CCA and is classified using the Generalized Additive Model (GAM). It is believed the extended work is justified with a 98.2% testing accuracy reading, thus providing an accurate diagnosis of normal versus atrophic gastritis.

Current status and future perspective of linked color imaging for gastric cancer screening: a literature review

Screening endoscopy has advanced to facilitate improvements in the detection and prognosis of gastric cancer. However, most early gastric cancers (EGCs) have subtle morphological or color features that are difficult to detect by white-light imaging (WLI); thus, even well-trained endoscopists can miss EGC when using this conventional endoscopic approach. This review summarizes the current and future status of linked color imaging (LCI), a new image-enhancing endoscopy (IEE) method, for gastric screening. LCI has been shown to produce bright images even at a distant view and provide excellent visibility of gastric cancer due to high color contrast relative to the surrounding tissue. LCI delineates EGC as orange-red and intestinal metaplasia as purple, regardless of a history of Helicobacter pylori (Hp) eradication, and contributes to the detection of superficial EGC. Moreover, LCI assists in the determination of Hp infection status, which is closely related to the risk of developing gastric cancer. Transnasal endoscopy (ultra-thin) using LCI is also useful for identifying gastric neoplastic lesions. Recently, several prospective studies have demonstrated that LCI has a higher detection ratio for gastric cancer than WLI. We believe that LCI should be used in routine upper gastrointestinal endoscopies.

Artificial intelligence in gastroenterology: A narrative review

Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.

Application of artificial intelligence in the endoscopic diagnosis of early gastric cancer, atrophic gastritis, and Helicobacter pylori infection

Gastric cancer (GC) is one of the most serious health problems worldwide. Chronic atrophic gastritis (CAG) is most commonly caused by Helicobacter pylori (H. pylori) infection. Currently, endoscopic detection of early gastric cancer (EGC) and CAG remains challenging for endoscopists, and the diagnostic accuracy of H. pylori infection by endoscopy is approximately 70%. Artificial intelligence (AI) can assist endoscopic diagnosis including detection, prediction of depth of invasion, boundary delineation, and anatomical location of EGC, and has achievable diagnostic ability even comparable to experienced endoscopists. In this review we summarized various AI-assisted systems in the diagnosis of EGC, CAG, and H. pylori infection.

Systematic review and meta-analysis: Artificial intelligence for the diagnosis of gastric precancerous lesions and Helicobacter pylori infection

BACKGROUND

The endoscopic diagnosis of Helicobacter-pylori(H.pylori) infection and gastric precancerous lesions(GPL), namely atrophic-gastritis and intestinal-metaplasia, still remains challenging. Artificial intelligence(AI) may represent a powerful resource for the endoscopic recognition of these conditions.

AIMS

To explore the diagnostic performance(DP) of AI in the diagnosis of GPL and H.pylori infection.

METHODS

A systematic-review was performed by two independent authors up to September 2021. Inclusion criteria were studies focusing on the DP of AI-system in the diagnosis of GPL and H.pylori infection. The pooled accuracy of studies included was reported.

RESULTS

Overall, 128 studies were found (PubMed-Embase-Cochrane Library) and four and nine studies were finally included regarding GPL and H.pylori infection, respectively. The pooled-accuracy(random effects model) was 90.3%(95%CI 84.3-94.9) and 79.6%(95%CI 66.7-90.0) with a significant heterogeneity[I²=90.4%(95%CI 78.5-95.7);I²=97.9%(97.2-98.6)] for GPL and H.pylori infection, respectively. The Begg's-test showed a significant publication-bias(p = 0.0371) only among studies regarding H.pylori infection. The pooled-accuracy(random-effects-model) was similar considering only studies using CNN-model for the diagnosis of H.pylori infection: 74.1%[(95%CI 51.6-91.3);I²=98.9%(95%CI 98.5-99.3)], Begg's-test(p = 0.1416) did not show publication-bias.

CONCLUSION

AI-system seems to be a good resource for an easier diagnosis of GPL and H.pylori infection, showing a pooled-diagnostic-accuracy of 90% and 80%, respectively. However, considering the high heterogeneity, these promising data need an external validation by randomized control trials and prospective real-time studies.

Artificial intelligence in gastric cancer: applications and challenges

Gastric cancer (GC) is one of the most common malignant tumors with high mortality. Accurate diagnosis and treatment decisions for GC rely heavily on human experts' careful judgments on medical images. However, the improvement of the accuracy is hindered by imaging conditions, limited experience, objective criteria, and inter-observer discrepancies. Recently, the developments of machine learning, especially deep-learning algorithms, have been facilitating computers to extract more information from data automatically. Researchers are exploring the far-reaching applications of artificial intelligence (AI) in various clinical practices, including GC. Herein, we aim to provide a broad framework to summarize current research on AI in GC. In the screening of GC, AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological confirmation. In the diagnosis of GC, AI can support tumor-node-metastasis (TNM) staging and subtype classification. For treatment decisions, AI can help with surgical margin determination and prognosis prediction. Meanwhile, current approaches are challenged by data scarcity and poor interpretability. To tackle these problems, more regulated data, unified processing procedures, and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.

Demarcation Line Determination for Diagnosis of Gastric Cancer Disease Range Using Unsupervised Machine Learning in Magnifying Narrow-Band Imaging

Background and Aims: It is important to determine an accurate demarcation line (DL) between the cancerous lesions and background mucosa in magnifying narrow-band imaging (M-NBI)-based diagnosis. However, it is difficult for novice endoscopists. We aimed to automatically determine the accurate DL using a machine learning method. Methods: We used an unsupervised machine learning approach to determine the DLs. Our method consists of the following four steps: (1) an M-NBI image is segmented into superpixels using simple linear iterative clustering; (2) the image features are extracted for each superpixel; (3) the superpixels are grouped into several clusters using the k-means method; and (4) the boundaries of the clusters are extracted as DL candidates. The 23 M-NBI images of 11 cases were used for performance evaluation. The evaluation investigated the similarity of the DLs identified by endoscopists and our method, and the Euclidean distance between the two DLs was calculated. For the single case of 11 cases, the histopathological examination was also conducted to evaluate the proposed system. Results: The average Euclidean distances for the 11 cases were 10.65, 11.97, 7.82, 8.46, 8.59, 9.72, 12.20, 9.06, 22.86, 8.45, and 25.36. The results indicated that the proposed method could identify similar DLs to those identified by experienced doctors. Additionally, it was confirmed that the proposed system could generate pathologically valid DLs by increasing the number of clusters. Conclusions: Our proposed system can support the training of inexperienced doctors as well as enrich the knowledge of experienced doctors in endoscopy.

Traditional and Modern Diagnostic Approaches in Diagnosing Pediatric Helicobacter pylori Infection

Helicobacter pylori (H. pylori) is the most common bacterial infection worldwide, is usually acquired during childhood and is related to gastric carcinogenesis during adulthood. Therefore, its early proper diagnosis and subsequent successful eradication represent the cornerstones of gastric cancer prevention. The aim of this narrative review was to assess traditional and modern diagnostic methods in terms of H. pylori diagnosis. Several invasive and non-invasive methods were described, each with its pros and cons. The invasive diagnostic methods comprise endoscopy with biopsy, rapid urease tests, histopathological exams, cultures and biopsy-based molecular tests. Among these, probably the most available, accurate and cost-effective test remains histology, albeit molecular tests definitely remain the most accurate despite their high costs. The non-invasive tests consist of urea breath tests, serology, stool antigens and non-invasive molecular tests. Urea breath tests and stool antigens are the most useful in clinical practice both for the diagnosis of H. pylori infection and for monitoring the eradication of this infection after therapy. The challenges related to accurate diagnosis lead to a choice that must be based on H. pylori virulence, environmental factors and host peculiarities.

Implementation of artificial intelligence in upper gastrointestinal endoscopy

The application of artificial intelligence (AI) using deep learning has significantly expanded in the field of esophagogastric endoscopy. Recent studies have shown promising results in detecting and differentiating early gastric cancer using AI tools built using white light, magnified, or image-enhanced endoscopic images. Some studies have reported the use of AI tools to predict the depth of early gastric cancer based on endoscopic images. Similarly, studies based on using AI for detecting early esophageal cancer have also been reported, with an accuracy comparable to that of endoscopy specialists. Moreover, an AI system, developed to diagnose pharyngeal cancer, has shown promising performance with high sensitivity. These reports suggest that, if introduced for regular use in clinical settings, AI systems can significantly reduce the burden on physicians. This review summarizes the current status of AI applications in the upper gastrointestinal tract and presents directions for clinical practice implementation and future research.

Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases

Artificial intelligence (AI) has enormous potential to support clinical routine workflows and therefore is gaining increasing popularity among medical professionals. In the field of gastroenterology, investigations on AI and computer-aided diagnosis (CAD) systems have mainly focused on the lower gastrointestinal (GI) tract. However, numerous CAD tools have been tested also in upper GI disorders showing encouraging results. The main application of AI in the upper GI tract is endoscopy; however, the need to analyze increasing loads of numerical and categorical data in short times has pushed researchers to investigate applications of AI systems in other upper GI settings, including gastroesophageal reflux disease, eosinophilic esophagitis, and motility disorders. AI and CAD systems will be increasingly incorporated into daily clinical practice in the coming years, thus at least basic notions will be soon required among physicians. For noninsiders, the working principles and potential of AI may be as fascinating as obscure. Accordingly, we reviewed systematic reviews, meta-analyses, randomized controlled trials, and original research articles regarding the performance of AI in the diagnosis of both malignant and benign esophageal and gastric diseases, also discussing essential characteristics of AI.

Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders

In the past, conventional machine learning was applied to analyze tabulated medical data while deep learning was applied to study afflictions such as gastrointestinal disorders. Neural networks were used to detect, classify, and delineate various images of lesions because the local feature selection and optimization of the deep learning model enabled accurate image analysis. With the accumulation of medical records, the evolution of computational power and graphics processing units, and the widespread use of open-source libraries in large-scale machine learning processes, medical artificial intelligence (AI) is overcoming its limitations. While early studies prioritized the automatic diagnosis of cancer or pre-cancerous lesions, the current expanded scope of AI includes benign lesions, quality control, and machine learning analysis of big data. However, the limited commercialization of medical AI and the need to justify its application in each field of research are restricting factors. Modeling assumes that observations follow certain statistical rules, and external validation checks whether assumption is correct or generalizable. Therefore, unused data are essential in the training or internal testing process to validate the performance of the established AI models. This article summarizes the studies on the application of AI models in upper gastrointestinal disorders. The current limitations and the perspectives on future development have also been discussed.

Scope of Artificial Intelligence in Gastrointestinal Oncology

Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis. In the field of gastroenterology, the application of artificial intelligence can be vast from diagnosis, predicting tumor histology, polyp characterization, metastatic potential, prognosis, and treatment response. It can also provide accurate prediction models to determine the need for intervention with computer-aided diagnosis. The number of research studies on artificial intelligence in gastrointestinal cancer has been increasing rapidly over the last decade due to immense interest in the field. This review aims to review the impact, limitations, and future potentials of artificial intelligence in screening, diagnosis, tumor staging, treatment modalities, and prediction models for the prognosis of various gastrointestinal cancers.

Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases

The development of convolutional neural networks has achieved impressive advances of machine learning in recent years, leading to an increasing use of artificial intelligence (AI) in the field of gastrointestinal (GI) diseases. AI networks have been trained to differentiate benign from malignant lesions, analyze endoscopic and radiological GI images, and assess histological diagnoses, obtaining excellent results and high overall diagnostic accuracy. Nevertheless, there data are lacking on side effects of AI in the gastroenterology field, and high-quality studies comparing the performance of AI networks to health care professionals are still limited. Thus, large, controlled trials in real-time clinical settings are warranted to assess the role of AI in daily clinical practice. This narrative review gives an overview of some of the most relevant potential applications of AI for gastrointestinal diseases, highlighting advantages and main limitations and providing considerations for future development.

Helicobacter pylori eradication: Exploring its impacts on the gastric mucosa

Helicobacter pylori (H. pylori) infects approximately 50% of all humans globally. Persistent H. pylori infection causes multiple gastric and extragastric diseases, indicating the importance of early diagnosis and timely treatment. H. pylori eradication produces dramatic changes in the gastric mucosa, resulting in restored function. Consequently, to better understand the importance of H. pylori eradication and clarify the subsequent recovery of gastric mucosal functions after eradication, we summarize histological, endoscopic, and gastric microbiota changes to assess the therapeutic effects on the gastric mucosa.

Artificial Intelligence in Upper Gastrointestinal Endoscopy

BACKGROUND

Over the past decade, several artificial intelligence (AI) systems are developed to assist in endoscopic assessment of (pre-)cancerous lesions of the gastrointestinal (GI) tract. In this review, we aimed to provide an overview of the possible indications of AI technology in upper GI endoscopy and hypothesize about potential challenges for its use in clinical practice.

SUMMARY

Application of AI in upper GI endoscopy has been investigated for several indications: (1) detection, characterization, and delineation of esophageal and gastric cancer (GC) and their premalignant conditions; (2) prediction of tumor invasion; and (3) detection of Helicobacter pylori. AI systems show promising results with an accuracy of up to 99% for the detection of superficial and advanced upper GI cancers. AI outperformed trainee and experienced endoscopists for the detection of esophageal lesions and atrophic gastritis. For GC, AI outperformed mid-level and trainee endoscopists but not expert endoscopists.

KEY MESSAGES

Application of artificial intelligence (AI) in upper gastrointestinal endoscopy may improve early diagnosis of esophageal and gastric cancer and may enable endoscopists to better identify patients eligible for endoscopic resection. The benefit of AI on the quality of upper endoscopy still needs to be demonstrated, while prospective trials are needed to confirm accuracy and feasibility during real-time daily endoscopy.

Diagnosis of Helicobacter pylori Infection and Recent Advances

BACKGROUND

Helicobacter pylori (H. pylori) infects approximately 50% of the world population. Its infection is associated with gastropathies, extra-gastric digestive diseases, and diseases of other systems. There is a canonical process from acute-on-chronic inflammation, chronic atrophic gastritis (CAG), intestinal metaplasia (IM), dysplasia, and intraepithelial neoplasia, eventually to gastric cancer (GC). H. pylori eradication abolishes the inflammatory response and early treatment prevents the progression to preneoplastic lesions.

METHODS

the test-and-treat strategy, endoscopy-based strategy, and screen-and-treat strategy are recommended to prevent GC based on risk stratification, prevalence, and patients' clinical manifestations and conditions. Challenges contain false-negative results, increasing antibiotic resistance, decreasing eradication rate, and poor retesting rate. Present diagnosis methods are mainly based on invasive endoscopy and noninvasive laboratory testing.

RESULTS

to improve the accuracy and effectiveness and reduce the missed diagnosis, some advances were achieved including newer imaging techniques (such as image-enhanced endoscopy (IEE), artificial intelligence (AI) technology, and quantitative real-time polymerase chain reaction (qPCR) and digital PCR (dPCR).

CONCLUSION

in the article, we summarized the diagnosis methods of H. pylori infection and recent advances, further finding out the opportunities in challenges.

Application of linked color imaging in the diagnosis of early gastrointestinal neoplasms and precancerous lesions: a review

INTRODUCTION

Minimally invasive endoscopic resection is often effective in the management of early gastrointestinal tumors. However, advanced and more effective methods of endoscopic examination are required to improve the rate of diagnosing early gastrointestinal tumors.

DISCUSSION

The development of dye-based image-enhanced endoscopy (d-IEE) and equipment-based image-enhanced endoscopy (e-IEE) has helped improve the diagnostic rate of early gastrointestinal tumor using endoscopy. In some special cases, these methods are still not accurate in diagnosing lesions. On the basis of these e-IEEs, a new endoscopic technique, linked color imaging (LCI), that combines a specific short wavelength narrow band of light with white light, has been developed.

CONCLUSION

In this article, we summarized the characteristics of LCI and the development of research regarding digestive tract examination.

PLAIN LANGUAGE SUMMARY

Application of linked color imaging in early gastrointestinal neoplasms At present, the complete diagnosis of early gastrointestinal tumors and precancerous lesions can be made by gastrointestinal endoscopy. With the improvement of therapeutic instruments and operators' experience, endoscopic therapy can often achieve significant effect in the treatment of early gastrointestinal tumors. The development and spread of equipment-based image-enhanced endoscopy (e-IEE) mode has helped improve the diagnosis rate of early gastrointestinal tumors under endoscopy. However, in some special cases, these methods are still not accurate for the diagnosis of lesions. On the basis of these E-IEEs, a new endoscopic technique, linked color imaging (LCI), has been developed, which combines a specific short wavelength narrow band of light with white light. LCI can significantly improve the diagnostic rate of all types of gastrointestinal mucosal lesions. Tumor lesions and inflammatory lesions can be distinguished by observing the mucosal microvascular structure and color difference. LCI helps detect early gastrointestinal mucosal lesions by taking advantage of the differences in light absorption of different wavelengths and contrast of enhanced colors in the later stage.

Current situation and prospect of artificial intelligence application in endoscopic diagnosis of Helicobacter pylori infection

With the appearance and prevalence of deep learning, artificial intelligence (AI) has been broadly studied and made great progress in various fields of medicine, including gastroenterology. Helicobacter pylori (H. pylori), closely associated with various digestive and extradigestive diseases, has a high infection rate worldwide. Endoscopic surveillance can evaluate H. pylori infection situations and predict the risk of gastric cancer, but there is no objective diagnostic criteria to eliminate the differences between operators. The computer-aided diagnosis system based on AI technology has demonstrated excellent performance for the diagnosis of H. pylori infection, which is superior to novice endoscopists and similar to skilled. Compared with the visual diagnosis of H. pylori infection by endoscopists, AI possesses voluminous advantages: High accuracy, high efficiency, high quality control, high objectivity, and high-effect teaching. This review summarizes the previous and recent studies on AI-assisted diagnosis of H. pylori infection, points out the limitations, and puts forward prospect for future research.

The role of computer-assisted systems for upper-endoscopy quality monitoring and assessment of gastric lesions

This article analyses the literature regarding the value of computer-assisted systems in esogastroduodenoscopy-quality monitoring and the assessment of gastric lesions. Current data show promising results in upper-endoscopy quality control and a satisfactory detection accuracy of gastric premalignant and malignant lesions, similar or even exceeding that of experienced endoscopists. Moreover, artificial systems enable the decision for the best treatment strategies in gastric-cancer patient care, namely endoscopic vs surgical resection according to tumor depth. In so doing, unnecessary surgical interventions would be avoided whilst providing a better quality of life and prognosis for these patients. All these performance data have been revealed by numerous studies using different artificial intelligence (AI) algorithms in addition to white-light endoscopy or novel endoscopic techniques that are available in expert endoscopy centers. It is expected that ongoing clinical trials involving AI and the embedding of computer-assisted diagnosis systems into endoscopic devices will enable real-life implementation of AI endoscopic systems in the near future and at the same time will help to overcome the current limits of the computer-assisted systems leading to an improvement in performance. These benefits should lead to better diagnostic and treatment strategies for gastric-cancer patients. Furthermore, the incorporation of AI algorithms in endoscopic tools along with the development of large electronic databases containing endoscopic images might help in upper-endoscopy assistance and could be used for telemedicine purposes and second opinion for difficult cases.

Helicobacter pylori Outer Membrane Vesicles and Extracellular Vesicles from Helicobacter pylori-Infected Cells in Gastric Disease Development

Extracellular vesicles (EVs) are cell-derived vesicles important in intercellular communication that play an essential role in host-pathogen interactions, spreading pathogen-derived as well as host-derived molecules during infection. Pathogens can induce changes in the composition of EVs derived from the infected cells and use them to manipulate their microenvironment and, for instance, modulate innate and adaptive inflammatory immune responses, both in a stimulatory or suppressive manner. Gastric cancer is one of the leading causes of cancer-related deaths worldwide and infection with Helicobacter pylori (H. pylori) is considered the main risk factor for developing this disease, which is characterized by a strong inflammatory component. EVs released by host cells infected with H. pylori contribute significantly to inflammation, and in doing so promote the development of disease. Additionally, H. pylori liberates vesicles, called outer membrane vesicles (H. pylori-OMVs), which contribute to atrophia and cell transformation in the gastric epithelium. In this review, the participation of both EVs from cells infected with H. pylori and H. pylori-OMVs associated with the development of gastric cancer will be discussed. By deciphering which functions of these external vesicles during H. pylori infection benefit the host or the pathogen, novel treatment strategies may become available to prevent disease.

Artificial intelligence: Thinking outside the box

Artificial intelligence (AI) for luminal gastrointestinal endoscopy is rapidly evolving. To date, most applications have focused on colon polyp detection and characterization. However, the potential of AI to revolutionize our current practice in endoscopy is much more broadly positioned. In this review article, the Authors provide new ideas on how AI might help endoscopists in the future to rediscover endoscopy practice.

Endoscopic three-categorical diagnosis of Helicobacter pylori infection using linked color imaging and deep learning: a single-center prospective study (with video)

BACKGROUND

Helicobacter pylori (H. pylori) eradication is required to reduce incidence related to gastric cancer. Recently, it was found that even after the successful eradication of H. pylori, an increased, i.e., moderate, risk of gastric cancer persists in patients with advanced mucosal atrophy and/or intestinal metaplasia. This study aimed to develop a computer-aided diagnosis (CAD) system to classify the status of H. pylori infection of patients into three categories: uninfected (with no history of H. pylori infection), currently infected, and post-eradication.

METHODS

The CAD system was based on linked color imaging (LCI) combined with deep learning (DL). First, a validation dataset was formed for the CAD systems by recording endoscopic movies of 120 subjects. Next, a training dataset of 395 subjects was prepared to enable DL. All endoscopic examinations were recorded using both LCI and white-light imaging (WLI). These endoscopic data were used to develop two different CAD systems, one for LCI (LCI-CAD) and one for WLI (WLI-CAD) images.

RESULTS

The diagnostic accuracy of the LCI-CAD system was 84.2% for uninfected, 82.5% for currently infected, and 79.2% for post-eradication status. Comparisons revealed superior accuracy of diagnoses based on LCI-CAD data relative based on WLI-CAD for uninfected, currently infected, and post-eradication cases. Furthermore, the LCI-CAD system demonstrated comparable diagnostic accuracy to that of experienced endoscopists with the validation data set of LCI.

CONCLUSIONS

The results of this study suggest the feasibility of an innovative gastric cancer screening program to determine cancer risk in individual subjects based on LCI-CAD.

A Gratifying Step forward for the Application of Artificial Intelligence in the Field of Endoscopy: A Narrative Review

Endoscopy is the optimal choice of diagnosis of gastrointestinal (GI) diseases. Following the advancements made in medical technology, different kinds of novel endoscopy-methods have emerged. Although the significant progress in the penetration of endoscopic tools that have markedly improved the diagnostic rate of GI diseases, there are still some limitations, including instability of human diagnostic performance caused by intensive labor burden and high missed diagnosis rate of subtle lesions. Recently, artificial intelligence (AI) has been applied gradually to assist endoscopists in addressing these issues.

Accuracy of artificial intelligence-assisted detection of upper GI lesions: a systematic review and meta-analysis

BACKGROUND AND AIMS

Artificial intelligence (AI)-assisted detection is increasingly used in upper endoscopy. We performed a meta-analysis to determine the diagnostic accuracy of AI on detection of gastric and esophageal neoplastic lesions and Helicobacter pylori (HP) status.

METHODS

We searched Embase, PubMed, Medline, Web of Science, and Cochrane databases for studies on AI detection of gastric or esophageal neoplastic lesions and HP status. After assessing study quality using the Quality Assessment of Diagnostic Accuracy Studies tool, a bivariate meta-analysis following a random-effects model was used to summarize the data and plot hierarchical summary receiver-operating characteristic curves. The diagnostic accuracy was determined by the area under the hierarchical summary receiver-operating characteristic curve (AUC).

RESULTS

Twenty-three studies including 969,318 images were included. The AUC of AI detection of neoplastic lesions in the stomach, Barrett's esophagus, and squamous esophagus and HP status were .96 (95% confidence interval [CI], .94-.99), .96 (95% CI, .93-.99), .88 (95% CI, .82-.96), and .92 (95% CI, .88-.97), respectively. AI using narrow-band imaging was superior to white-light imaging on detection of neoplastic lesions in squamous esophagus (.92 vs .83, P < .001). The performance of AI was superior to endoscopists in the detection of neoplastic lesions in the stomach (AUC, .98 vs .87; P < .001), Barrett's esophagus (AUC, .96 vs .82; P < .001), and HP status (AUC, .90 vs .82; P < .001).

CONCLUSIONS

AI is accurate in the detection of upper GI neoplastic lesions and HP infection status. However, most studies were based on retrospective reviews of selected images, which requires further validation in prospective trials.

Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy

BACKGROUND

Helicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of H pylori infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition and classification.

OBJECTIVE

This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of H pylori infection using endoscopic images.

METHODS

Two independent evaluators searched core databases. The inclusion criteria included studies with endoscopic images of H pylori infection and with application of AI for the prediction of H pylori infection presenting diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed.

RESULTS

Ultimately, 8 studies were identified. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve of AI for the prediction of H pylori infection were 0.87 (95% CI 0.72-0.94), 0.86 (95% CI 0.77-0.92), 40 (95% CI 15-112), and 0.92 (95% CI 0.90-0.94), respectively, in the 1719 patients (385 patients with H pylori infection vs 1334 controls). Meta-regression showed methodological quality and included the number of patients in each study for the purpose of heterogeneity. There was no evidence of publication bias. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images.

CONCLUSIONS

An AI algorithm is a reliable tool for endoscopic diagnosis of H pylori infection. The limitations of lacking external validation performance and being conducted only in Asia should be overcome.

TRIAL REGISTRATION

PROSPERO CRD42020175957; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957.

Review: Diagnosis of Helicobacter pylori infection

New imaging techniques are still the topic of many evaluations for both the diagnosis of Helicobacter pylori gastritis and the detection of early gastric cancer. Concerning invasive tests, there were studies on the reuse of the rapid urease test material for other tests, and a novel fluorescent method to be used for histology but with limited sensitivity. Progress occurred essentially in the molecular methods area, especially next-generation sequencing which is applied to detect both H pylori and the mutations associated with antibiotic resistance. For non-invasive tests, a few studies have been published on the validity of breath collection bags, the shortening of the testing time, the performance of different analysers or the added value of citric acid in the protocol. The accuracy of serological immunochromatographic tests is also improving. Multiplex serology detecting antibodies to certain proteins allows confirmation of a current infection. Dried blood spots can be used to collect and store blood without a loss of accuracy. Finally, the serum antibody titer can be useful in predicting the risk of gastric cancer. Several stool antigen tests were evaluated with good results, and a novel test using immunomagnetic beads coated with monoclonal antibodies is potentially interesting. PCR detection in stools can also be effective but needs an efficient DNA extraction method. The use of easyMAG^® (bioMérieux) combined with Amplidiag® H pylori + ClariR (Mobidiag) appears to be powerful.

The role of linked color imaging in endoscopic diagnosis of Helicobacter pylori associated gastritis

OBJECTIVE

Linked color imaging (LCI), a novel image-enhanced endoscopy, can make it easy to recognize differences in mucosal color. It may be helpful for diagnosing H. pylori associated gastritis and H. pylori infection status. We investigated whether LCI could improve the diagnostic accuracy of H. pylori associated gastritis.

MATERIALS AND METHODS

Upper endoscopy was performed for 100 patients using white light imaging (WLI) and LCI. During the exam, endoscopic video was recorded. It was then analyzed by four expert endoscopists. They reviewed these videos for endoscopic diagnosis of atrophic gastritis, metaplastic gastritis, nodular gastritis and H. pylori infection. Tissue biopsies with rapid urease test were done to confirm H. pylori infection status and intestinal metaplasia.

RESULTS

Kappa values for the inter-observer variability among the four endoscopists were fair to moderate under WLI and fair to good under LCI. Sensitivity, specificity, positive predictive value and negative predictive value for diagnosing H. pylori infection using WLI were 32.4%, 93.3%, 85.2% and 53.6%, respectively, while those for LCI were 57.4%, 91.3%, 88.7% and 64.3%, respectively. Total diagnostic accuracies for diagnosing H. pylori infection using WLI/LCI were 70.8%/78.8%. The accuracy and sensitivity of LCI for diagnosing H. pylori infection were significantly higher than those of WLI (p < .001 for both). However, there were no significant differences in the accuracy, sensitivity or specificity for diagnosing metaplastic gastritis between LCI and WLI.

CONCLUSIONS

LCI has better diagnostic accuracy for H. pylori infection status than WLI. Clinical trial registration number: KCT0003674.

Artificial intelligence in luminal endoscopy

Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett's, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence-augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence-augmented diagnostic luminal endoscopy into our routine clinical practice.

Can image-enhanced endoscopy improve the diagnosis of Kyoto classification of gastritis in the clinical setting?

Endoscopic diagnosis of Helicobacter pylori (H. pylori) infection, the most common cause of gastric cancer, is very important to clarify high-risk patients of gastric cancer for reducing morbidity and mortality of gastric cancer. Recently, the Kyoto classification of gastritis was developed based on the endoscopic characteristics of H. pylori infection-associated gastritis for clarifying H. pylori infection status and evaluating risk factors of gastric cancer. Recently, magnifying endoscopy with narrow-band imaging (NBI) has reported benefits of the accuracy and reproducibility of endoscopic diagnosis for H. pylori-related premalignant lesions. In addition to NBI, various types of image-enhanced endoscopies (IEEs) are available including autofluorescence imaging, blue laser imaging, and linked color imaging. This review focuses on understanding the clinical applications and the corresponding evidences shown to improve the diagnosis of gastritis based on Kyoto classification using currently available advanced technologies of IEEs.

Linked color imaging for the detection of early gastrointestinal neoplasms

In routine upper and lower gastrointestinal endoscopy, overlooking neoplastic lesions is inevitable even for well-trained endoscopists. Various methods have been reported to improve the detection of gastrointestinal neoplasms including chromoendoscopy, special endoscopes, and processor and image enhanced technologies. Equipment-based image enhanced endoscopy (e-IEE) using narrow band imaging (NBI) and blue laser imaging (BLI) is useful to characterize known lesions with magnification at a close-up view. However, they are not useful for the early detection of superficial, pale neoplasms, or both because of the weak image at a distant view in a wide lumen such as the stomach or colon. Linked color imaging (LCI) is a novel pre- and post-processing technology developed by Fujifilm Corporation that has sufficient brightness to illuminate a wide lumen. LCI delineates early gastric cancers as orange-red and intestinal metaplasia as purple. LCI improves the adenoma detection rate in the colon and decreases the polyp miss rate. LCI contributes to the detection of superficial lesions throughout the gastrointestinal tract by enhancing the color contrast between the neoplasm and the surrounding mucosa. LCI can distinguish them by their specific color allocation based mainly on the distribution of capillaries. The authors believe that moving forward, LCI should be used in routine upper and lower gastrointestinal endoscopy.