Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases

Diagnostic performance of AI-assisted endoscopy diagnosis of digestive system tumors: an umbrella review

The diagnostic performance of artificial intelligence (AI)-assisted endoscopy for digestive tumors remains controversial. The objective of this umbrella review was to summarize the comprehensive evidence for the AI-assisted endoscopic diagnosis of digestive system tumors. We grouped the evidence according to the location of each digestive system tumor and performed separate subgroup analyses on the basis of the method of data collection and form of the data. We also compared the diagnostic performance of AI with that of experts and nonexperts. For early digestive system cancer and precancerous lesions, AI showed a high diagnostic performance in capsule endoscopy and esophageal squamous cell carcinoma. Additionally, AI-assisted endoscopic ultrasonography (EUS) had good diagnostic accuracy for pancreatic cancer. In the subgroup analysis, AI had a better diagnostic performance than experts for most digestive system tumors. However, the diagnostic performance of AI using video data requires improvement.

The Role of Artificial Intelligence and Big Data for Gastrointestinal Disease

Artificial intelligence (AI) is a rapidly evolving presence in all fields and industries, with the ability to both improve quality and reduce the burden of human effort. Gastroenterology is a field with a focus on diagnostic techniques and procedures, and AI and big data have established and growing roles to play. Alongside these opportunities are challenges, which will evolve in parallel.

Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews

BACKGROUND

Artificial intelligence (AI) has the potential to transform cancer diagnosis, ultimately leading to better patient outcomes.

OBJECTIVE

We performed an umbrella review to summarize and critically evaluate the evidence for the AI-based imaging diagnosis of cancers.

METHODS

PubMed, Embase, Web of Science, Cochrane, and IEEE databases were searched for relevant systematic reviews from inception to June 19, 2024. Two independent investigators abstracted data and assessed the quality of evidence, using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Systematic Reviews and Research Syntheses. We further assessed the quality of evidence in each meta-analysis by applying the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria. Diagnostic performance data were synthesized narratively.

RESULTS

In a comprehensive analysis of 158 included studies evaluating the performance of AI algorithms in noninvasive imaging diagnosis across 8 major human system cancers, the accuracy of the classifiers for central nervous system cancers varied widely (ranging from 48% to 100%). Similarities were observed in the diagnostic performance for cancers of the head and neck, respiratory system, digestive system, urinary system, female-related systems, skin, and other sites. Most meta-analyses demonstrated positive summary performance. For instance, 9 reviews meta-analyzed sensitivity and specificity for esophageal cancer, showing ranges of 90%-95% and 80%-93.8%, respectively. In the case of breast cancer detection, 8 reviews calculated the pooled sensitivity and specificity within the ranges of 75.4%-92% and 83%-90.6%, respectively. Four meta-analyses reported the ranges of sensitivity and specificity in ovarian cancer, and both were 75%-94%. Notably, in lung cancer, the pooled specificity was relatively low, primarily distributed between 65% and 80%. Furthermore, 80.4% (127/158) of the included studies were of high quality according to the JBI Critical Appraisal Checklist, with the remaining studies classified as medium quality. The GRADE assessment indicated that the overall quality of the evidence was moderate to low.

CONCLUSIONS

Although AI shows great potential for achieving accelerated, accurate, and more objective diagnoses of multiple cancers, there are still hurdles to overcome before its implementation in clinical settings. The present findings highlight that a concerted effort from the research community, clinicians, and policymakers is required to overcome existing hurdles and translate this potential into improved patient outcomes and health care delivery.

TRIAL REGISTRATION

PROSPERO CRD42022364278; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022364278.

Role of Artificial Intelligence in the Detection and Management of Premalignant and Malignant Lesions of the Esophagus and Stomach

The advent of artificial intelligence (AI) and deep learning algorithms, particularly convolutional neural networks, promises to address pitfalls, bridging the care for patients at high risk with improved detection (computer-aided detection [CADe]) and characterization (computer-aided diagnosis [CADx]) of lesions. This review describes the available artificial intelligence (AI) technology and the current data on AI tools for screening esophageal squamous cell cancer, Barret's esophagus-related neoplasia, and gastric cancer. These tools outperformed endoscopists in many situations. Recent randomized controlled trials have demonstrated the successful application of AI tools in clinical practice with improved outcomes.

Deep learning detected histological differences between invasive and non-invasive areas of early esophageal cancer

The depth of invasion plays a critical role in predicting the prognosis of early esophageal cancer, but the reasons behind invasion and the changes occurring in invasive areas are still not well understood. This study aimed to explore the morphological differences between invasive and non-invasive areas in early esophageal cancer specimens that have undergone endoscopic submucosal dissection (ESD), using artificial intelligence (AI) to shed light on the underlying mechanisms. In this study, data from 75 patients with esophageal squamous cell carcinoma (ESCC) were analyzed and endoscopic assessments were conducted to determine submucosal (SM) invasion. An AI model, specifically a Clustering-constrained Attention Multiple Instance Learning model (CLAM), was developed to predict the depth of cancer by training on surface histological images taken from both invasive and non-invasive regions. The AI model highlighted specific image portions, or patches, which were further examined to identify morphological differences between the two types of areas. The 256-pixel AI model demonstrated an average area under the receiver operating characteristic curve (AUC) value of 0.869 and an accuracy (ACC) of 0.788. The analysis of the AI-identified patches revealed that regions with invasion (SM) exhibited greater vascularity compared with non-invasive regions (epithelial). The invasive patches were characterized by a significant increase in the number and size of blood vessels, as well as a higher count of red blood cells (all with p-values <0.001). In conclusion, this study demonstrated that AI could identify critical differences in surface histopathology between non-invasive and invasive regions, particularly highlighting a higher number and larger size of blood vessels in invasive areas.

The application of the combination between artificial intelligence and endoscopy in gastrointestinal tumors

Gastrointestinal (GI) tumors have always been a major type of malignant tumor and a leading cause of tumor‐related deaths worldwide. The main principles of modern medicine for GI tumors are early prevention, early diagnosis, and early treatment, with early diagnosis being the most effective measure. Endoscopy, due to its ability to visualize lesions, has been one of the primary modalities for screening, diagnosing, and treating GI tumors. However, a qualified endoscopist often requires long training and extensive experience, which to some extent limits the wider use of endoscopy. With advances in data science, artificial intelligence (AI) has brought a new development direction for the endoscopy of GI tumors. AI can quickly process large quantities of data and images and improve diagnostic accuracy with some training, greatly reducing the workload of endoscopists and assisting them in early diagnosis. Therefore, this review focuses on the combined application of endoscopy and AI in GI tumors in recent years, describing the latest research progress on the main types of tumors and their performance in clinical trials, the application of multimodal AI in endoscopy, the development of endoscopy, and the potential applications of AI within it, with the aim of providing a reference for subsequent research.

A deep learning approach for gastroscopic manifestation recognition based on Kyoto Gastritis Score

OBJECTIVE

The risk of gastric cancer can be predicted by gastroscopic manifestation recognition and the Kyoto Gastritis Score. This study aims to validate the applicability of AI approaches for recognizing gastroscopic manifestations according to the definition of Kyoto Gastritis Score, with the goal of improving early gastric cancer detection and reducing gastric cancer mortality.

METHODS

In this retrospective study, 29013 gastric endoscopy images were collected and carefully annotated into five categories according to the Kyoto Gastritis Score, i.e. atrophy (A), diffuse redness (DR), enlarged folds (H), intestinal metaplasia (IM), and nodularity (N). As a multi-label recognition task, we propose a deep learning approach composed of five GAM-EfficientNet models, each performing a multiple classification to quantify gastroscopic manifestations, i.e. no presentation or the severity score 0-2. This approach was compared with endoscopists of varying years of experience in terms of accuracy, specificity, precision, recall, and F1 score.

RESULTS

The approach demonstrated good performance in identifying the five manifestations of the Kyoto Gastritis Score, with an average accuracy, specificity, precision, recall, and F1 score of 78.70%, 91.92%, 80.23%, 78.70%, and 0.78, respectively. The average performance of five experienced endoscopists was 72.63%, 90.00%, 77.68%, 72.63%, and 0.73, while that of five less experienced endoscopists was 66.60%, 87.44%, 70.88%, 66.60%, and 0.66, respectively. The sample t-test indicates that the approach's average accuracy, specificity, precision, recall, and F1 score for identifying the five manifestations were significantly higher than those of less experienced endoscopists, experienced endoscopists, and all endoscopists on average (p < 0.05).

CONCLUSION

Our study demonstrates the potential of deep learning approaches on gastric manifestation recognition over junior, even senior endoscopists. Thus, the deep learning approach holds potential as an auxiliary tool, although prospective validation is still needed to assess its clinical applicability.

Advancements in biliopancreatic endoscopy - A comprehensive review of artificial intelligence in EUS and ERCP

The development and implementation of artificial intelligence (AI), particularly deep learning (DL) models, has generated significant interest across various fields of gastroenterology. While research in luminal endoscopy has seen rapid translation to clinical practice with approved AI devices, its potential extends far beyond, offering promising benefits for biliopancreatic endoscopy like optical characterization of strictures during cholangioscopy or detection and classification of pancreatic lesions during diagnostic endoscopic ultrasound (EUS). This narrative review provides an up-to-date of the latest literature and available studies in this field. Serving as a comprehensive guide to the current landscape of AI in biliopancreatic endoscopy, emphasizing technological advancements, main applications, ethical considerations, and future directions for research and clinical implementation.

Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis

Introduction

Artificial intelligence has come to be the highlight in almost all fields of science. It uses various models and algorithms to detect patterns and specific findings to diagnose a disease with utmost accuracy. With the increasing need for accurate and precise diagnosis of disease, employing artificial intelligence models and concepts in healthcare setup can be beneficial.

Methodology

The search engines and databases employed in this study are PubMed, ScienceDirect and Medline. Studies published between 1st January 2013 to 1st February 2023 were included in this analysis. The selected articles were screened preliminarily using the Rayyan web tool, after which investigators screened the selected articles individually. The risk of bias for the selected studies was assessed using QUADAS-2 tool specially designed to test bias among studies related to diagnostic test reviews.

Results

In this review, 17 studies were included from a total of 12,173 studies. These studies were analysed for their sensitivity, accuracy, positive predictive value, specificity and negative predictive value in diagnosing barrette's neoplasia, cardiac arrest, esophageal adenocarcinoma, sepsis and gastrointestinal stromal tumors. All the studies reported heterogeneity with p-value <0.05 at confidence interval 95%.

Conclusion

The existing evidential data suggests that artificial intelligence can be highly helpful in the field of diagnosis providing maximum precision and early detection. This helps to prevent disease progression and also helps to provide treatment at the earliest. Employing artificial intelligence in diagnosis will define the advancement of health care environment and also be beneficial in every aspect concerned with treatment to illnesses.

Pharmacological management of gastro-esophageal reflux disease: state of the art in 2024

INTRODUCTION

Gastroesophageal reflux disease (GERD) is a chronic disease of the esophagus characterized by the regurgitation of stomach contents into the esophagus, causing troublesome symptoms and/or complications. Among patients with GERD, around 30% of patients have visible mucosal damage, while 70% have normal esophageal mucosa. Accordingly, the optimal pharmacological treatment of GERD should address different disease manifestations, including symptoms, the mucosal damage when present, and possible chronic complications, including strictures, Barrett's esophagus, and esophageal adenocarcinoma.

AREAS COVERED

Available medical treatments for GERD include proton pump inhibitors (PPIs), potassium-competitive acid blockers (PCABs), histamine receptor antagonists (H2-RAs), prokinetics, and mucosal protectants, such as alginates, hyaluronic acid/chondroitin-sulfate, and poliprotect. Each compound has its own advantages and disadvantages, and knowledge of expected benefits and tips for their use is paramount for the success of treatment. In addition, the appropriateness of indications for initiating treatment is also crucial to achieve positive results when managing GERD patients.

EXPERT OPINION

PPIs, PCABs, H2-RAs, prokinetics, and mucosal protectants can all be used in patients with GERD, but careful assessment of patients' characteristics as well as advantages and disadvantages of each therapeutic compound is essential to ensure successful treatment of GERD.

Diagnostic Accuracy of Artificial Intelligence in Endoscopy: Umbrella Review

Background

Some research has already reported the diagnostic value of artificial intelligence (AI) in different endoscopy outcomes. However, the evidence is confusing and of varying quality.

Objective

This review aimed to comprehensively evaluate the credibility of the evidence of AI's diagnostic accuracy in endoscopy.

Methods

Before the study began, the protocol was registered on PROSPERO (CRD42023483073). First, 2 researchers searched PubMed, Web of Science, Embase, and Cochrane Library using comprehensive search terms. Then, researchers screened the articles and extracted information. We used A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR2) to evaluate the quality of the articles. When there were multiple studies aiming at the same result, we chose the study with higher-quality evaluations for further analysis. To ensure the reliability of the conclusions, we recalculated each outcome. Finally, the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) was used to evaluate the credibility of the outcomes.

Results

A total of 21 studies were included for analysis. Through AMSTAR2, it was found that 8 research methodologies were of moderate quality, while other studies were regarded as having low or critically low quality. The sensitivity and specificity of 17 different outcomes were analyzed. There were 4 studies on esophagus, 4 studies on stomach, and 4 studies on colorectal regions. Two studies were associated with capsule endoscopy, two were related to laryngoscopy, and one was related to ultrasonic endoscopy. In terms of sensitivity, gastroesophageal reflux disease had the highest accuracy rate, reaching 97%, while the invasion depth of colon neoplasia, with 71%, had the lowest accuracy rate. On the other hand, the specificity of colorectal cancer was the highest, reaching 98%, while the gastrointestinal stromal tumor, with only 80%, had the lowest specificity. The GRADE evaluation suggested that the reliability of most outcomes was low or very low.

Conclusions

AI proved valuabe in endoscopic diagnoses, especially in esophageal and colorectal diseases. These findings provide a theoretical basis for developing and evaluating AI-assisted systems, which are aimed at assisting endoscopists in carrying out examinations, leading to improved patient health outcomes. However, further high-quality research is needed in the future to fully validate AI's effectiveness.

The 1st EoETALY Consensus on the Diagnosis and Management of Eosinophilic Esophagitis-Current Treatment and Monitoring

The present document constitutes Part 2 of the EoETALY Consensus Statements guideline on the diagnosis and management of eosinophilic esophagitis (EoE) developed by experts in the field of EoE across Italy (i.e., EoETALY Consensus Group). Part 1 was published as a different document, and included three chapters discussing 1) definition, epidemiology, and pathogenesis; 2) clinical presentation and natural history and 3) diagnosis of EoE. The present work provides guidelines on the management of EoE in two final chapters: 4) treatment and 5) monitoring and follow-up, and also includes considerations on knowledge gaps and a proposed research agenda for the coming years. The guideline was developed through a Delphi process, with grading of the strength and quality of the evidence of the recommendations performed according to accepted GRADE criteria.This document has received the endorsement of three Italian national societies including the Italian Society of Gastroenterology (SIGE), the Italian Society of Neurogastroenterology and Motility (SINGEM), and the Italian Society of Allergology, Asthma, and Clinical Immunology (SIAAIC). The guidelines also involved the contribution of members of ESEO Italia, the Italian Association of Families Against EoE.

The 1st EoETALY Consensus on the Diagnosis and Management of Eosinophilic Esophagitis - Definition, Clinical Presentation and Diagnosis

Eosinophilic esophagitis (EoE) is a chronic type 2-mediated inflammatory disease of the esophagus that represents the most common eosinophilic gastrointestinal disease. Experts in the field of EoE across Italy (i.e., EoETALY Consensus Group) including gastroenterologists, endoscopists, allergologists/immunologists, and paediatricians conducted a Delphi process to develop updated consensus statements for the management of patients with EoE and update the previous position paper of the Italian Society of Gastroenterology (SIGE) in light of recent evidence. Grading of the strength and quality of the evidence of the recommendations was performed using accepted GRADE criteria. The guideline is divided in two documents: Part 1 includes three chapters, namely 1) definition, epidemiology, and pathogenesis; 2) clinical presentation and natural history, and 3) diagnosis, while Part 2 includes two chapters: 4) treatment and 5) monitoring and follow-up. This document has received the endorsement of three Italian national societies including the SIGE, the Italian Society of Neurogastroenterology and Motility (SINGEM), and the Italian Society of Allergology, Asthma, and Clinical Immunology (SIAAIC). With regards to patients' involvement, these guidelines involved the contribution of members of ESEO Italia, the Italian Association of Families Against EoE.

Artificial Intelligence Tools for the Diagnosis of Eosinophilic Esophagitis in Adults Reporting Dysphagia: Development, External Validation, and Software Creation for Point-of-Care Use

BACKGROUND

Despite increased awareness of eosinophilic esophagitis (EoE), the diagnostic delay has remained stable over the past 3 decades. There is a need to improve the diagnostic performance and optimize resources allocation in the setting of EoE.

OBJECTIVE

We developed and validated 2 point-of-care machine learning (ML) tools to predict a diagnosis of EoE before histology results during office visits.

METHODS

We conducted a multicenter study in 3 European tertiary referral centers for EoE. We built predictive ML models using retrospectively extracted clinical and esophagogastroduodenoscopy (EGDS) data collected from 273 EoE and 55 non-EoE dysphagia patients. We validated the models on an independent cohort of 93 consecutive patients with dysphagia undergoing EGDS with biopsies at 2 different centers. Models' performance was assessed by area under the curve (AUC), sensitivity, specificity, and positive and negative predictive values (PPV and NPV). The models were integrated into a point-of-care software package.

RESULTS

The model trained on clinical data alone showed an AUC of 0.90 and a sensitivity, specificity, PPV, and NPV of 0.90, 0.75, 0.80, and 0.87, respectively, for the diagnosis of EoE in the external validation cohort. The model trained on a combination of clinical and endoscopic data showed an AUC of 0.94, and a sensitivity, specificity, PPV, and NPV of 0.94, 0.68, 0.77, and 0.91, respectively, in the external validation cohort.

CONCLUSION

Our software-integrated models (https://webapplicationing.shinyapps.io/PointOfCare-EoE/) can be used at point-of-care to improve the diagnostic workup of EoE and optimize resources allocation.

Convolutional neural network deep learning model accurately detects rectal cancer in endoanal ultrasounds

BACKGROUND

Imaging is vital for assessing rectal cancer, with endoanal ultrasound (EAUS) being highly accurate in large tertiary medical centers. However, EAUS accuracy drops outside such settings, possibly due to varied examiner experience and fewer examinations. This underscores the need for an AI-based system to enhance accuracy in non-specialized centers. This study aimed to develop and validate deep learning (DL) models to differentiate rectal cancer in standard EAUS images.

METHODS

A transfer learning approach with fine-tuned DL architectures was employed, utilizing a dataset of 294 images. The performance of DL models was assessed through a tenfold cross-validation.

RESULTS

The DL diagnostics model exhibited a sensitivity and accuracy of 0.78 each. In the identification phase, the automatic diagnostic platform achieved an area under the curve performance of 0.85 for diagnosing rectal cancer.

CONCLUSIONS

This research demonstrates the potential of DL models in enhancing rectal cancer detection during EAUS, especially in settings with lower examiner experience. The achieved sensitivity and accuracy suggest the viability of incorporating AI support for improved diagnostic outcomes in non-specialized medical centers.

Analyzing the Impact of Oncological Data at Different Time Points and Tumor Biomarkers on Artificial Intelligence Predictions for Five-Year Survival in Esophageal Cancer

AIM: In this study, we use Artificial Intelligence (AI), including Machine (ML) and Deep Learning (DL), to predict the long-term survival of resectable esophageal cancer (EC) patients in a high-volume surgical center. Our objective is to evaluate the predictive efficacy of AI methods for survival prognosis across different time points of oncological treatment. This involves comparing models trained with clinical data, integrating either Tumor, Node, Metastasis (TNM) classification or tumor biomarker analysis, for long-term survival predictions. METHODS: In this retrospective study, 1002 patients diagnosed with EC between 1996 and 2021 were analyzed. The original dataset comprised 55 pre- and postoperative patient characteristics and 55 immunohistochemically evaluated biomarkers following surgical intervention. To predict the five-year survival status, four AI methods (Random Forest RF, XG Boost XG, Artificial Neural Network ANN, TabNet TN) and Logistic Regression (LR) were employed. The models were trained using three predefined subsets of the training dataset as follows: (I) the baseline dataset (BL) consisting of pre-, intra-, and postoperative data, including the TNM but excluding tumor biomarkers, (II) clinical data accessible at the time of the initial diagnostic workup (primary staging dataset, PS), and (III) the PS dataset including tumor biomarkers from tissue microarrays (PS + biomarkers), excluding TNM status. We used permutation feature importance for feature selection to identify only important variables for AI-driven reduced datasets and subsequent model retraining. RESULTS: Model training on the BL dataset demonstrated similar predictive performances for all models (Accuracy, ACC: 0.73/0.74/0.76/0.75/0.73; AUC: 0.78/0.82/0.83/0.80/0.79 RF/XG/ANN/TN/LR, respectively). The predictive performance and generalizability declined when the models were trained with the PS dataset. Surprisingly, the inclusion of biomarkers in the PS dataset for model training led to improved predictions (PS dataset vs. PS dataset + biomarkers; ACC: 0.70 vs. 0.77/0.73 vs. 0.79/0.71 vs. 0.75/0.69 vs. 0.72/0.63 vs. 0.66; AUC: 0.77 vs. 0.83/0.80 vs. 0.85/0.76 vs. 0.86/0.70 vs. 0.76/0.70 vs. 0.69 RF/XG/ANN/TN/LR, respectively). The AI models outperformed LR when trained with the PS datasets. The important features shared after AI-driven feature selection in all models trained with the BL dataset included histopathological lymph node status (pN), histopathological tumor size (pT), clinical tumor size (cT), age at the time of surgery, and postoperative tracheostomy. Following training with the PS dataset with biomarkers, the important predictive features included patient age at the time of surgery, TP-53 gene mutation, Mesothelin expression, thymidine phosphorylase (TYMP) expression, NANOG homebox protein expression, and indoleamine 2,3-dioxygenase (IDO) expressed on tumor-infiltrating lymphocytes, as well as tumor-infiltrating Mast- and Natural killer cells. CONCLUSION: Different AI methods similarly predict the long-term survival status of patients with EC and outperform LR, the state-of-the-art classification model. Survival status can be predicted with similar predictive performance with patient data at an early stage of treatment when utilizing additional biomarker analysis. This suggests that individual survival predictions can be made early in cancer treatment by utilizing biomarkers, reducing the necessity for the pathological TNM status post-surgery. This study identifies important features for survival predictions that vary depending on the timing of oncological treatment.

Current status of quality control in screening esophagogastroduodenoscopy and the emerging role of artificial intelligence

Esophagogastroduodenoscopy (EGD) screening is being implemented in countries with a high incidence of upper gastrointestinal (UGI) cancer. High-quality EGD screening ensures the yield of early diagnosis and prevents suffering from advanced UGI cancer and minimal operational-related discomfort. However, performance varied dramatically among endoscopists, and quality control for EGD screening remains suboptimal. Guidelines have recommended potential measures for endoscopy quality improvement and research has been conducted for evidence. Moreover, artificial intelligence offers a promising solution for computer-aided diagnosis and quality control during EGD examinations. In this review, we summarized the key points for quality assurance in EGD screening based on current guidelines and evidence. We also outline the latest evidence, limitations, and future prospects of the emerging role of artificial intelligence in EGD quality control, aiming to provide a foundation for improving the quality of EGD screening.

The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis

Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise, diagnostic skill, and thus human error. Artificial intelligence (AI) in endoscopy is increasingly bridging this gap. This systematic review and meta-analysis consolidate the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases and articles on the role of AI in the endoscopic diagnosis of esophageal cancer management were included. A meta-analysis was also performed. Fourteen studies (1590 patients) assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma-the pooled sensitivity and specificity were 91.2% (84.3-95.2%) and 80% (64.3-89.9%). Nine studies (478 patients) assessed AI capabilities of diagnosing esophageal adenocarcinoma with the pooled sensitivity and specificity of 93.1% (86.8-96.4) and 86.9% (81.7-90.7). The remaining studies formed the qualitative summary. AI technology, as an adjunct to endoscopy, can assist in accurate, early detection of esophageal malignancy. It has shown superior results to endoscopists alone in identifying early cancer and assessing depth of tumor invasion, with the added benefit of not requiring a specialized skill set. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.

Diets including Animal Food Are Associated with Gastroesophageal Reflux Disease

Gastroesophageal reflux disease (GERD) is a clinical condition with a prevalence of up to 25% in Western countries. Typical GERD symptoms include heartburn and retrosternal regurgitation. Lifestyle modifications, including diet, are considered a first-line therapeutic approach. To evaluate the impact of life habits on GERD in this cross-sectional study, we used data collected through an online survey from 1146 participants. GERD was defined according to the Montreal Consensus. For all participants, clinical and lifestyle characteristics were recorded. Overall, 723 participants (63.1%) consumed a diet including animal food (non-vegans), and 423 participants (36.9%) were vegans. The prevalence of GERD was 11% (CI 95%, 9-14%) in non-vegans and 6% (CI 95%, 4-8%) in vegans. In the multivariate analysis, after adjusting for confounding factors, subjects on a non-vegan diet were associated with a two-fold increase in the prevalence of GERD compared to vegans (OR = 1.96, CI 95%, 1.22-3.17, p = 0.006). BMI and smoking habits were also significantly associated with GERD. This study shows that an animal food-based diet (meat, fish, poultry, dairy, and eggs) is associated with an increased risk of GERD compared to a vegan diet. These findings might inform the lifestyle management of patients with GERD-related symptoms.

The Role of Artificial Intelligence in Prospective Real-Time Histological Prediction of Colorectal Lesions during Colonoscopy: A Systematic Review and Meta-Analysis

Artificial intelligence (AI) presents a novel platform for improving disease diagnosis. However, the clinical utility of AI remains limited to discovery studies, with poor translation to clinical practice. Current data suggests that 26% of diminutive pre-malignant lesions and 3.5% of colorectal cancers are missed during colonoscopies. The primary aim of this study was to explore the role of artificial intelligence in real-time histological prediction of colorectal lesions during colonoscopy. A systematic search using MeSH headings relating to "AI", "machine learning", "computer-aided", "colonoscopy", and "colon/rectum/colorectal" identified 2290 studies. Thirteen studies reporting real-time analysis were included. A total of 2958 patients with 5908 colorectal lesions were included. A meta-analysis of six studies reporting sensitivities (95% CI) demonstrated that endoscopist diagnosis was superior to a computer-assisted detection platform, although no statistical significance was reached (p = 0.43). AI applications have shown encouraging results in differentiating neoplastic and non-neoplastic lesions using narrow-band imaging, white light imaging, and blue light imaging. Other modalities include autofluorescence imaging and elastic scattering microscopy. The current literature demonstrates that despite the promise of new endoscopic AI models, they remain inferior to expert endoscopist diagnosis. There is a need to focus developments on real-time histological predictions prior to clinical translation to demonstrate improved diagnostic capabilities and time efficiency.

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.

Robotic Colonoscopy and Beyond: Insights into Modern Lower Gastrointestinal Endoscopy

Lower gastrointestinal endoscopy is considered the gold standard for the diagnosis and removal of colonic polyps. Delays in colonoscopy following a positive fecal immunochemical test increase the likelihood of advanced adenomas and colorectal cancer (CRC) occurrence. However, patients may refuse to undergo conventional colonoscopy (CC) due to fear of possible risks and pain or discomfort. In this regard, patients undergoing CC frequently require sedation to better tolerate the procedure, increasing the risk of deep sedation or other complications related to sedation. Accordingly, the use of CC as a first-line screening strategy for CRC is hampered by patients' reluctance due to its invasiveness and anxiety about possible discomfort. To overcome the limitations of CC and improve patients' compliance, several studies have investigated the use of robotic colonoscopy (RC) both in experimental models and in vivo. Self-propelling robotic colonoscopes have proven to be promising thanks to their peculiar dexterity and adaptability to the shape of the lower gastrointestinal tract, allowing a virtually painless examination of the colon. In some instances, when alternatives to CC and RC are required, barium enema (BE), computed tomographic colonography (CTC), and colon capsule endoscopy (CCE) may be options. However, BE and CTC are limited by the need for subsequent investigations whenever suspicious lesions are found. In this narrative review, we discussed the current clinical applications of RC, CTC, and CCE, as well as the advantages and disadvantages of different endoscopic procedures, with a particular focus on RC.

Encouraging appropriate use of proton pump inhibitors: existing initiatives and proposals for the future

INTRODUCTION

Proton pump inhibitors (PPIs) have revolutionized the management of acid-related disorders, representing today the mainstay treatment of these conditions. However, despite their large range of indications and usefulness, the remarkable expansion of their use in the last two decades cannot be explained by the increasing prevalence of acid-related diseases only. An inappropriate prescription for clinical conditions in which the pathogenetic role of acid has not been documented has been described, with the natural consequence of increasing the costs and the potential risk of iatrogenic harm due to adverse events and complications recently emerged.

AREAS COVERED

In this review, we summarize current indications of PPIs administration, potential adverse events associated with their chronic utilization, and misuse of PPIs. Moreover, we describe existing and possible initiatives for improving the use of PPIs, and some proposals for the future.

EXPERT OPINION

PPI deprescribing is the preferred and most effective approach to reduce the use of PPIs, rather than adopting sharp discontinuation, probably due to fewer withdrawal symptoms. Nonetheless, large knowledge gaps still exist in clinical practice regarding the optimal approach of PPI deprescribing in various clinical scenarios. Further prospective well-designed international studies are eagerly warranted to improve our perspectives on controlling global PPI inappropriate use.

Ambulatory pH-Impedance Findings Confirm That Grade B Esophagitis Provides Objective Diagnosis of Gastroesophageal Reflux Disease

INTRODUCTION

The Lyon Consensus designates Los Angeles (LA) grade C/D esophagitis or acid exposure time (AET) >6% on impedance-pH monitoring (MII-pH) as conclusive for gastroesophageal reflux disease (GERD). We aimed to evaluate proportions with objective GERD among symptomatic patients with LA grade A, B, and C esophagitis on endoscopy.

METHODS

Demographics, clinical data, endoscopy findings, and objective proton-pump inhibitor response were collected from symptomatic prospectively enrolled patients from 2 referral centers. Off-therapy MII-pH parameters included AET, number of reflux episodes, mean nocturnal baseline impedance, and postreflux swallow-induced peristaltic wave index. Objective GERD evidence was compared between LA grades.

RESULTS

Of 155 patients (LA grade A: 74 patients, B: 61 patients, and C: 20 patients), demographics and presentation were similar across LA grades. AET >6% was seen in 1.4%, 52.5%, and 75%, respectively, in LA grades A, B, and C. Using additional MII-pH metrics, an additional 16.2% with LA grade A and 47.5% with LA grade B esophagitis had AET 4%-6% with low mean nocturnal baseline impedance and postreflux swallow-induced peristaltic wave index; there were no additional gains using the number of reflux episodes or symptom-reflux association metrics. Compared with LA grade C (100% conclusive GERD based on endoscopic findings), 100% of LA grade B esophagitis also had objective GERD but only 17.6% with LA grade A esophagitis ( P < 0.001 compared with each). Proton-pump inhibitor response was comparable between LA grades B and C (74% and 70%, respectively) but low in LA grade A (39%, P < 0.001).

DISCUSSION

Grade B esophagitis indicates an objective diagnosis of GERD.

Comparison of acid-lowering drugs for endoscopy negative reflux disease: Systematic review and network Meta-Analysis

BACKGROUND

The comparative efficacy and safety of medical therapies for gastro-esophageal reflux symptoms in endoscopy-negative reflux disease is unclear. We conducted a network meta-analysis to evaluate efficacy and safety of proton pump inhibitors (PPIs), histamine-2-receptor antagonists, potassium-competitive acid blockers (PCABs), and alginates in patients with endoscopy-negative reflux disease.

METHODS

We searched MEDLINE, EMBASE, EMBASE Classic, and the Cochrane central register of controlled trials from inception to February 1, 2022. We included randomized controlled trials (RCTs) comparing efficacy of all drugs versus each other, or versus a placebo, in adults with endoscopy-negative reflux disease. Results were reported as pooled relative risks with 95% confidence intervals to summarize effect of each comparison tested, with treatments ranked according to P-score.

KEY RESULTS

We identified 23 RCTs containing 10,735 subjects with endoscopy-negative reflux disease. Based on failure to achieve complete relief of symptoms between ≥2 and <4 weeks, omeprazole 20 mg o.d. (P-score 0.94) ranked first, with esomeprazole 20 mg o.d. or 40 mg o.d. ranked second and third. In achieving adequate relief, only rabeprazole 10 mg o.d. was significantly more efficacious than placebo. For failure to achieve complete relief at ≥4 weeks, dexlansoprazole 30 mg o.d. (P-score 0.95) ranked first, with 30 ml alginate q.i.d. combined with omeprazole 20 mg o.d., and 30 ml alginate t.i.d. second and third. In terms of failure to achieve adequate relief at ≥4 weeks, dexlansoprazole 60 mg o.d. ranked first (P-score 0.90), with dexlansoprazole 30 mg o.d. and rabeprazole 20 mg o.d. second and third. All drugs were safe and well-tolerated.

CONCLUSIONS & INFERENCES

Our results confirm superiority of PPIs compared with most other drugs in treating endoscopy-negative reflux disease. Future RCTs should aim to better classify patients with endoscopy-negative reflux disease, and to establish the role of alginates and PCABs in achieving symptom relief in both the short- and long-term.

Esophageal dysmotility in patients with eosinophilic esophagitis: pathogenesis, assessment tools, manometric characteristics, and clinical implications

Eosinophilic esophagitis (EoE) represents a growing cause of chronic esophageal morbidity whose incidence and prevalence are increasing rapidly. The disease is characterized by eosinophilic infiltrates of the esophagus and organ dysfunction. Typical symptoms include dysphagia, chest pain, and bolus impaction, which are associated to mechanical obstructions in most patients. However, up to one in three EoE patients has no visible obstruction, suggesting that a motor disorder of the esophagus may underlie symptoms. Although potentially relevant for treatment refractoriness and symptomatic burden, esophageal dysmotility is often neglected when assessing EoE patients. The first systematic review investigating esophageal motility patterns in patients with EoE was published only recently. Accordingly, we reviewed the pathogenesis, assessment tools, manometric characteristics, and clinical implications of dysmotility in patients with EoE to highlight its clinical relevance. In summary, eosinophils can influence the amplitude of esophageal contractions via different mechanisms. The prevalence of dysmotility may increase with disease duration, possibly representing a late feature of EoE. Patients with EoE may display a wide range of motility disorders and possible disease-specific manometric pressurization patterns may be useful for raising a clinical suspicion. Intermittent dysmotility events have been found to correlate with symptoms on prolonged esophageal manometry, although high-resolution manometry studies have reported inconsistent results, possibly due to the suboptimal sensitivity of current manometry protocols. Motor abnormalities may recover following EoE treatment in a subset of patients, but invasive management of the motor disorder is required in some instances. In conclusion, esophageal motor abnormalities may have a role in eliciting symptoms, raising clinical suspicion, and influencing treatment outcome in EoE. The assessment of esophageal motility appears valuable in the EoE setting.

Development and Validation of Deep Learning Models for the Multiclassification of Reflux Esophagitis Based on the Los Angeles Classification

This study is to evaluate the feasibility of deep learning (DL) models in the multiclassification of reflux esophagitis (RE) endoscopic images, according to the Los Angeles (LA) classification for the first time. The images were divided into three groups, namely, normal, LA classification A + B, and LA C + D. The images from the HyperKvasir dataset and Suzhou hospital were divided into the training and validation datasets as a ratio of 4 : 1, while the images from Jintan hospital were the independent test set. The CNNs- or Transformer-architectures models (MobileNet, ResNet, Xception, EfficientNet, ViT, and ConvMixer) were transfer learning via Keras. The visualization of the models was proposed using Gradient-weighted Class Activation Mapping (Grad-CAM). Both in the validation set and the test set, the EfficientNet model showed the best performance as follows: accuracy (0.962 and 0.957), recall for LA A + B (0.970 and 0.925) and LA C + D (0.922 and 0.930), Marco-recall (0.946 and 0.928), Matthew's correlation coefficient (0.936 and 0.884), and Cohen's kappa (0.910 and 0.850), which was better than the other models and the endoscopists. According to the EfficientNet model, the Grad-CAM was plotted and highlighted the target lesions on the original images. This study developed a series of DL-based computer vision models with the interpretable Grad-CAM to evaluate the feasibility in the multiclassification of RE endoscopic images. It firstly suggests that DL-based classifiers show promise in the endoscopic diagnosis of esophagitis.

Diagnostics of Gastrointestinal Motility and Function: Update for Clinicians

Disorders of gastrointestinal (GI) tract motility and function are prevalent in the general population and negatively affect quality of life [...].

Application of an artificial intelligence system for endoscopic diagnosis of superficial esophageal squamous cell carcinoma

BACKGROUND

Upper gastrointestinal endoscopy is critical for esophageal squamous cell carcinoma (ESCC) detection; however, endoscopists require long-term training to avoid missing superficial lesions.

AIM

To develop a deep learning computer-assisted diagnosis (CAD) system for endoscopic detection of superficial ESCC and investigate its application value.

METHODS

We configured the CAD system for white-light and narrow-band imaging modes based on the YOLO v5 algorithm. A total of 4447 images from 837 patients and 1695 images from 323 patients were included in the training and testing datasets, respectively. Two experts and two non-expert endoscopists reviewed the testing dataset independently and with computer assistance. The diagnostic performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity.

RESULTS

The area under the receiver operating characteristics curve, accuracy, sensitivity, and specificity of the CAD system were 0.982 [95% confidence interval (CI): 0.969-0.994], 92.9% (95%CI: 89.5%-95.2%), 91.9% (95%CI: 87.4%-94.9%), and 94.7% (95%CI: 89.0%-97.6%), respectively. The accuracy of CAD was significantly higher than that of non-expert endoscopists (78.3%, P < 0.001 compared with CAD) and comparable to that of expert endoscopists (91.0%, P = 0.129 compared with CAD). After referring to the CAD results, the accuracy of the non-expert endoscopists significantly improved (88.2% vs 78.3%, P < 0.001). Lesions with Paris classification type 0-IIb were more likely to be inaccurately identified by the CAD system.

CONCLUSION

The diagnostic performance of the CAD system is promising and may assist in improving detectability, particularly for inexperienced endoscopists.

Applications of Artificial Intelligence to Eosinophilic Esophagitis

Eosinophilic Esophagitis (EoE) is a chronic immune-related inflammation, and challenges to its diagnosis and treatment evaluation persist. This literature review evaluates all AI applications to EOE, including 15 studies using AI algorithms for counting eosinophils in biopsies, as well as newer diagnostics using mRNA transcripts in biopsies, endoscopic photos, blood and urine biomarkers, and an improved scoring system for disease classification. We also discuss the clinical impact of these models, challenges faced in applying AI to EoE, and future applications. In conclusion, AI has the potential to improve diagnostics and clinical evaluation in EoE, improving patient outcomes.

Advances on Neurogastroenterology and Motility Disorders: Pathophysiology, Diagnostics and Management

Symptoms related to abnormalities in gastrointestinal tract motility and functions are very common in the general population, affecting both pediatrics and adults, from both sexes [...].

Clinical use of mean nocturnal baseline impedance and post-reflux swallow-induced peristaltic wave index for the diagnosis of gastro-esophageal reflux disease

The clinical diagnosis of gastro-esophageal reflux disease (GERD) is based on the presence of typical esophageal troublesome symptoms. In clinical practice, heartburn relief following a proton pump inhibitor (PPI) trial or endoscopy can confirm a diagnosis of GERD. In cases of diagnostic uncertainty or before anti-reflux interventions, combined impedance-pH monitoring (MII-pH) provides a comprehensive assessment of both physical and chemical properties of the refluxate, allowing to achieve a conclusive diagnosis of GERD. Recently, the Lyon Consensus proposed the use of mean nocturnal baseline impedance (MNBI) and post-reflux swallow-induced peristaltic wave index (PSPW-I) as novel MII-pH metrics to support the diagnosis of GERD. The calculation of MNBI and PSPW-I currently needs to be performed manually, but artificial intelligence systems for the automated analysis of MII-pH tracings are being developed. Several studies demonstrated the increased diagnostic yield MNBI and PSPW-I for the categorization of patients with GERD at both on- and off-PPI MII-pH monitoring. Accordingly, we performed a narrative review on the clinical use and diagnostic yield of MNBI and PSPW-I when the diagnosis of GERD is uncertain. Based on currently available evidence, we strongly support the evaluation of PSPW-I and MNBI as part of the standard assessment of MII-pH tracings for the evaluation of GERD, especially in patients with endoscopy-negative heartburn.