Diagnostic Accuracy of Artificial Intelligence (AI) to Detect Early Neoplasia in Barrett's Esophagus: A Non-comparative Systematic Review and Meta-Analysis

From the mathematical model to the patient: The scientific and human aspects of artificial intelligence in gastrointestinal surgery

Recent medical literature shows that the application of artificial intelligence (AI) models in gastrointestinal pathology is an exponentially growing field, with promising models that show very high performances. Regarding inflammatory bowel disease (IBD), recent reviews demonstrate promising diagnostic and prognostic AI models. However, studies are generally at high risk of bias (especially in AI models that are image-based). The creation of specific AI models that improve diagnostic performance and allow the establishment of a general prognostic forecast in IBD is of great interest, as it may allow the stratification of patients into subgroups and, in turn, allow the creation of different diagnostic and therapeutic protocols for these patients. Regarding surgical models, predictive models of postoperative complications have shown great potential in large-scale studies. In this work, the authors present the development of a predictive algorithm for early post-surgical complications in Crohn's disease based on a Random Forest model with exceptional predictive ability for complications within the cohort. The present work, based on logical and reasoned, clinical, and applicable aspects, lays a solid foundation for future prospective work to further develop post-surgical prognostic tools for IBD. The next step is to develop in a prospective and multicenter way, a collaborative path to optimize this line of research and make it applicable to our patients.

Precision endoscopy in the era of climate change and sustainability

Global warming caused by increased greenhouse gas (GHG) emissions has a direct impact on human health. Gastrointestinal (GI) endoscopy contributes significantly to GHG emissions due to energy consumption, reprocessing of endoscopes and accessories, production of equipment, safe disposal of biohazardous waste, and travel by patients. Moreover, GHGs are also generated in histopathology through tissue processing and the production of biopsy specimen bottles. The reduction in unnecessary surveillance endoscopies and biopsies is a practical approach to decrease GHG emissions without affecting disease outcomes. This narrative review explores the role of precision medicine in GI endoscopy, such as image-enhanced endoscopy and artificial intelligence, with a focus on decreasing unnecessary endoscopic procedures and biopsies in the surveillance and diagnosis of premalignant lesions in the esophagus, stomach, and colon. This review offers strategies to minimize unnecessary endoscopic procedures and biopsies, decrease GHG emissions, and maintain high-quality patient care, thereby contributing to sustainable healthcare practices.

Role of artificial intelligence in Barrett’s esophagus

The application of artificial intelligence (AI) in gastrointestinal endoscopy has gained significant traction over the last decade. One of the more recent applications of AI in this field includes the detection of dysplasia and cancer in Barrett’s esophagus (BE). AI using deep learning methods has shown promise as an adjunct to the endoscopist in detecting dysplasia and cancer. Apart from visual detection and diagnosis, AI may also aid in reducing the considerable interobserver variability in identifying and distinguishing dysplasia on whole slide images from digitized BE histology slides. This review aims to provide a comprehensive summary of the key studies thus far as well as providing an insight into the future role of AI in Barrett’s esophagus.

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

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

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

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

Management of Post Ablative Barrett's Esophagus: a Review of Current Practices and Look at Emerging Technologies

Purpose of review

Endoscopic eradication therapy is an effective and durable treatment for Barrett's esophagus (BE) related neoplasia, but even after achieving successful eradication, these patients remain at risk for recurrence and require ongoing routine examinations. The optimal surveillance protocol including endoscopic technique, sampling strategy, and timing are still being refined. The aim of this review is to discuss current management principles for the post ablation patient and emerging technologies to guide clinical practice.

Recent findings

There is increasing evidence to support less frequent surveillance exams in the first year after complete eradication of intestinal metaplasia and a move towards targeted biopsies of visible lesions and sampling high-risk locations such as the gastroesophageal junction. Promising technologies on the horizon that could impact management include novel biomarkers, personalized surveillance intervals, and non-endoscopic approaches.

Summary

Ongoing high-quality examinations after endoscopic eradication therapy are key to limiting recurrent BE. Surveillance intervals should be based on the pretreatment grade of dysplasia. Future research should focus on technologies and surveillance practices that are most efficient for patients and the healthcare system.

Diagnosis and Management of Barrett's Esophagus

Barrett's esophagus is a metaplastic change of esophageal mucosa, which can be characterized by its salmon-colored lining and the presence of columnar epithelium with goblet cells. It is a well-established precancerous state of esophageal adenocarcinoma, a tumor with very poor survival rates, which incidence is rapidly growing. Despite numerous research, the debate about its diagnosis and management is still ongoing. This article aims to provide an overview of the current recommendations and new discoveries regarding the subject.