A Pilot Study on Automatic Three-Dimensional Quantification of Barrett's Esophagus for Risk Stratification and Therapy Monitoring

Adherence to quality indicators and best practices in surveillance endoscopy of Barrett's esophagus: A video-based assessment

Background and study aims Adherence to quality indicators (QIs) and best practices (BPs) for endoscopic surveillance of Barrett's esophagus (BE) is low based on clinical documentation which is an inaccurate representation of events occurring during procedures. This study aimed to assess adherence to measurable QI and BP using video evaluation. Methods We performed a single center video-based retrospective review of surveillance endoscopies performed for BE ≥1 cm between March 1, 2018 and October 1, 2020. Adherence to QIs and BPs was assessed through video review and documentation. Videos were evaluated by five gastroenterologists. Interrater variability was determined using 10 videos before reviewing the remaining 128 videos. A generalized linear regression model was used to determine predictors of adherence to QIs and BPs. Results There were 138 endoscopies reviewed. Inspection with virtual chromoendoscopy (VC) occurred in 75 cases (54%) on video review with documentation in 50 of these cases (67%). Adherence to the Seattle protocol (SP) occurred in 74 cases (54%) on video review with documentation in 28 of these cases (38%). Use of VC or the SP was documented but not observed on video review in 16 (12%) and 30 (22%) cases, respectively. Length of BE was associated with increased use of the Prague classification (odds ratio [OR] 1.21, 95% confidence interval [CI] 1.07-1.37) while years in practice was associated with a decreased likelihood of VC use (OR 0.93, 95% CI 0.88-0.99). Conclusions This study validates prior data demonstrating poor adherence to QIs and BPs and highlights discrepancies between clinical documentation and events occurring during procedures.

Deep Learning in Barrett's Esophagus Diagnosis: Current Status and Future Directions

Barrett's esophagus (BE) represents a pre-malignant condition characterized by abnormal cellular proliferation in the distal esophagus. A timely and accurate diagnosis of BE is imperative to prevent its progression to esophageal adenocarcinoma, a malignancy associated with a significantly reduced survival rate. In this digital age, deep learning (DL) has emerged as a powerful tool for medical image analysis and diagnostic applications, showcasing vast potential across various medical disciplines. In this comprehensive review, we meticulously assess 33 primary studies employing varied DL techniques, predominantly featuring convolutional neural networks (CNNs), for the diagnosis and understanding of BE. Our primary focus revolves around evaluating the current applications of DL in BE diagnosis, encompassing tasks such as image segmentation and classification, as well as their potential impact and implications in real-world clinical settings. While the applications of DL in BE diagnosis exhibit promising results, they are not without challenges, such as dataset issues and the "black box" nature of models. We discuss these challenges in the concluding section. Essentially, while DL holds tremendous potential to revolutionize BE diagnosis, addressing these challenges is paramount to harnessing its full capacity and ensuring its widespread application in clinical practice.

Cancer risk stratification system and classification of gastritis: Perspectives

Kyoto global consensus reports that the current ICD-10 classification for gastritis is obsolete. The Kyoto classification of gastritis states that severe mucosal atrophy has a high risk of gastric cancer, while mild to moderate atrophy has a low risk. The updated Kimura-Takemoto classification of atrophic gastritis considers five histological types of multifocal corpus atrophic gastritis according to stages C2 to O3. This method of morphological diagnosis of atrophic gastritis increases sensitivity by 2.4 times for severe atrophy compared to the updated Sydney system. This advantage should be considered when stratifying the high risk of gastric cancer. The updated Kimura-Takemoto classification of atrophic gastritis should be used as a reference standard (gold standard) in studies of morpho-functional relationships to identify serological markers of atrophic gastritis with evidence-based effectiveness. The use of artificial intelligence in the serological screening of atrophic gastritis makes it possible to screen a large number of the population. During serological screening of atrophic gastritis and risk stratification of gastric cancer, it is advisable to use the Kyoto classification of gastritis with updated Kimura-Takemoto classification of atrophic gastritis.

Role of artificial intelligence in diagnosing Barrett's esophagus-related neoplasia

Barrett's esophagus is associated with an increased risk of adenocarcinoma. Thorough screening during endoscopic surveillance is crucial to improve patient prognosis. Detecting and characterizing dysplastic or neoplastic Barrett's esophagus during routine endoscopy are challenging, even for expert endoscopists. Artificial intelligence-based clinical decision support systems have been developed to provide additional assistance to physicians performing diagnostic and therapeutic gastrointestinal endoscopy. In this article, we review the current role of artificial intelligence in the management of Barrett's esophagus and elaborate on potential artificial intelligence in the future.

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.

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

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

Global variations in diagnostic guidelines for Barrett's esophagus

Endoscopic diagnosis of gastroesophageal junction and Barrett's esophagus is essential for surveillance and early detection of esophageal adenocarcinoma and esophagogastric junction cancer. Despite its small size, the gastroesophageal junction has many inherent problems, including marked differences in diagnostic methods for Barrett's esophagus in international guidelines. To define Barrett's esophagus, gastroesophageal junction location should be clarified. Although gastric folds and palisade vessels are landmarks for identifying this junction, they are sometimes difficult to observe due to air entry or reflux esophagitis. The possibility of diagnosing a malignancy associated with Barrett's esophagus <1 cm, identified using palisade vessels, should be re-examined. Nontargeted biopsies of Barrett's esophagus are commonly used to detect intestinal metaplasia, dysplasia, and cancer as described in the Seattle protocol. Barrett's esophagus with intestinal metaplasia has a high risk of becoming cancerous. Furthermore, the frequency of cancer in patients with Barrett's esophagus without intestinal metaplasia is high, and the guidelines differ on whether to include the presence of intestinal metaplasia in the diagnosis of Barrett's esophagus. Use of advanced imaging technologies, including narrow-band imaging with magnifying endoscopy and linked color imaging, is reportedly valid for diagnosing Barrett's esophagus. Furthermore, artificial intelligence has facilitated the diagnosis of Barrett's esophagus through its deep learning and image recognition capabilities. However, it is necessary to first use the endoscopic definition of the gastroesophageal junction, which is common in all countries, and then elucidate the characteristics of Barrett's esophagus in each region, for example, length differences in the risk of carcinogenesis with and without intestinal metaplasia.

Advances and horizons for artificial intelligence of endoscopic screening and surveillance of gastric and esophageal disease

The development of artificial intelligence in endoscopic assessment of the gastrointestinal tract has shown progressive enhancement in diagnostic acuity. This review discusses the expanding applications for gastric and esophageal diseases. The gastric section covers the utility of AI in detecting and characterizing gastric polyps and further explores prevention, detection, and classification of gastric cancer. The esophageal discussion highlights applications for use in screening and surveillance in Barrett's esophagus and in high-risk conditions for esophageal squamous cell carcinoma. Additionally, these discussions highlight applications for use in assessing eosinophilic esophagitis and future potential in assessing esophageal microbiome changes.

Artificial Intelligence in the Management of Barrett’s Esophagus and Early Esophageal Adenocarcinoma

Simple Summary

Esophageal adenocarcinoma is increasing in incidence and is the most common subtype of esophageal cancer in Western societies. AI systems are currently under development and validation in many fields of gas-troenterology.

Abstract

Esophageal adenocarcinoma is increasing in incidence and is the most common subtype of esophageal cancer in Western societies. The stepwise progression of Barrett´s metaplasia to high-grade dysplasia and invasive adenocarcinoma provides an opportunity for screening and surveillance. There are important unresolved issues, which include (i) refining the definition of the screening population in order to avoid unnecessary invasive diagnostics, (ii) a more precise prediction of the (very heterogeneous) individual progression risk from metaplasia to invasive cancer in order to better tailor surveillance recommendations, (iii) improvement of the quality of endoscopy in order to reduce the high miss rate for early neoplastic lesions, and (iv) support for the diagnosis of tumor infiltration depth in order to guide treatment decisions. Artificial intelligence (AI) systems might be useful as a support to better solve the above-mentioned issues.

Anatomical Engineering and 3D Printing for Surgery and Medical Devices: International Review and Future Exponential Innovations

Three-dimensional printing (3DP) has recently gained importance in the medical industry, especially in surgical specialties. It uses different techniques and materials based on patients' needs, which allows bioprofessionals to design and develop unique pieces using medical imaging provided by computed tomography (CT) and magnetic resonance imaging (MRI). Therefore, the Department of Biology and Medicine and the Department of Physics and Engineering, at the Bioastronautics and Space Mechatronics Research Group, have managed and supervised an international cooperation study, in order to present a general review of the innovative surgical applications, focused on anatomical systems, such as the nervous and craniofacial system, cardiovascular system, digestive system, genitourinary system, and musculoskeletal system. Finally, the integration with augmented, mixed, virtual reality is analyzed to show the advantages of personalized treatments, taking into account the improvements for preoperative, intraoperative planning, and medical training. Also, this article explores the creation of devices and tools for space surgery to get better outcomes under changing gravity conditions.

Identification of Barrett's esophagus in endoscopic images using deep learning

BACKGROUND

Development of a deep learning method to identify Barrett's esophagus (BE) scopes in endoscopic images.

METHODS

443 endoscopic images from 187 patients of BE were included in this study. The gastroesophageal junction (GEJ) and squamous-columnar junction (SCJ) of BE were manually annotated in endoscopic images by experts. Fully convolutional neural networks (FCN) were developed to automatically identify the BE scopes in endoscopic images. The networks were trained and evaluated in two separate image sets. The performance of segmentation was evaluated by intersection over union (IOU).

RESULTS

The deep learning method was proved to be satisfying in the automated identification of BE in endoscopic images. The values of the IOU were 0.56 (GEJ) and 0.82 (SCJ), respectively.

CONCLUSIONS

Deep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the BE scope in endoscopic images. This automated recognition method helps clinicians to locate and recognize the scopes of BE in endoscopic examinations.

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.