- Recently, the use of computer-aided detection (CADe) for colonoscopy has been investigated to improve the adenoma detection rate (ADR). We aimed to assess the efficacy of a regulatory-approved CADe in a large-scale study with high numbers of patients and endoscopists.
- Quality improvement in colorectal cancer screening and surveillance by colonoscopy is based on the assumption that an increase in adenoma detection rate (ADR) has the potential to decrease the risk of colorectal cancer. Techniques and technologies to improve ADR evaluated in a recent network meta-analysis1 included add-on devices (cap, endocuff, endoring, G-EYE), enhanced imaging techniques (chromoendoscopy, narrow-band imaging, flexible spectral imaging color enhancement, blue laser imaging), new endoscopes (full-spectrum endoscopy, extra-wide-angle-view colonoscopy, dual focus), and low-cost optimizing of existing resources (water-aided colonoscopy, second observer, dynamic position change), alone or in combination with high-definition colonoscopy or each other.
- Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis.
- One-fourth of colorectal neoplasia are missed at screening colonoscopy, representing the main cause of interval colorectal cancer. Deep learning systems with real-time computer-aided polyp detection (CADe) showed high accuracy in artificial settings, and preliminary randomized controlled trials (RCTs) reported favorable outcomes in the clinical setting. The aim of this meta-analysis was to summarize available RCTs on the performance of CADe systems in colorectal neoplasia detection.
- Artificial intelligence (AI), specifically deep learning, offers the potential to enhance the field of GI endoscopy in areas ranging from lesion detection and classification to quality metrics and documentation. Progress in this field will be measured by whether AI implementation can lead to improved patient outcomes and more efficient clinical workflow for GI endoscopists. The aims of this article are to report the findings of a multidisciplinary group of experts focusing on issues in AI research and applications related to gastroenterology and endoscopy, to review the current status of the field, and to produce recommendations for investigators developing and studying new AI technologies for gastroenterology.
- Endoscopy is one of the cornerstones in the field of gastroenterology. The original fiberoptic endoscope was developed in the 1950s. From this point in time and decade after decade the field of endoscopy continues to this day to grow and evolve. Endoscopic retrograde cholangiography was developed in the 1970s and EUS in the 1980s, further showing the potential of endoscopy to have no boundaries. The image quality of the scope is now high-definition white light along with optical enhancements such as narrow-band imaging (NBI), with the goal to improve mucosal surface area inspection to both identify and interpret abnormal areas.