Masterclass: Advanced GI Endoscopic Imaging (On-Demand) | December 2025-Saeed

Saeed - Learning Curve and Training

Pdf Summary

This document discusses the learning curve and training aspects of advanced endoscopic imaging techniques, focusing on colorectal neoplasia detection and characterization. Advanced imaging methods, including high-definition white light (HDWL), dye-based, and virtual chromoendoscopy, alongside artificial intelligence (AI), enable real-time optical diagnosis, influencing clinical decisions during endoscopy procedures. Structured training accelerates proficiency, improves safety, and may reduce costs and pathology workload.<br /><br />Current guidelines emphasize adherence to performance thresholds (PIVI), high-confidence assessments, comprehensive documentation, and quality assurance. While some studies (e.g., DISCARD-2) have questioned routine clinical use of certain imaging techniques, organizations like the ESGE have set competency standards and training criteria.<br /><br />Training should start with mastering basic high-quality endoscopy techniques with appropriate equipment. Effective training courses rely on validated classification systems and include in vivo phases for practical learning. Self-learning with image-set mastery validated at the scope, coupled with feedback from histology and expert review, is recommended, with AI anticipated to enhance future training.<br /><br />Evidence shows brief, focused didactic or video-based training significantly improves diagnostic accuracy, with many studies reporting considerable accuracy gain after short sessions. Chromoendoscopy and virtual chromoendoscopy (NBI, i-scan, BLI) demonstrate steep learning curves but achieve high sensitivity and negative predictive values after training. Other modalities like autofluorescence imaging, confocal laser endomicroscopy, and volumetric laser endomicroscopy also benefit from brief targeted training.<br /><br />AI-assisted optical diagnosis has shown to match required performance thresholds, providing rapid learning curves and increased diagnostic confidence, especially for non-experts. AI integration is key to future training pathways, enabling real-time feedback, automated performance audits, and scalable digital solutions.<br /><br />Conclusions highlight the need for standardized advanced imaging training modules, integration of AI tools for continuous competency tracking, and the absence of current standardized curricula in GI fellowship programs. Overall, short, structured courses combined with AI promise to revolutionize endoscopic imaging training and practice.

Keywords

advanced endoscopic imaging

colorectal neoplasia detection

high-definition white light (HDWL)

virtual chromoendoscopy

artificial intelligence (AI) in endoscopy

training and learning curve

performance thresholds (PIVI)

classification systems

diagnostic accuracy improvement

standardized training modules