Image classification using optical-digital image enhancement methods and deep learning in endoscopic examinations

Gastrointestinal tract (GIT) diseases remain among the most pressing challenges in modern medicine, with external environmental factors affecting human health negatively. The rapid development of artificial intelligence and computer vision is aimed at improving existing methods for disease detection through the analysis of biomedical images. This study summarizes recent scientific advances in endoscopy that integrate machine learning with both digital and opto-digital image enhancement technologies. The paper reviews sources evaluating the use of white light imaging (WLI) and various enhancement modes such as NBI, BLI, i-Scan, and FICE. A classification of endoscopic image enhancement methods is provided, along with recommendations for their application based on anatomical regions of the GIT. In addition, the study presents an overview of the use of enhanced endoscopic imaging and its combination with computer vision for increasing diagnostic parameters such as accuracy, specificity, and sensitivity based on data obtained during gastrointestinal examinations. On average, sensitivity increased by 17%, and specificity by 39% compared to results from novice endoscopists. The study also explores the trend of developing new architectural approaches for integrating opto-digital and digital methods into machine learning, as well as a comparison of diagnostic quality between AI systems and human endoscopists. An analysis of the current state of such technologies is presented, along with prospects for the development of machine learning in automated computer-aided diagnosis (CAD) systems. Challenges related to classification accuracy degradation are identified, their causes analyzed, and recommendations for performance improvement are provided. Automated CAD systems are viewed as an effective support tool for young physicians in pathology detection, helping to reduce examination time and minimize the risk of missing critical areas that require focused attention.