Automated Recognition of Abnormalities in Gastrointestinal Endoscopic Images – Evaluation of an AI Tool for Identifying Polyps and Other Irregularities

Abstract

This study investigates the application of artificial intelligence (AI) for the automatic detection of pathological abnormalities in gastrointestinal endoscopic images. Specifically, it evaluates the performance of an AI tool in identifying and classifying lesions such as polyps and other irregularities, including inflammatory changes, within real-time endoscopic procedures. The primary objective is to assess the tool's diagnostic accuracy and its potential to improve lesion detection, thereby reducing the likelihood of overlooked abnormalities. Leveraging advanced machine learning techniques, particularly convolutional neural networks (CNNs), the AI system aims to enhance diagnostic precision and support clinicians in making prompt, evidence-based decisions. Key advantages of AI integration in endoscopy include improved sensitivity, minimized detection errors, and the potential to optimize clinical workflow efficiency. However, the study also addresses significant challenges, including the necessity for large, heterogeneous datasets for model validation, the need for standardized AI applications, and the ethical implications of AI-assisted clinical decision-making. Additionally, the potential benefits of combining AI with complementary imaging technologies, such as fluorescence imaging and spectroscopy, are explored to further enhance diagnostic capabilities. In conclusion, the study highlights the promising role of AI in gastrointestinal endoscopy while underscoring the importance of continued research, algorithmic refinement, and the establishment of regulatory frameworks to fully harness its clinical potential.