From Staining Techniques to Artificial Intelligence: A Review of Colorectal Polyps Characterization

Abstract

This review article provides a comprehensive overview of the evolving techniques in image-enhanced endoscopy (IEE) for the characterization of colorectal polyps, and the potential of artificial intelligence (AI) in revolutionizing the diagnostic accuracy of endoscopy. We discuss the historical use of dye-spray and virtual chromoendoscopy for the characterization of colorectal polyps, which are now being replaced with more advanced technologies. Specifically, we focus on the application of AI to create a “virtual biopsy” for the detection and characterization of colorectal polyps, with potential for replacing histopathological diagnosis. The incorporation of AI has the potential to provide an evolutionary learning system that aids in the diagnosis and management of patients with the best possible outcomes. A detailed analysis of the literature supporting AI-assisted diagnostic techniques for the detection and characterization of colorectal polyps, with a particular emphasis on AI’s characterization mechanism, is provided. The benefits of AI over traditional IEE techniques, including the reduction in human error in diagnosis, and its potential to provide an accurate diagnosis with similar accuracy to the gold standard are presented. However, the need for large-scale testing of AI in clinical practice and the importance of integrating patient data into the diagnostic process are acknowledged. In conclusion, the constant evolution of IEE technology and the potential for AI to revolutionize the field of endoscopy in the future are presented.

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

“…The constant updates and improvements driven by ongoing research keep TensorFlow at the forefront of machine learning technology, allowing users to leverage the latest advancements for optimal model performance. In our application, TensorFlow's adaptability, scalability, community support, and visualization tools are instrumental, making it an indispensable asset for achieving optimal results and advancing our specific goals [63], [64], [65], [66]. Thus, the neural network is trained using the squares identified by the Learning Focal Point (LFP) algorithm, illustrated in Fig.…”