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In the academic community, such video files are often part of larger datasets, such as the (a public cohort of multimodal colonoscopy videos), which are used to train machine learning classifiers. These datasets require high-quality, focused images where polyps occupy at least 10% of the frame and are free from artifacts like blood or blurriness.

The "EVIS" prefix typically refers to the or EVIS EXERA video processor systems used globally in hospitals for colonoscopies and endoscopies. These systems capture high-definition video signals that are then processed by AI models to assist doctors in identifying abnormalities. EVIS-436.mp4

: The primary goal of these videos in a research setting is to increase the Adenoma Detection Rate (ADR) . Studies have shown that AI-assisted systems can significantly improve the detection of flat or sessile polyps that might be missed by the human eye alone. Research Utility In the academic community, such video files are

: Systems using this technology, such as those discussed in research from the National Cancer Center in Tokyo , are designed to alert endoscopists to the presence of colorectal polyps or adenomas in real-time. These systems capture high-definition video signals that are