Algorithm-Based Selection of MRI Images to Support Medical Education and Cognitive Skill Development

The study developed and applied a computational algorithm that selects the best medical images, aiming to improve pattern recognition skills among medical students and professionals. The algorithm, written in MATLAB, uses six quantitative metrics—sharpness, contrast, brightness, entropy, processing time, and structural artifacts—and is applied to physiological or pathological magnetic resonance neuroimaging to identify the image that most contributes to improving the visual and diagnostic performance of groups of participants. This is achieved through combinatorial weight analysis (56 combinations), in which the image with the highest number of wins is selected. The system was applied to a study with 60 participants divided into three groups (medical students, residents, and specialists in neurology), who answered two questionnaires: the first consisting of raw images selected at random, and the second using images previously processed and selected by the algorithm. The data were statistically analyzed, showing improvement in absolute and relative performance when comparing input and output, with emphasis on the domains of topographic anatomy and zonal recognition of structures. The results indicate that the use of images optimized by objective criteria improves the accuracy of image identification and participant confidence. It is concluded that the use of reproducible and measurable computational solutions is relevant in medical qualification, improving educational and diagnostic performance.