https://www.rmib.mx/index.php/rmib/issue/feed 2026-01-13T15:29:06+00:00 Dra. Saida Mayela García Montes rib.somib@gmail.com Open Journal Systems <center> <p><strong>MISSION</strong></p> <p align="left"><em>La Revista Mexicana de Ingeniería Biomédica</em> (The Mexican Journal of Biomedical Engineering, RMIB, for its Spanish acronym) is a publication oriented to the dissemination of papers of the Mexican and international scientific community whose lines of research are aligned to the improvement of the quality of life through engineering techniques.</p> <p align="left">The papers that are considered for being published in the RMIB must be original, unpublished, and first rate, and they can cover the areas of Medical Instrumentation, Biomedical Signals, Medical Information Technology, Biomaterials, Clinical Engineering, Physiological Models, and Medical Imaging as well as lines of research related to various branches of engineering applied to the health sciences.</p> <p align="left">The RMIB is an electronic publication continuously released since 2020, structured into three volumes (January, May, September) by the Mexican Society of Biomedical Engineering, founded since 1979. It publishes articles in spanish and english and is aimed at academics, researchers and professionals interested in the subspecialties of Biomedical Engineering.</p> <p><strong>INDEXES</strong></p> <p><em>La Revista Mexicana de Ingeniería Biomédica</em> is a quarterly publication, and it is found in the following indexes:</p> <p><img src="https://www.rmib.mx/public/site/images/administrador/índices_y_repositorios_(1100_×_1000 px).jpg" /></p> </center> https://www.rmib.mx/index.php/rmib/article/view/1520 2025-07-22T22:00:24+00:00 Jorge Ernesto Gonzalez Diaz d04010291@orizaba.tecnm.mx Roberto Márquez Castro m16011135@orizaba.tecnm.mx José Luis Sánchez Cervantes jose.sc@orizaba.tecnm.mx Giner Alor Hernández giner.ah@orizaba.tecnm.mx Augusto Javier Reyes Delgado d17010207@orizaba.tecnm.mx Alfonso Flores Leal alfonso.fl@orizaba.tecnm.mx Martin Mancilla Gomez mmancilla@uv.mx

<p>Age-related macular degeneration (AMD) is a progressive eye disease that primarily affects individuals over 50 years old. Among the AMD variants, wet is the most severe, as it represents the advanced stage of dry AMD and can cause severe vision loss if not detected in time. This study focuses on the development of WAMDS2, a web module designed to identify characteristics associated with wet AMD, facilitating early and accurate detection. To achieve this, a literature review was conducted on AMD and advanced techniques in computer vision and deep learning. The proposed model integrates Swin Transformer V2, a vision transformer implemented in PyTorch, to analyze fundus images and classify the different stages of the disease. The system’s performance was evaluated using metrics such as accuracy, recall, and F1-Score. An accuracy of 84.76% was achieved on the test set, suggesting its feasibility in clinical settings. The obtained results highlight the potential of WAMDS2 in ophthalmology and computer vision, demonstrating its capability to enhance automated diagnosis and patient care.</p>

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