{"id":50739,"date":"2026-06-26T17:08:25","date_gmt":"2026-06-26T20:08:25","guid":{"rendered":"https:\/\/phelcom.com\/?post_type=paper&p=50739"},"modified":"2026-06-29T11:39:34","modified_gmt":"2026-06-29T14:39:34","slug":"mbrset-a-portable-retina-fundus-photos-benchmark-dataset-for-clinical-and-demographic-prediction","status":"publish","type":"paper","link":"https:\/\/phelcom.com\/pt-br\/paper\/mbrset-a-portable-retina-fundus-photos-benchmark-dataset-for-clinical-and-demographic-prediction\/","title":{"rendered":"mBRSET: A Portable Retina Fundus Photos Benchmark Dataset for Clinical and Demographic Prediction"},"featured_media":50094,"template":"","class_list":["post-50739","paper","type-paper","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\nmBRSET: A Portable Retina Fundus Photos Benchmark Dataset for Clinical and Demographic Prediction - PHELCOM Technologies<\/title>\n<meta name=\"description\" content=\"Imaging exams, particularly retinal fundus photos, are crucial for diagnosing and monitoring ophthalmological pathologies, but traditional tabletop fundus cameras are expensive, cumbersome, and inaccessible, especially in Low- and Middle-Income Countries (LMICs), leading to a significant scarcity of ophthalmological data in these regions. The advent of compact, portable cameras offers a cost-effective solution for screening and telemedicine, crucial for preventing visual impairment in resource-limited settings. However, the existing Artificial Intelligence (AI) algorithms designed for these purposes often lack accuracy and fairness due to a lack of representative and generalizable data from LMICs and the growing portable imaging modality. To address this, the paper introduces mBRSET, the first publicly available diabetic retinopathy dataset captured using handheld retinal cameras in real-life, high-burden scenarios in Brazil, aiming to fill the gap by providing diverse data and comprehensive metadata to advance the development of fair and accurate AI-assisted diagnostic tools.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/phelcom.com\/pt-br\/paper\/mbrset-a-portable-retina-fundus-photos-benchmark-dataset-for-clinical-and-demographic-prediction\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"mBRSET: A Portable Retina Fundus Photos Benchmark Dataset for Clinical and Demographic Prediction - PHELCOM Technologies\" \/>\n<meta property=\"og:description\" content=\"Imaging exams, particularly retinal fundus photos, are crucial for diagnosing and monitoring ophthalmological pathologies, but traditional tabletop fundus cameras are expensive, cumbersome, and inaccessible, especially in Low- and Middle-Income Countries (LMICs), leading to a significant scarcity of ophthalmological data in these regions. The advent of compact, portable cameras offers a cost-effective solution for screening and telemedicine, crucial for preventing visual impairment in resource-limited settings. However, the existing Artificial Intelligence (AI) algorithms designed for these purposes often lack accuracy and fairness due to a lack of representative and generalizable data from LMICs and the growing portable imaging modality. To address this, the paper introduces mBRSET, the first publicly available diabetic retinopathy dataset captured using handheld retinal cameras in real-life, high-burden scenarios in Brazil, aiming to fill the gap by providing diverse data and comprehensive metadata to advance the development of fair and accurate AI-assisted diagnostic tools.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/phelcom.com\/pt-br\/paper\/mbrset-a-portable-retina-fundus-photos-benchmark-dataset-for-clinical-and-demographic-prediction\/\" \/>\n<meta property=\"og:site_name\" content=\"PHELCOM Technologies\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/phelcom\/\" \/>\n<meta property=\"article:modified_time\" content=\"2026-06-29T14:39:34+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/phelcom.com\/wp-content\/uploads\/2025\/11\/mBRSET-A-Portable-Retina-Fundus-Photos-Benchmark-Dataset-for-Clinical-and-Demographic-Prediction.png\" \/>\n\t<meta property=\"og:image:width\" content=\"593\" \/>\n\t<meta property=\"og:image:height\" content=\"839\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. tempo de leitura\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minuto\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/phelcom.com\\\/pt-br\\\/paper\\\/mbrset-a-portable-retina-fundus-photos-benchmark-dataset-for-clinical-and-demographic-prediction\\\/\",\"url\":\"https:\\\/\\\/phelcom.com\\\/pt-br\\\/paper\\\/mbrset-a-portable-retina-fundus-photos-benchmark-dataset-for-clinical-and-demographic-prediction\\\/\",\"name\":\"mBRSET: A Portable Retina Fundus Photos Benchmark Dataset for Clinical and Demographic Prediction - PHELCOM Technologies\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/phelcom.com\\\/pt-br\\\/en\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/phelcom.com\\\/pt-br\\\/paper\\\/mbrset-a-portable-retina-fundus-photos-benchmark-dataset-for-clinical-and-demographic-prediction\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/phelcom.com\\\/pt-br\\\/paper\\\/mbrset-a-portable-retina-fundus-photos-benchmark-dataset-for-clinical-and-demographic-prediction\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/phelcom.com\\\/wp-content\\\/uploads\\\/2025\\\/11\\\/mBRSET-A-Portable-Retina-Fundus-Photos-Benchmark-Dataset-for-Clinical-and-Demographic-Prediction.png\",\"datePublished\":\"2026-06-26T20:08:25+00:00\",\"dateModified\":\"2026-06-29T14:39:34+00:00\",\"description\":\"Imaging exams, particularly retinal fundus photos, are crucial for diagnosing and monitoring ophthalmological pathologies, but traditional tabletop fundus cameras are expensive, cumbersome, and inaccessible, especially in Low- and Middle-Income Countries (LMICs), leading to a significant scarcity of ophthalmological data in these regions. The advent of compact, portable cameras offers a cost-effective solution for screening and telemedicine, crucial for preventing visual impairment in resource-limited settings. However, the existing Artificial Intelligence (AI) algorithms designed for these purposes often lack accuracy and fairness due to a lack of representative and generalizable data from LMICs and the growing portable imaging modality. 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