Non-invasive multimodal dataset for the detection of iron deficiency anemia in young adults: fingertip videos, palm videos, and nail photographs
Iron deficiency anemia affects a significant proportion of the young population in both rural and urban areas of Peru. In response to the need for non-invasive, accessible, and reproducible methods for its detection, we developed this dataset as part of a research project funded by the Universidad...
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| Format: | Article |
| Language: | Spanish |
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Universidad Nacional de San Martín
2025-07-01
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| Series: | Revista Científica de Sistemas e Informática |
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| Online Access: | https://revistas.unsm.edu.pe/index.php/rcsi/article/view/955 |
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| author | Miguel Angel Valles-Coral Richard Injante Jorge Raul Navarro-Cabrera Lloy Pinedo Luis Gerardo Salazar-Ramirez María Elena Farro-Roque Luz Karen Quintanilla-Morales |
| author_facet | Miguel Angel Valles-Coral Richard Injante Jorge Raul Navarro-Cabrera Lloy Pinedo Luis Gerardo Salazar-Ramirez María Elena Farro-Roque Luz Karen Quintanilla-Morales |
| author_sort | Miguel Angel Valles-Coral |
| collection | DOAJ |
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Iron deficiency anemia affects a significant proportion of the young population in both rural and urban areas of Peru. In response to the need for non-invasive, accessible, and reproducible methods for its detection, we developed this dataset as part of a research project funded by the Universidad Nacional de San Martín, which applies computer vision techniques to automatically classify patients as anemic or non-anemic. The aim is to provide a standardized base of videos and images that supports the development and validation of classification and regression models to estimate hemoglobin levels without the need for blood extraction. This data paper presents a multimodal dataset composed of non-invasive visual records collected to facilitate the detection of iron deficiency anemia in young adults through machine learning models. The dataset includes 909 fingertip videos, 909 palm videos (with controlled hand opening), and 909 nail photographs, all linked to individual clinical data such as age, sex, hemoglobin level, and symptomatology.
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| format | Article |
| id | doaj-art-e04445c7b8ab4c0aacd03de33269cc03 |
| institution | Kabale University |
| issn | 2709-992X |
| language | Spanish |
| publishDate | 2025-07-01 |
| publisher | Universidad Nacional de San Martín |
| record_format | Article |
| series | Revista Científica de Sistemas e Informática |
| spelling | doaj-art-e04445c7b8ab4c0aacd03de33269cc032025-08-20T03:38:12ZspaUniversidad Nacional de San MartínRevista Científica de Sistemas e Informática2709-992X2025-07-015210.51252/rcsi.v5i2.955Non-invasive multimodal dataset for the detection of iron deficiency anemia in young adults: fingertip videos, palm videos, and nail photographs Miguel Angel Valles-Coral0https://orcid.org/0000-0002-8806-2892Richard Injante1https://orcid.org/0000-0002-2449-8937Jorge Raul Navarro-Cabrera2https://orcid.org/0000-0002-7369-4459Lloy Pinedo3https://orcid.org/0000-0002-5569-8739Luis Gerardo Salazar-Ramirez4María Elena Farro-Roque5https://orcid.org/0000-0001-5163-786XLuz Karen Quintanilla-Morales6Universidad Nacional de San Martín Universidad Nacional de San Martín Universidad Nacional de San MartínUniversidad Nacional de San MartínUniversidad Nacional de San MartínUniversidad Nacional de San Martín Universidad Nacional de San Martín Iron deficiency anemia affects a significant proportion of the young population in both rural and urban areas of Peru. In response to the need for non-invasive, accessible, and reproducible methods for its detection, we developed this dataset as part of a research project funded by the Universidad Nacional de San Martín, which applies computer vision techniques to automatically classify patients as anemic or non-anemic. The aim is to provide a standardized base of videos and images that supports the development and validation of classification and regression models to estimate hemoglobin levels without the need for blood extraction. This data paper presents a multimodal dataset composed of non-invasive visual records collected to facilitate the detection of iron deficiency anemia in young adults through machine learning models. The dataset includes 909 fingertip videos, 909 palm videos (with controlled hand opening), and 909 nail photographs, all linked to individual clinical data such as age, sex, hemoglobin level, and symptomatology. https://revistas.unsm.edu.pe/index.php/rcsi/article/view/955artificial intelligencebiomedical videosclinical datasetcomputer visionhemoglobinmachine learning |
| spellingShingle | Miguel Angel Valles-Coral Richard Injante Jorge Raul Navarro-Cabrera Lloy Pinedo Luis Gerardo Salazar-Ramirez María Elena Farro-Roque Luz Karen Quintanilla-Morales Non-invasive multimodal dataset for the detection of iron deficiency anemia in young adults: fingertip videos, palm videos, and nail photographs Revista Científica de Sistemas e Informática artificial intelligence biomedical videos clinical dataset computer vision hemoglobin machine learning |
| title | Non-invasive multimodal dataset for the detection of iron deficiency anemia in young adults: fingertip videos, palm videos, and nail photographs |
| title_full | Non-invasive multimodal dataset for the detection of iron deficiency anemia in young adults: fingertip videos, palm videos, and nail photographs |
| title_fullStr | Non-invasive multimodal dataset for the detection of iron deficiency anemia in young adults: fingertip videos, palm videos, and nail photographs |
| title_full_unstemmed | Non-invasive multimodal dataset for the detection of iron deficiency anemia in young adults: fingertip videos, palm videos, and nail photographs |
| title_short | Non-invasive multimodal dataset for the detection of iron deficiency anemia in young adults: fingertip videos, palm videos, and nail photographs |
| title_sort | non invasive multimodal dataset for the detection of iron deficiency anemia in young adults fingertip videos palm videos and nail photographs |
| topic | artificial intelligence biomedical videos clinical dataset computer vision hemoglobin machine learning |
| url | https://revistas.unsm.edu.pe/index.php/rcsi/article/view/955 |
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