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A dataset of skin lesion images collected in Argentina for the evaluation of AI tools in this population
In recent years, numerous dermatological image databases have been published to make possible the development and validation of artificial intelligence-based technologies to support healthcare professionals in the diagnosis of skin diseases. However, the generation of these datasets confined to cert...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584927/ https://www.ncbi.nlm.nih.gov/pubmed/37853053 http://dx.doi.org/10.1038/s41597-023-02630-0 |
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author | Ricci Lara, María Agustina Rodríguez Kowalczuk, María Victoria Lisa Eliceche, Maite Ferraresso, María Guillermina Luna, Daniel Roberto Benitez, Sonia Elizabeth Mazzuoccolo, Luis Daniel |
author_facet | Ricci Lara, María Agustina Rodríguez Kowalczuk, María Victoria Lisa Eliceche, Maite Ferraresso, María Guillermina Luna, Daniel Roberto Benitez, Sonia Elizabeth Mazzuoccolo, Luis Daniel |
author_sort | Ricci Lara, María Agustina |
collection | PubMed |
description | In recent years, numerous dermatological image databases have been published to make possible the development and validation of artificial intelligence-based technologies to support healthcare professionals in the diagnosis of skin diseases. However, the generation of these datasets confined to certain countries as well as the lack of demographic information accompanying the images, prevents having a real knowledge of in which populations these models could be used. Consequently, this hinders the translation of the models to the clinical setting. This has led the scientific community to encourage the detailed and transparent reporting of the databases used for artificial intelligence developments, as well as to promote the formation of genuinely international databases that can be representative of the world population. Through this work, we seek to provide details of the processing stages of the first public database of dermoscopy and clinical images created in a hospital in Argentina. The dataset comprises 1,616 images corresponding to 1,246 unique lesions collected from 623 patients. |
format | Online Article Text |
id | pubmed-10584927 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105849272023-10-20 A dataset of skin lesion images collected in Argentina for the evaluation of AI tools in this population Ricci Lara, María Agustina Rodríguez Kowalczuk, María Victoria Lisa Eliceche, Maite Ferraresso, María Guillermina Luna, Daniel Roberto Benitez, Sonia Elizabeth Mazzuoccolo, Luis Daniel Sci Data Data Descriptor In recent years, numerous dermatological image databases have been published to make possible the development and validation of artificial intelligence-based technologies to support healthcare professionals in the diagnosis of skin diseases. However, the generation of these datasets confined to certain countries as well as the lack of demographic information accompanying the images, prevents having a real knowledge of in which populations these models could be used. Consequently, this hinders the translation of the models to the clinical setting. This has led the scientific community to encourage the detailed and transparent reporting of the databases used for artificial intelligence developments, as well as to promote the formation of genuinely international databases that can be representative of the world population. Through this work, we seek to provide details of the processing stages of the first public database of dermoscopy and clinical images created in a hospital in Argentina. The dataset comprises 1,616 images corresponding to 1,246 unique lesions collected from 623 patients. Nature Publishing Group UK 2023-10-18 /pmc/articles/PMC10584927/ /pubmed/37853053 http://dx.doi.org/10.1038/s41597-023-02630-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Ricci Lara, María Agustina Rodríguez Kowalczuk, María Victoria Lisa Eliceche, Maite Ferraresso, María Guillermina Luna, Daniel Roberto Benitez, Sonia Elizabeth Mazzuoccolo, Luis Daniel A dataset of skin lesion images collected in Argentina for the evaluation of AI tools in this population |
title | A dataset of skin lesion images collected in Argentina for the evaluation of AI tools in this population |
title_full | A dataset of skin lesion images collected in Argentina for the evaluation of AI tools in this population |
title_fullStr | A dataset of skin lesion images collected in Argentina for the evaluation of AI tools in this population |
title_full_unstemmed | A dataset of skin lesion images collected in Argentina for the evaluation of AI tools in this population |
title_short | A dataset of skin lesion images collected in Argentina for the evaluation of AI tools in this population |
title_sort | dataset of skin lesion images collected in argentina for the evaluation of ai tools in this population |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584927/ https://www.ncbi.nlm.nih.gov/pubmed/37853053 http://dx.doi.org/10.1038/s41597-023-02630-0 |
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