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Accuracy of breast density assessment using artificial intelligence by convolutional neural network for carriers of UGT1A1 polymorphisms with Gilbert's Syndrome – a pilot study

Crossref DOI link: https://doi.org/10.1016/j.clinsp.2026.101038

Published Print: 2026-01

Update policy: https://doi.org/10.1016/elsevier_cm_policy

Authors

Maesaka, Jonathan Yugo https://orcid.org/0000-0002-1725-8732
Mota, Bruna Salani https://orcid.org/0000-0001-9567-1066
Shimizu, Carlos https://orcid.org/0000-0001-9364-4750
Rocha, Bruno Aragão https://orcid.org/0000-0001-5288-8150
Vianna, Luis Gustavo Rocha https://orcid.org/0000-0002-0404-8023
Cerri, Giovanni Guido https://orcid.org/0000-0002-6707-8721
Filassi, José Roberto https://orcid.org/0000-0002-1192-4220
Maciel, Gustavo Arantes Rosa https://orcid.org/0000-0001-9097-8528
Soares-Jr, José Maria https://orcid.org/0000-0003-0774-9404
Ono, Suzane Kioko https://orcid.org/0000-0002-6963-0911
Baracat, Edmund Chada https://orcid.org/0000-0003-0111-9030
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Text and Data Mining valid from 2026-01-01

Text and Data Mining valid from 2026-01-01

Version of Record valid from 2026-06-25
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This article is maintained by: Elsevier

Article Title: Accuracy of breast density assessment using artificial intelligence by convolutional neural network for carriers of UGT1A1 polymorphisms with Gilbert's Syndrome – a pilot study

Journal Title: Clinics

CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.clinsp.2026.101038

Content Type: article

Copyright: © 2026 HCFMUSP. Published by Elsevier España, S.L.U.

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