AKUImg: A database of cartilage images of Alkaptonuria patients.


Journal

Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250

Informations de publication

Date de publication:
07 2020
Historique:
received: 26 02 2020
revised: 12 06 2020
accepted: 12 06 2020
entrez: 14 7 2020
pubmed: 14 7 2020
medline: 22 6 2021
Statut: ppublish

Résumé

ApreciseKUre is a multi-purpose digital platform facilitating data collection, integration and analysis for patients affected by Alkaptonuria (AKU), an ultra-rare autosomal recessive genetic disease. We present an ApreciseKUre plugin, called AKUImg, dedicated to the storage and analysis of AKU histopathological slides, in order to create a Precision Medicine Ecosystem (PME), where images can be shared among registered researchers and clinicians to extend the AKU knowledge network. AKUImg includes a new set of AKU images taken from cartilage tissues acquired by means of a microscopic technique. The repository, in accordance to ethical policies, is publicly available after a registration request, to give to scientists the opportunity to study, investigate and compare such precious resources. AKUImg is also integrated with a preliminary but accurate predictive system able to discriminate the presence/absence of AKU by comparing histopatological affected/control images. The algorithm is based on a standard image processing approach, namely histogram comparison, resulting to be particularly effective in performing image classification, and constitutes a useful guide for non-AKU researchers and clinicians.

Identifiants

pubmed: 32658739
pii: S0010-4825(20)30221-3
doi: 10.1016/j.compbiomed.2020.103863
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

103863

Informations de copyright

Copyright © 2020 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Alberto Rossi (A)

University of Florence, Department of Information Engineering, Via di Santa Marta, Florence, Italy; University of Siena, Department of Information Engineering and Mathematics, Via Roma 56, Siena, Italy. Electronic address: alberto.rossi@unifi.it.

Giorgia Giacomini (G)

University of Siena, Department of Biotechnology, Chemistry and Pharmacy, Via Aldo Moro 2, Siena, Italy; University of Siena, Department of Information Engineering and Mathematics, Via Roma 56, Siena, Italy.

Vittoria Cicaloni (V)

University of Siena, Department of Biotechnology, Chemistry and Pharmacy, Via Aldo Moro 2, Siena, Italy; Toscana Life Sciences Foundation, Via Fiorentina 1, Siena, Italy.

Silvia Galderisi (S)

University of Siena, Department of Biotechnology, Chemistry and Pharmacy, Via Aldo Moro 2, Siena, Italy.

Maria Serena Milella (MS)

University of Siena, Department of Biotechnology, Chemistry and Pharmacy, Via Aldo Moro 2, Siena, Italy.

Andrea Bernini (A)

University of Siena, Department of Biotechnology, Chemistry and Pharmacy, Via Aldo Moro 2, Siena, Italy.

Lia Millucci (L)

University of Siena, Department of Biotechnology, Chemistry and Pharmacy, Via Aldo Moro 2, Siena, Italy.

Ottavia Spiga (O)

University of Siena, Department of Biotechnology, Chemistry and Pharmacy, Via Aldo Moro 2, Siena, Italy.

Monica Bianchini (M)

University of Siena, Department of Information Engineering and Mathematics, Via Roma 56, Siena, Italy.

Annalisa Santucci (A)

University of Siena, Department of Biotechnology, Chemistry and Pharmacy, Via Aldo Moro 2, Siena, Italy.

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