AKUImg: A database of cartilage images of Alkaptonuria patients. (July 2020)
- Record Type:
- Journal Article
- Title:
- AKUImg: A database of cartilage images of Alkaptonuria patients. (July 2020)
- Main Title:
- AKUImg: A database of cartilage images of Alkaptonuria patients
- Authors:
- Rossi, Alberto
Giacomini, Giorgia
Cicaloni, Vittoria
Galderisi, Silvia
Milella, Maria Serena
Bernini, Andrea
Millucci, Lia
Spiga, Ottavia
Bianchini, Monica
Santucci, Annalisa - Abstract:
- Abstract: 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. Graphical abstract: Highlights: Development of the first Alkaptonuria-dedicated image repository. Release of an online tool to distinguish AKU/control cartilage slides for supporting researchers. Definition of an easily extendable method to be applied to otherAbstract: 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. Graphical abstract: Highlights: Development of the first Alkaptonuria-dedicated image repository. Release of an online tool to distinguish AKU/control cartilage slides for supporting researchers. Definition of an easily extendable method to be applied to other metabolic diseases. … (more)
- Is Part Of:
- Computers in biology and medicine. Volume 122(2020)
- Journal:
- Computers in biology and medicine
- Issue:
- Volume 122(2020)
- Issue Display:
- Volume 122, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 122
- Issue:
- 2020
- Issue Sort Value:
- 2020-0122-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-07
- Subjects:
- Alkaptonuria -- Rare disease -- Precision medicine -- Histopatological images
Medicine -- Data processing -- Periodicals
Biology -- Data processing -- Periodicals
610.285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00104825/ ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.compbiomed.2020.103863 ↗
- Languages:
- English
- ISSNs:
- 0010-4825
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.880000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 13369.xml