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Facial erythema detects diabetic neuropathy using the fusion of machine learning, random matrix theory and self organized criticality

Rubeosis faciei diabeticorum, caused by microangiopathy and characterized by a chronic facial erythema, is associated with diabetic neuropathy. In clinical practice, facial erythema of patients with diabetes is evaluated based on subjective observations of visible redness, which often goes unnoticed...

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Autores principales: Nadimi, Esmaeil S., Majtner, Tomas, Yderstraede, Knud B., Blanes-Vidal, Victoria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7546636/
https://www.ncbi.nlm.nih.gov/pubmed/33033383
http://dx.doi.org/10.1038/s41598-020-73744-3
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author Nadimi, Esmaeil S.
Majtner, Tomas
Yderstraede, Knud B.
Blanes-Vidal, Victoria
author_facet Nadimi, Esmaeil S.
Majtner, Tomas
Yderstraede, Knud B.
Blanes-Vidal, Victoria
author_sort Nadimi, Esmaeil S.
collection PubMed
description Rubeosis faciei diabeticorum, caused by microangiopathy and characterized by a chronic facial erythema, is associated with diabetic neuropathy. In clinical practice, facial erythema of patients with diabetes is evaluated based on subjective observations of visible redness, which often goes unnoticed leading to microangiopathic complications. To address this major shortcoming, we designed a contactless, non-invasive diagnostic point-of-care-device (POCD) consisting of a digital camera and a screen. Our solution relies on (1) recording videos of subject’s face (2) applying Eulerian video magnification to videos to reveal important subtle color changes in subject’s skin that fall outside human visual limits (3) obtaining spatio-temporal tensor expression profile of these variations (4) studying empirical spectral density (ESD) function of the largest eigenvalues of the tensors using random matrix theory (5) quantifying ESD functions by modeling the tails and decay rates using power law in systems exhibiting self-organized-criticality and (6) designing an optimal ensemble of learners to classify subjects into those with diabetic neuropathy and those of a control group. By analyzing a short video, we obtained a sensitivity of 100% in detecting subjects diagnosed with diabetic neuropathy. Our POCD paves the way towards the development of an inexpensive home-based solution for early detection of diabetic neuropathy and its associated complications.
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spelling pubmed-75466362020-10-14 Facial erythema detects diabetic neuropathy using the fusion of machine learning, random matrix theory and self organized criticality Nadimi, Esmaeil S. Majtner, Tomas Yderstraede, Knud B. Blanes-Vidal, Victoria Sci Rep Article Rubeosis faciei diabeticorum, caused by microangiopathy and characterized by a chronic facial erythema, is associated with diabetic neuropathy. In clinical practice, facial erythema of patients with diabetes is evaluated based on subjective observations of visible redness, which often goes unnoticed leading to microangiopathic complications. To address this major shortcoming, we designed a contactless, non-invasive diagnostic point-of-care-device (POCD) consisting of a digital camera and a screen. Our solution relies on (1) recording videos of subject’s face (2) applying Eulerian video magnification to videos to reveal important subtle color changes in subject’s skin that fall outside human visual limits (3) obtaining spatio-temporal tensor expression profile of these variations (4) studying empirical spectral density (ESD) function of the largest eigenvalues of the tensors using random matrix theory (5) quantifying ESD functions by modeling the tails and decay rates using power law in systems exhibiting self-organized-criticality and (6) designing an optimal ensemble of learners to classify subjects into those with diabetic neuropathy and those of a control group. By analyzing a short video, we obtained a sensitivity of 100% in detecting subjects diagnosed with diabetic neuropathy. Our POCD paves the way towards the development of an inexpensive home-based solution for early detection of diabetic neuropathy and its associated complications. Nature Publishing Group UK 2020-10-08 /pmc/articles/PMC7546636/ /pubmed/33033383 http://dx.doi.org/10.1038/s41598-020-73744-3 Text en © The Author(s) 2020 Open AccessThis 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/.
spellingShingle Article
Nadimi, Esmaeil S.
Majtner, Tomas
Yderstraede, Knud B.
Blanes-Vidal, Victoria
Facial erythema detects diabetic neuropathy using the fusion of machine learning, random matrix theory and self organized criticality
title Facial erythema detects diabetic neuropathy using the fusion of machine learning, random matrix theory and self organized criticality
title_full Facial erythema detects diabetic neuropathy using the fusion of machine learning, random matrix theory and self organized criticality
title_fullStr Facial erythema detects diabetic neuropathy using the fusion of machine learning, random matrix theory and self organized criticality
title_full_unstemmed Facial erythema detects diabetic neuropathy using the fusion of machine learning, random matrix theory and self organized criticality
title_short Facial erythema detects diabetic neuropathy using the fusion of machine learning, random matrix theory and self organized criticality
title_sort facial erythema detects diabetic neuropathy using the fusion of machine learning, random matrix theory and self organized criticality
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7546636/
https://www.ncbi.nlm.nih.gov/pubmed/33033383
http://dx.doi.org/10.1038/s41598-020-73744-3
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