Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets

Abstract One of the most interesting and everyday natural phenomenon is the formation of different patterns after the evaporation of liquid droplets on a solid surface. The analysis of dried patterns from blood droplets has recently gained a lot of attention, experimentally and theoretically, due to...

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Autores principales:	Lama Hamadeh, Samia Imran, Martin Bencsik, Graham R. Sharpe, Michael A. Johnson, David J. Fairhurst
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5201995e6a44419cb1776e4ee2471625
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spelling	oai:doaj.org-article:5201995e6a44419cb1776e4ee24716252021-12-02T10:59:52ZMachine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets10.1038/s41598-020-59847-x2045-2322https://doaj.org/article/5201995e6a44419cb1776e4ee24716252020-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-59847-xhttps://doaj.org/toc/2045-2322Abstract One of the most interesting and everyday natural phenomenon is the formation of different patterns after the evaporation of liquid droplets on a solid surface. The analysis of dried patterns from blood droplets has recently gained a lot of attention, experimentally and theoretically, due to its potential application in diagnostic medicine and forensic science. This paper presents evidence that images of dried blood droplets have a signature revealing the exhaustion level of the person, and discloses an entirely novel approach to studying human dried blood droplet patterns. We took blood samples from 30 healthy young male volunteers before and after exhaustive exercise, which is well known to cause large changes to blood chemistry. We objectively and quantitatively analysed 1800 images of dried blood droplets, developing sophisticated image processing analysis routines and optimising a multivariate statistical machine learning algorithm. We looked for statistically relevant correlations between the patterns in the dried blood droplets and exercise-induced changes in blood chemistry. An analysis of the various measured physiological parameters was also investigated. We found that when our machine learning algorithm, which optimises a statistical model combining Principal Component Analysis (PCA) as an unsupervised learning method and Linear Discriminant Analysis (LDA) as a supervised learning method, is applied on the logarithmic power spectrum of the images, it can provide up to 95% prediction accuracy, in discriminating the physiological conditions, i.e., before or after physical exercise. This correlation is strongest when all ten images taken per volunteer per condition are averaged, rather than treated individually. Having demonstrated proof-of-principle, this method can be applied to identify diseases.Lama HamadehSamia ImranMartin BencsikGraham R. SharpeMichael A. JohnsonDavid J. FairhurstNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lama Hamadeh Samia Imran Martin Bencsik Graham R. Sharpe Michael A. Johnson David J. Fairhurst Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets
description	Abstract One of the most interesting and everyday natural phenomenon is the formation of different patterns after the evaporation of liquid droplets on a solid surface. The analysis of dried patterns from blood droplets has recently gained a lot of attention, experimentally and theoretically, due to its potential application in diagnostic medicine and forensic science. This paper presents evidence that images of dried blood droplets have a signature revealing the exhaustion level of the person, and discloses an entirely novel approach to studying human dried blood droplet patterns. We took blood samples from 30 healthy young male volunteers before and after exhaustive exercise, which is well known to cause large changes to blood chemistry. We objectively and quantitatively analysed 1800 images of dried blood droplets, developing sophisticated image processing analysis routines and optimising a multivariate statistical machine learning algorithm. We looked for statistically relevant correlations between the patterns in the dried blood droplets and exercise-induced changes in blood chemistry. An analysis of the various measured physiological parameters was also investigated. We found that when our machine learning algorithm, which optimises a statistical model combining Principal Component Analysis (PCA) as an unsupervised learning method and Linear Discriminant Analysis (LDA) as a supervised learning method, is applied on the logarithmic power spectrum of the images, it can provide up to 95% prediction accuracy, in discriminating the physiological conditions, i.e., before or after physical exercise. This correlation is strongest when all ten images taken per volunteer per condition are averaged, rather than treated individually. Having demonstrated proof-of-principle, this method can be applied to identify diseases.
format	article
author	Lama Hamadeh Samia Imran Martin Bencsik Graham R. Sharpe Michael A. Johnson David J. Fairhurst
author_facet	Lama Hamadeh Samia Imran Martin Bencsik Graham R. Sharpe Michael A. Johnson David J. Fairhurst
author_sort	Lama Hamadeh
title	Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets
title_short	Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets
title_full	Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets
title_fullStr	Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets
title_full_unstemmed	Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets
title_sort	machine learning analysis for quantitative discrimination of dried blood droplets
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/5201995e6a44419cb1776e4ee2471625
work_keys_str_mv	AT lamahamadeh machinelearninganalysisforquantitativediscriminationofdriedblooddroplets AT samiaimran machinelearninganalysisforquantitativediscriminationofdriedblooddroplets AT martinbencsik machinelearninganalysisforquantitativediscriminationofdriedblooddroplets AT grahamrsharpe machinelearninganalysisforquantitativediscriminationofdriedblooddroplets AT michaelajohnson machinelearninganalysisforquantitativediscriminationofdriedblooddroplets AT davidjfairhurst machinelearninganalysisforquantitativediscriminationofdriedblooddroplets
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Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets

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