Machine learning analysis for quantitative discrimination of dried blood droplets

Hamadeh, L. ORCID: 0000-0002-1766-532X, Imran, S., Bencsik, M. ORCID: 0000-0002-6278-0378, Sharpe, G.R. ORCID: 0000-0002-4575-2332, Johnson, M.A. ORCID: 0000-0002-8226-9438 and Fairhurst, D.J. ORCID: 0000-0001-5311-0762, 2020. Machine learning analysis for quantitative discrimination of dried blood droplets. Scientific Reports, 10: 3313. ISSN 2045-2322

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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.

Item Type: Journal article
Publication Title: Scientific Reports
Creators: Hamadeh, L., Imran, S., Bencsik, M., Sharpe, G.R., Johnson, M.A. and Fairhurst, D.J.
Publisher: Springer
Date: 2020
Volume: 10
ISSN: 2045-2322
Identifiers:
NumberType
10.1038/s41598-020-59847-xDOI
1297677Other
Rights: © the author(s) 2020. This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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.
Divisions: Schools > School of Science and Technology
Record created by: Jill Tomkinson
Date Added: 26 Feb 2020 11:00
Last Modified: 21 Jul 2020 10:54
Related URLs:
URI: http://irep.ntu.ac.uk/id/eprint/39289

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