Machine Learning Analysis for Quantitative Discrimination of Dried Blood Droplets
Open Access
- 24 February 2020
- journal article
- research article
- Published by Springer Science and Business Media LLC in Scientific Reports
- Vol. 10 (1), 1-13
- https://doi.org/10.1038/s41598-020-59847-x
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.Keywords
This publication has 72 references indexed in Scilit:
- Microstructural analysis of biological fluidsTechnical Physics, 2012
- Influence of Substrate Nature on the Evaporation of a Sessile Drop of BloodJournal of Heat Transfer, 2012
- From craquelures to spiral crack patterns: influence of layer thickness on the crack patterns induced by desiccationSoft Matter, 2011
- Self-Organized Crystallization Patterns from Evaporating Droplets of Common Wheat Grain Leakages as a Potential Tool for Quality AnalysisThe Scientific World Journal, 2011
- High-dimensional pattern regression using machine learning: From medical images to continuous clinical variablesNeuroImage, 2010
- Evolution of mud-crack patterns during repeated drying cyclesSoft Matter, 2010
- Morphologies resulting from the directional propagation of fracturesPhysical Review E, 2003
- A framework for noise‐power spectrum analysis of multidimensional imagesMedical Physics, 2002
- A change in the physical state of a nonequilibrium blood plasma protein film in patients with carcinomaTechnical Physics, 2002
- PCA versus LDAIEEE Transactions on Pattern Analysis and Machine Intelligence, 2001