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 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. The generation of complex and varied patterns as a result of the liquid drying process is a common yet intriguing phenomenon in nature 1. One of the first studies of drying droplets began with the publication in 1997 by Deegan et al. 2 with an explanation of the formation of a ring-like structure commonly called a 'coffee ring' resulting from the evaporation of a droplet containing microparticles 3. The reason for the formation of such a pattern is that the contact line, where the droplet meets the substrate, is pinned in place, due to the particles in the liquid. Consequently, liquid from the center of the droplet must flow outwards to replenish the liquid that evaporates at the rim bringing the particles with it 4. The generic form of the coffee ring has been investigated and analysed by many researchers using different liquids including coffee 2,5,6 , nanofluids 7 , polymers 8,9 and DNA 10. Interestingly, in the past few decades, the analysis of patterns from dried droplets of biological fluids 11-13 has gained a lot of attention due to applications in fields such as biomedical 14-16 and forensic sciences 16,17 , with some reports of successful medical applications of the Litos test for analysing urine dropl...

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