Machine Learning based Point-of-Care Disease Diagnostics using Dried patterns formed by E. coli bacteria-laden Sessile Urine Droplets

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Abstract

Urinary Tract Infection (UTI), primarily caused by E. coli bacteria, is a rising global health concern, affecting women and the elderly at a disproportionately high rate. Despite recent advancements in the diagnosis techniques, a critical gap still persists in terms of time delay, high cost and false-positive predictions. Hence there is a critical requirement for rapid, reliable and cheap point-of-care diagnostic tools. As a vital step towards addressing this need, here we propose an Artificial Intelligence based diagnosis technique which involves microscopic images of dried patterns formed by E. coli bacteria-laden sessile urine droplets. In this study, the variation in the underlying pattern formation behavior with the change in bacterial concentration has been perceived through machine learning (deep residual network based) model pipeline for diagnosis and severity estimation. Image classification (pattern analysis) has been performed based on dried deposits/patterns obtained from evaporated bacteria-laden sessile urine droplets. In addition, the impact of bacterial concentration in a given urine sample has been studied, as an attempt to qualitatively estimate a severity index. Overall, this study focuses on understanding and unleashing the potential of analyzing dried deposit/pattern for UTI diagnosis, as a quick, cheap and accessible point-of-care application, which can be extended to cyber-physical systems as a robust, deployable diagnostic tool particularly in rural areas as a first-line diagnostic tool, allowing users to perform an initial self-assessment prior to consulting a medical professional.

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