Overall workflow of the Machine Learning (ML) system in CDaaS
The global problem of antibiotic resistance is fast becoming one of the major scientific issues of modern times. The development of new antibiotics is slow and difficult work, but bacterial resistance is decreasing our arsenal of existing drugs posing a catastrophic threat as ordinary infections become untreatable. This is a particularly evident in the current COVID-19 pandemic. Currently, there is no efficient and fast technical solution to overcome this phenomenon, rather the diagnosis is based on clinical examination in a doctor’s clinic or in hospital, in addition to some biochemical tests in labs which might take up to a few days to get the results.
CDaaS uses artificial intelligence (AI) and machine learning technology to provide infection identification of either bacterial or viral causes, based on samples provided to the CDaaS system by the Point of Care (PoC) givers i.e. GPs or consultants. The CDaaS (Clinical Data as a Service) platform gives GPs, surgeons and third parties access to synthesised diagnostic medical data allowing them to determine an early and accurate infection diagnosis solution.
The heart of CDaaS is an AI machine learning-based system that provides the critical analysis for submitted biomarker samples, i.e. blood pressure, body temperature, based on multiple indicators within the samples.
CDaaS focuses on extending the market around lab-on-a-chip devices while enabling the creation of a mobile application that can assist medical doctors in assessing the nature of infections in the GP practice or hospital setting. This will be imperative to the rate of diagnosing patients with COVID-19 thus reducing the spread of the virus.
For more information on the project visit https://www.tssg.org/projects/cdaas/
Researchers: Martin Tolan (TSSG), Yahya Almardeny (TSSG), Peter Scanlon (TSSG) Frances Cleary (TSSG).