COVID-19 infection and machine learning using Artificial Intelligence [COVID-19]
Research type
Research Study
Full title
Rapid diagnosis of COVID-19 positive patients with Artificial Intelligence (AI) algorithm using clinical and image analytical parameters to evaluate the lymphocyte subsets in the peripheral blood.
IRAS ID
282667
Contact name
Mahesh Prahladan
Contact email
Sponsor organisation
East Suffolk and North Essex Foundation NHS Trust
Duration of Study in the UK
0 years, 6 months, 1 days
Research summary
There is increasing evidence of numbers of specific white blood cells, namely the lymphocytes, being negatively affected in patients suffering from SARS-CoV-2 infection, causing COVID-19. The aim of our study is to establish whether the morphology of lymphocytes from COVID-19 patients is significantly different from that of control patients, i.e. with other unrelated viral infections or non suspected viral infections, to identify a unique disease “fingerprint“, based on a simple blood smear. \n\nInitially, individual control and COVID-19 positive lymphocyte images, obtained from peripheral blood smears, will be analysed in a double-blind study using open source histopathology imaging software CellProfiler against several recognised categories, such as nucleus morphology, cytoplasm-to-nucleus ratio, etc… Each category will be stratified in order to design a grading system that could potentially be correlated with other current relevant clinical, haematological and biochemical parameters, such as: pulse, blood pressure and respiratory rate, lactate dehydrogenase (LDH) ferritin, C-reactive protein (CRP) and renal function. \n\nFurthermore, upon establishment of a robust system of graded categories, we will develop an Artificial Intelligence (AI)-based automated processing workflow utilising or adapting open-source software to facilitate an end-to-end image analysis protocol. This will result in a cost-effective, reliable methodology capable of providing a rapid diagnostic tool with high sensitivity and specificity for COVID-19 detection. \nThis tool may ultimately help in the prediction of the clinical outcomes (severity and mortality) at the time of patient presentation, which potentially may help in the management both in a hospital and outpatient settings.\n\nWe predict that this innovative approach would limit, if not eliminate, the need for complex testing based upon the use of expensive, limited-in-stock reagents, and equipment. We envisage that this workflow will be adaptable to point of care diagnostic solutions, with a great advantage for both advanced and less sophisticated health systems. \n\n\n
REC name
South West - Central Bristol Research Ethics Committee
REC reference
20/SW/0079
Date of REC Opinion
24 Apr 2020
REC opinion
Favourable Opinion