Patients suspected of COVID-19 infection are tested using the 'gold standard' RT-PCR assay obtained via a nasal/throat swab. However there can be significant time delays in processing such samples. More seriously, such swab tests can misdiagnose patients, giving a supposedly clean bill of health to patients who actually do have COVID-19 disease - the so called 'false negative' problem.
This can be extremely serious for vulnerable patients with underlying medical conditions and for whom the earliest possible intervention is critical.
CT scans offer a quick (< 1hr) and efficient alternative means of diagnosing COVID-19, the radiographic data providing radiologists with evidence of disease etiology.
COVID-19 lesions in the lungs can be difficult to discern from community acquired pneumonias, other pulmonary disorders and against the diverse background of 'normal' lung conditions - particularly in the earliest stages of infection.
"This is where Artificial Intelligence (AI) could play a critical role in supporting radiology analysis, by training deep learning algorithms on a large dataset of CT scans of known status, and ensuring that all of the images to be used have been standardised across the various imaging protocols used at different CT scan facilities," says Dr Aaron Golden, School of Mathematics, Statistics and Applied Mathematics at NUI Galway.
About the project
Dr Golden aims to build an AI imaging system to support radiology teams in expedited diagnosis of early stage COVID-19 disease using CT scans.
The project will use cutting-edge AI infrastructure to standardise thousands of publicly available chest CT scans.
By using a type of machine learning algorithm called Generative Adversarial Networks, the team aim to correct for variations in imaging protocols between differing radiology facilities.
This will allow for the re-assessment of the performance of two recently published open source convolutional neural network based classifiers developed by medical researchers in China that have been previously shown to demonstrate high sensitivity/specificity in diagnosing COVID-19 in CT scans in selected patient groups with a high incidence of this disease.
The project is funded by Ireland's national COVID-19 Rapid Response Research and Innovation funding, via the Health Research Board and Irish Research Council. #CovidResearchIreland
About the PI
Dr Aaron Golden is a Lecturer in the School of Mathematics, Statistics and Applied Mathematics, a member of the School's Bioinformatics and Biostatistics Research Cluster and a PI in the Ryan Institute.
Dr. Golden is a physicist by training with 24 years experience working as a data scientist. He has successfully lead diverse teams working on projects covering topics in the earth observation, astronomical and biomedical sciences, with funding support from Science Foundation Ireland, Enterprise Ireland & the Environmental Protection Agency. Between 2011-2016 he benefited from extensive clinical research experience whilst an Associate Professor of Genetics (Division of Computational Genomics) at the Albert Einstein College of Medicine in New York City.