An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Disease

TL;DR

This paper presents an automated deep learning-based method for diagnosing and assessing COVID-19 severity from chest CT scans, aiding early detection and resource management.

Contribution

It introduces a novel automated approach for COVID-19 diagnosis and prognosis using deep learning on CT scans, including severity quantification and treatment assessment.

Findings

Achieved 93% classification accuracy on hold-out data.

Automated severity scoring correlates with treatment effectiveness.

Supports timely diagnosis and resource planning in hospitals.

Abstract

Since the outbreak of Coronavirus Disease 2019 (COVID-19), most of the impacted patients have been diagnosed with high fever, dry cough, and soar throat leading to severe pneumonia. Hence, to date, the diagnosis of COVID-19 from lung imaging is proved to be a major evidence for early diagnosis of the disease. Although nucleic acid detection using real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) remains a gold standard for the detection of COVID-19, the proposed approach focuses on the automated diagnosis and prognosis of the disease from a non-contrast chest computed tomography (CT)scan for timely diagnosis and triage of the patient. The prognosis covers the quantification and assessment of the disease to help hospitals with the management and planning of crucial resources, such as medical staff, ventilators and intensive care units (ICUs) capacity. The approach utilises deep learning techniques for automated quantification of the severity of COVID-19 disease via measuring the area of multiple rounded ground-glass opacities (GGO) and consolidations in the periphery (CP) of the lungs and accumulating them to form a severity score. The severity of the disease can be correlated with the medicines prescribed during the triage to assess the effectiveness of the treatment. The proposed approach shows promising results where the classification model achieved 93% accuracy on hold-out data.