Machine learning analysis of chest CT to detect cardiac abnormalities in COVID19

Abstract

Abstract Introduction Standard, un-gated chest CT can be used as the basis of detailed segmentation of the atrial and ventricular cardiac chambers. In conditions such as COVID19 where dedicated cardiac imaging may be hazardous or unavailable atlas-based machine learning tools allow automatic quantification of cardiac morphology and may allow early detection of abnormalities. Purpose To develop an automated screening tool to detect cardiac changes associated with COVID19 on chest/lung CT to allow early treatment and appropriate selection of patients for dedicated cardiac imaging. Methods A previously validated atlas-based cardiac contouring algorithm was modified to work within the setting of variable and severe lung pathology. The modified technique was used to segment the left and right atria and ventricles from non-contrast CT scans. We applied the developed algorithm to the Moscow University COVID19 CT dataset. 1110 scans were available. COVID19 severity was graded 0 to 4. Grade 4 was not used in analysis due to insufficient numbers. Cardiac chamber sizes were compared according to COVID19 severity status. In a limited cohort of repeat studies, the feasibility of polar mapping to demonstrated serial morphological change was tested. Results A statistically significant increase of average cardiac chamber volumes was noted relative to mild Grade 0 COVID19 at every incremental severity grade (Figure 1). Changes in average ventricular volumes were greater (up to 15.2% and 16.9% for left and right ventricles) than changes in atrial volumes (up 12.1% and 7.6% for left and right atria). Automated quantification was successful in the large majority of cases and inter-patient polar mapping of sequential data to detect progressive chamber enlargement appears feasible (Figure 2). Conclusion Machine learning methods permit automatic quantification of cardiac chamber size from standard lung CT scans. Cardiac changes on lung CT examinations may be used to identify cardiac abnormalities at an early stage and could be useful to triage individuals for dedicated cardiac investigations. With further refinement, this method may be useful to detect and track temporal cardiac changes in COVID19, as well as in other pulmonary pathology and conditions in which chest CT is routinely used. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): SPHERE Research consortium Figure 1Figure 2