Comparison Of Artificial Intelligence Enabled Methods In The Computed Tomographic Assessment Of COVID-19 Disease.

ObjectivesComparison of three different Artificial intelligence (AI) methods of assessment for patients undergoing Computed tomography (CT) for suspected Covid-19 disease. Parameters studied were probability of diagnosis, quantification of disease severity and the time to reach the diagnosis. Methods107 consecutive patients of suspected Covid-19 patients were evaluated using the three AI methods labeled as Al-I,II, III alongwith visual analysis labeled as VT for predicting probability of Covid-19, determining CT severity score (CTSS) and index (CTSI), percentage opacification (PO) and high opacification (POHO). Sensitivity, specificity along with area under curves were estimated for each method and the CTSS and CTSI correlated using Friedman test. ResultsOut of 107 patients 71 patients were Covid-19 positive and 20 negative by RT-PCR while 16 did not get RT-PCR done. Al-III method showed higher sensitivity and specificity of 93% and 88% respectively to predict probability of Covid 19. It had 2 false positive patients of interstitial lung disease. Al-II method had sensitivity and specificity of 66% and 83% respectively while visual (VT) analysis showed sensitivity and specificity of 59.7% and 62% respectively. Statistically significant differences were also seen in CTSI and PO estimation between Al-I and III methods (p< 0.0001) with Al-III showing fastest time to calculate results. ConclusionsAl-III method gave better results to make an accurate and quick diagnosis of the Covid-19 with AUC of 0.85 to predict probability of Covid-19 alongwith quantification of Covid-19 lesions in the form of PO, POHO as compared to other AI methods and also by visual analysis. KEY POINTSCT examinations of the chest can be more accurate and informative in detecting Covid-19 if combined with AI methods which are being designed to achieve this objective. In this study we compared three AI methods with Visual analysis and the results show. O_LIAl-III method had a higher sensitivity and specificity of 93% and 88% compared to other methods in predicting probability of Covid-19. C_LIO_LISignificant inter method variations were seen in quantifying Covid-19 opacities as CTSS,CTSI, PO and POHO variables (p< 0.0001). Al-III method showed no statistical difference with VT method for PO variable (p = 0.24) and was the only method which depicted all the variables.. C_LIO_LITime to processing results was the shortest with Al-III method. C_LI