COMPARISON OF ARTIFICIAL INTELLIGENCE ENABLED METHODS IN THE COMPUTED TOMOGRAPHIC ASSESSMENT OF COVID-19 DISEASE.
Objectives: Comparison 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 . Methods: 107 consecutive patients of suspected Covid-19 patients were evaluated using the three AI methods labeled as AI-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. Results: Out of 107 patients 71 patients were Covid-19 positive and 20 negative by RT-PCR while 16 did not get RT-PCR done. AI-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. AI-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 AI-I and III methods (p<0.0001) with AI-III showing fastest time to calculate results. Conclusions: AI-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.