Artificial intelligence- assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems

Objective: We aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest CT scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID- 19 in comparison to semiquantitative visual scoring systems. Methods: A deep- learning algorithm was utilized to quantify the pneumonia burden, while semiquantitative pneumonia severity scores were estimated through visual means. The primary outcome was clinical deterioration, the composite end point including admission to the intensive care unit, need for invasive mechanical ventilation, or vasopressor therapy, as well as in- hospital death. Results: The final population comprised 743 patients (mean age 65 +/- 17 years, 55% men), of whom 175 (23.5%) experienced clinical deterioration or death. The area under the receiver operating characteristic curve (AUC) for predicting the primary outcome was significantly higher for AI- assisted quantitative pneumonia burden (0.739, p = 0.021) compared with the visual lobar severity score (0.711, p < 0.001) and visual segmental severity score (0.722, p = 0.042). AI- assisted pneumonia assessment exhibited lower performance when applied for calculation of the lobar severity score (AUC of 0.723, p = 0.021). Time taken for AI- assisted quantification of pneumonia burden was lower (38 +/- 10 s) compared to that of visual lobar (328 +/- 54 s, p < 0.001) and segmental (698 +/- 147 s, p < 0.001) severity scores. Conclusion: Utilizing AI- assisted quantification of pneumonia burden from chest CT scans offers a more accurate prediction of clinical deterioration in patients with COVID- 19 compared to semiquantitative severity scores, while requiring only a fraction of the analysis time.