@article{Grodecki_K-2023_178688,
title = {Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems},
author = {Grodecki, K. and Killekar, A. and Simon, J. and Lin, A. and Cadet, S. and Mcelhinney, P. and Chan, C. and Williams, M. and Pressman, B. and Julien, P. and Li, D. and Chen, P. and Gaibazzi, N. and Thakur, U. and Mancini, E. and Agalbato, C. and Munechika, J. and Matsumoto, H. and Menè, R. and Parati, G. and Cernigliaro, F. and Nerlekar, N. and Torlasco, C. and Pontone, G. and Maurovich-Horvat, P. and Slomka, P. and Dey, D.},
month = {jun},
year = {2023},
abstract = {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 semi-quantitative visual scoring systems. Methods: A deep-learning algorithm was utilized to quantify the pneumonia burden, while semi-quantitative 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 &lt; 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 &lt; 0.001) and segmental (698 ± 147 s, p &lt; 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 semi-quantitative severity scores, while requiring only a fraction of the analysis time. Advances in knowledge: Quantitative pneumonia burden assessed using AI demonstrated higher performance for predicting clinical deterioration compared to current semi-quantitative scoring systems. Such an AI system has the potential to be applied for image-based triage of COVID-19 patients in clinical practice.},
volume = {96},
issue = {1149},
journal = {British Journal of Radiology},
publisher = {Oxford University Press},
url = {https://doi.org/10.1259/bjr.20220180},
}
