TY - JOUR
AU - Grodecki, Kajetan
AU - Killekar, Aditya
AU - Simon, Judit
AU - Lin, Andrew
AU - Cadet, Sebastien
AU - Mcelhinney, Priscilla
AU - Chan, Cato
AU - Williams, Michelle C
AU - Pressman, Barry D
AU - Julien, Peter
AU - Li, Debiao
AU - Chen, Peter
AU - Gaibazzi, Nicola
AU - Thakur, Udit
AU - Mancini, Elisabetta
AU - Agalbato, Cecilia
AU - Munechika, Jiro
AU - Matsumoto, Hidenari
AU - Menè, Roberto
AU - Parati, Gianfranco
AU - Cernigliaro, Franco
AU - Nerlekar, Nitesh
AU - Torlasco, Camilla
AU - Pontone, Gianluca
AU - Maurovich-Horvat, Pal
AU - Slomka, Piotr J
AU - Dey, Damini
PY - 2023
DA - June
TI - Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems
JO - British Journal of Radiology
VL - 96
IS - 1149
DO - https://doi.org/10.1259/bjr.20220180
AB - 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.
PB - Oxford University Press
UR - https://www.research.ed.ac.uk/en/publications/61692a00-cc51-4002-ac9e-1f218e181d7a
KW - Coronavirus (COVID-19)
KW - Digital health and technology
ER