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

Kajetan Grodecki; Aditya Killekar; Judit Simon; Andrew Lin; Sebastien Cadet; Priscilla McElhinney; Cato Chan; Michelle C. Williams; Barry D. Pressman; Peter Julien; Debiao Li; Peter Chen; Nicola Gaibazzi; Udit Thakur; Maria Elisabetta Mancini; Cecilia Agalbato; Jiro Munechika; Hidenari Matsumoto; Roberto Menè; Gianfranco Parati; Franco Cernigliaro; Nitesh Nerlekar; Camilla Torlasco; Gianluca Pontone; Pál Maurovich‐Horvat; Piotr J. Slomka; Damini Dey

doi:10.1259/bjr.20220180

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Gianfranco Parati

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

Article 2023 en

Authors

KG
Kajetan Grodecki
AK
Aditya Killekar
JS
Judit Simon

Abstract

1 min read

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.

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

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