학술논문
Enhancing physicians’ radiology diagnostics of COVID-19’s effects on lung health by leveraging artificial intelligence
Document Type
article
Author
Óscar Gasulla; Maria J. Ledesma-Carbayo; Luisa N. Borrell; Jordi Fortuny-Profitós; Ferran A. Mazaira-Font; Jose María Barbero Allende; David Alonso-Menchén; Josep García-Bennett; Belen Del Río-Carrrero; Hector Jofré-Grimaldo; Aleix Seguí; Jorge Monserrat; Miguel Teixidó-Román; Adrià Torrent; Miguel Ángel Ortega; Melchor Álvarez-Mon; Angel Asúnsolo
Source
Frontiers in Bioengineering and Biotechnology, Vol 11 (2023)
Subject
Language
English
ISSN
2296-4185
Abstract
Introduction: This study aimed to develop an individualized artificial intelligence model to help radiologists assess the severity of COVID-19’s effects on patients’ lung health.Methods: Data was collected from medical records of 1103 patients diagnosed with COVID-19 using RT- qPCR between March and June 2020, in Hospital Madrid-Group (HM-Group, Spain). By using Convolutional Neural Networks, we determine the effects of COVID-19 in terms of lung area, opacities, and pulmonary air density. We then combine these variables with age and sex in a regression model to assess the severity of these conditions with respect to fatality risk (death or ICU).Results: Our model can predict high effect with an AUC of 0.736. Finally, we compare the performance of the model with respect to six physicians’ diagnosis, and test for improvements on physicians’ performance when using the prediction algorithm.Discussion: We find that the algorithm outperforms physicians (39.5% less error), and thus, physicians can significantly benefit from the information provided by the algorithm by reducing error by almost 30%.