Rapid screening for COVID-19 by applying artificial intelligence to chest computed tomography images: A feasibility study

• Published in: Medicine access @ point of care Vol. 5; p. 23992026211013644
• Main Authors: Galván, Pedro, Fusillo, José, González, Felipe, Vukujevic, Oraldo, Recalde, Luciano, Rivas, Ronald, Ortellado, José, Portillo, Juan, Mazzoleni, Julio, Hilario, Enrique
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-01-2021; Sage Publications Ltd; SAGE Publishing
• Subjects: Artificial intelligence; Coronaviruses; COVID-19; Disease transmission; Feasibility studies; Pleural effusion; Pneumonia; Research @ Point of Care; Telemedicine; Tomography; Paraguay; telemedicine; rapid screening; telediagnosis; COVID diagnosis; telehealth; Paraguay; ICTs in health; artificial intelligence
• ISSN: 2399-2026; 2399-2026
• DOI: 10.1177/23992026211013644

Aim:
The aim of the study was to present the results and impact of the application of artificial intelligence (AI) in the rapid diagnosis of COVID-19 by telemedicine in public health in Paraguay.
Methods:
This is a descriptive, multi-centered, observational design feasibility study based on an AI tool for the rapid detection of COVID-19 in chest computed tomography (CT) images of patients with respiratory difficulties attending the country’s public hospitals. The patients’ digital CT images were transmitted to the AI diagnostic platform, and after a few minutes, radiologists and pneumologists specialized in COVID-19 downloaded the images for evaluation, confirmation of diagnosis, and comparison with the genetic diagnosis (reverse transcription polymerase chain reaction (RT-PCR)). It was also determined the percentage of agreement between two similar AI systems applied in parallel to study the viability of using it as an alternative method of screening patients with COVID-19 through telemedicine.
Results:
Between March and August 2020, 911 rapid diagnostic tests were carried out on patients with respiratory disorders to rule out COVID-19 in 14 hospitals nationwide. The average age of patients was 50.7 years, 62.6% were male and 37.4% female. Most of the diagnosed respiratory conditions corresponded to the age group of 27–59 years (252 studies), the second most frequent corresponded to the group over 60 years, and the third to the group of 19–26 years. The most frequent findings of the radiologists/pneumologists were severe pneumonia, bilateral pneumonia with pleural effusion, bilateral pulmonary emphysema, diffuse ground glass opacity, hemidiaphragmatic paresis, calcified granuloma in the lower right lobe, bilateral pleural effusion, sequelae of tuberculosis, bilateral emphysema, and fibrotic changes, among others. Overall, an average of 86% agreement and 14% diagnostic discordance was determined between the two AI systems. The sensitivity of the AI system was 93% and the specificity 80% compared with RT-PCR.
Conclusion:
Paraguay has an AI-based telemedicine screening system for the rapid stratified detection of COVID-19 from chest CT images of patients with respiratory conditions. This application strengthens the integrated network of health services, rationalizing the use of specialized human resources, equipment, and inputs for laboratory diagnosis.