ABSTRACT
The lung is one of the most affected organs by the novel virus and the imagistic exploration of the thorax such as CT scanning and X-Ray has had an important impact in assessing the severity of the disease. The study aims to investigate the presence of pulmonary lesions using Artificial Intelligence (AI) in SARS CoV2 hospitalised positive patients, based on vaccinal status from September 2021 - to January 2022. We conducted a cohort study in which we included 186 patients from which only 124 patients had their CT scans analyzed with the algorithm. In this study, we investigated the extension of lung lesions in SARS CoV2 positive patients based on their vaccinal status using an artificial intelligence algorithm. The majority of the study population was composed of males (57%), unvaccinated patients (84%) with comorbidities (79.03%). More than double unvaccinated patients compared to unvaccinated ones have been found to have more than 75% of the lungs affected. Patients in the age group 30-39 have had the most lung lesions with a mean of 69% of both lungs affected. Unvaccinated patients with comorbidities have had 5% more lung lesions than vaccinated patients with comorbidities. The interpretation time of a CT scan has been reduced by 50% using AI and in 85% of cases, the mathematical algorithm has had similar results to the ones provided by the radiologist. The study revealed a higher percentage of lung lesions among unvaccinated SARS CoV2 positive patients admitted to the hospital, underlining the importance of vaccination and also the importance of artificial intelligence in the management of a SARS CoV2 patient.
ABSTRACT
Chest computed tomography (CT) has played a valuable, distinct role in the screening, diagnosis, and follow-up of COVID-19 patients. The quantification of COVID-19 pneumonia on CT has proven to be an important predictor of the treatment course and outcome of the patient although it remains heavily reliant on the radiologist's subjective perceptions. Here, we show that with the adoption of CT for COVID-19 management, a new type of psychophysical bias has emerged in radiology. A preliminary survey of 40 radiologists and a retrospective analysis of CT data from 109 patients from two hospitals revealed that radiologists overestimated the percentage of lung involvement by 10.23 ± 4.65% and 15.8 ± 6.6%, respectively. In the subsequent randomised controlled trial, artificial intelligence (AI) decision support reduced the absolute overestimation error (P < 0.001) from 9.5% ± 6.6 (No-AI analysis arm, n = 38) to 1.0% ± 5.2 (AI analysis arm, n = 38). These results indicate a human perception bias in radiology that has clinically meaningful effects on the quantitative analysis of COVID-19 on CT. The objectivity of AI was shown to be a valuable complement in mitigating the radiologist's subjectivity, reducing the overestimation tenfold.Trial registration: https://Clinicaltrial.gov . Identifier: NCT05282056, Date of registration: 01/02/2022.