tetano
Editor, Senior Moderator
Br J Radiol
. 2022 Jan 1;95(1129):20210759.
doi: 10.1259/bjr.20210759.
The diagnostic performance of deep-learning-based CT severity score to identify COVID-19 pneumonia
Anna Sára Kardos[SUP] 1 2 [/SUP], Judit Simon[SUP] 1 2 [/SUP], Chiara Nardocci[SUP] 1 [/SUP], István Viktor Szabó[SUP] 1 [/SUP], Norbert Nagy[SUP] 1 [/SUP], Renad Heyam Abdelrahman[SUP] 1 [/SUP], Emese Zsarnóczay[SUP] 1 2 [/SUP], Bence Fejér[SUP] 1 [/SUP], Balázs Futácsi[SUP] 1 [/SUP], Veronika Müller[SUP] 3 [/SUP], Béla Merkely[SUP] 2 [/SUP], Pál Maurovich-Horvat[SUP] 1 2 [/SUP]
Affiliations
Abstract
Objective: To determine the diagnostic accuracy of a deep-learning (DL)-based algorithm using chest computed tomography (CT) scans for the rapid diagnosis of coronavirus disease 2019 (COVID-19), as compared to the reference standard reverse-transcription polymerase chain reaction (RT-PCR) test.
Methods: In this retrospective analysis, data of COVID-19 suspected patients who underwent RT-PCR and chest CT examination for the diagnosis of COVID-19 were assessed. By quantifying the affected area of the lung parenchyma, severity score was evaluated for each lobe of the lung with the DL-based algorithm. The diagnosis was based on the total lung severity score ranging from 0 to 25. The data were randomly split into a 40% training set and a 60% test set. Optimal cut-off value was determined using Youden-index method on the training cohort.
Results: A total of 1259 patients were enrolled in this study. The prevalence of RT-PCR positivity in the overall investigated period was 51.5%. As compared to RT-PCR, sensitivity, specificity, positive predictive value, negative predictive value and accuracy on the test cohort were 39.0%, 80.2%, 68.0%, 55.0% and 58.9%, respectively. Regarding the whole data set, when adding those with positive RT-PCR test at any time during hospital stay or "COVID-19 without virus detection", as final diagnosis to the true positive cases, specificity increased from 80.3% to 88.1% and the positive predictive value increased from 68.4% to 81.7%.
Conclusion: DL-based CT severity score was found to have a good specificity and positive predictive value, as compared to RT-PCR. This standardized scoring system can aid rapid diagnosis and clinical decision making.
Advances in knowledge: DL-based CT severity score can detect COVID-19-related lung alterations even at early stages, when RT-PCR is not yet positive.
. 2022 Jan 1;95(1129):20210759.
doi: 10.1259/bjr.20210759.
The diagnostic performance of deep-learning-based CT severity score to identify COVID-19 pneumonia
Anna Sára Kardos[SUP] 1 2 [/SUP], Judit Simon[SUP] 1 2 [/SUP], Chiara Nardocci[SUP] 1 [/SUP], István Viktor Szabó[SUP] 1 [/SUP], Norbert Nagy[SUP] 1 [/SUP], Renad Heyam Abdelrahman[SUP] 1 [/SUP], Emese Zsarnóczay[SUP] 1 2 [/SUP], Bence Fejér[SUP] 1 [/SUP], Balázs Futácsi[SUP] 1 [/SUP], Veronika Müller[SUP] 3 [/SUP], Béla Merkely[SUP] 2 [/SUP], Pál Maurovich-Horvat[SUP] 1 2 [/SUP]
Affiliations
- PMID: 34889645
- DOI: 10.1259/bjr.20210759
Abstract
Objective: To determine the diagnostic accuracy of a deep-learning (DL)-based algorithm using chest computed tomography (CT) scans for the rapid diagnosis of coronavirus disease 2019 (COVID-19), as compared to the reference standard reverse-transcription polymerase chain reaction (RT-PCR) test.
Methods: In this retrospective analysis, data of COVID-19 suspected patients who underwent RT-PCR and chest CT examination for the diagnosis of COVID-19 were assessed. By quantifying the affected area of the lung parenchyma, severity score was evaluated for each lobe of the lung with the DL-based algorithm. The diagnosis was based on the total lung severity score ranging from 0 to 25. The data were randomly split into a 40% training set and a 60% test set. Optimal cut-off value was determined using Youden-index method on the training cohort.
Results: A total of 1259 patients were enrolled in this study. The prevalence of RT-PCR positivity in the overall investigated period was 51.5%. As compared to RT-PCR, sensitivity, specificity, positive predictive value, negative predictive value and accuracy on the test cohort were 39.0%, 80.2%, 68.0%, 55.0% and 58.9%, respectively. Regarding the whole data set, when adding those with positive RT-PCR test at any time during hospital stay or "COVID-19 without virus detection", as final diagnosis to the true positive cases, specificity increased from 80.3% to 88.1% and the positive predictive value increased from 68.4% to 81.7%.
Conclusion: DL-based CT severity score was found to have a good specificity and positive predictive value, as compared to RT-PCR. This standardized scoring system can aid rapid diagnosis and clinical decision making.
Advances in knowledge: DL-based CT severity score can detect COVID-19-related lung alterations even at early stages, when RT-PCR is not yet positive.