tetano
Editor, Senior Moderator
Eur J Radiol
. 2023 Apr 7;163:110827.
doi: 10.1016/j.ejrad.2023.110827. Online ahead of print.
Added value of chest CT in a machine learning-based prediction model to rule out COVID-19 before inpatient admission: A retrospective university network study
Martin Krämer[SUP] 1 [/SUP], Maja Ingwersen[SUP] 2 [/SUP], Ulf Teichgräber[SUP] 3 [/SUP], Felix Güttler[SUP] 4 [/SUP]; RACOON consortium[SUP] 5 [/SUP]
Affiliations
Abstract
Purpose: During the coronavirus disease 2019 (COVID-19) pandemic, hospitals still face the challenge of timely identification of infected individuals before inpatient admission. An artificial intelligence approach based on an established clinical network may improve prospective pandemic preparedness.
Method: Supervised machine learning was used to construct diagnostic models to predict COVID-19. A pooled database was retrospectively generated from 4437 participant data that were collected between January 2017 and October 2020 at 12 German centers that belong to the radiological cooperative network of the COVID-19 (RACOON) consortium. A total of 692 (15.6 %) participants were COVID-19 positive according to the reference of the reverse transcription-polymerase chain reaction test. The diagnostic models included chest CT features (model R), clinical examination and laboratory test features (model CL), or all three feature categories (model RCL). Performance outcomes included accuracy, sensitivity, specificity, negative and positive predictive value, and area under the receiver operating curve (AUC).
Results: Performance of predictive models improved significantly by adding chest CT features to clinical evaluation and laboratory test features. Without (model CL) and with inclusion of chest CT (model RCL), sensitivity was 0.82 and 0.89 (p < 0.0001), specificity was 0.84 and 0.89 (p < 0.0001), negative predictive value was 0.96 and 0.97 (p < 0.0001), AUC was 0.92 and 0.95 (p < 0.0001), and proportion of false negative classifications was 2.6 % and 1.7 % (p < 0.0001), respectively.
Conclusions: Addition of chest CT features to machine learning-based predictive models improves the effectiveness in ruling out COVID-19 before inpatient admission to regular wards.
Keywords: Artificial intelligence; COVID-19; Computed tomography; Machine learning; Predictive value of tests.
. 2023 Apr 7;163:110827.
doi: 10.1016/j.ejrad.2023.110827. Online ahead of print.
Added value of chest CT in a machine learning-based prediction model to rule out COVID-19 before inpatient admission: A retrospective university network study
Martin Krämer[SUP] 1 [/SUP], Maja Ingwersen[SUP] 2 [/SUP], Ulf Teichgräber[SUP] 3 [/SUP], Felix Güttler[SUP] 4 [/SUP]; RACOON consortium[SUP] 5 [/SUP]
Affiliations
- PMID: 37087928
- DOI: 10.1016/j.ejrad.2023.110827
Abstract
Purpose: During the coronavirus disease 2019 (COVID-19) pandemic, hospitals still face the challenge of timely identification of infected individuals before inpatient admission. An artificial intelligence approach based on an established clinical network may improve prospective pandemic preparedness.
Method: Supervised machine learning was used to construct diagnostic models to predict COVID-19. A pooled database was retrospectively generated from 4437 participant data that were collected between January 2017 and October 2020 at 12 German centers that belong to the radiological cooperative network of the COVID-19 (RACOON) consortium. A total of 692 (15.6 %) participants were COVID-19 positive according to the reference of the reverse transcription-polymerase chain reaction test. The diagnostic models included chest CT features (model R), clinical examination and laboratory test features (model CL), or all three feature categories (model RCL). Performance outcomes included accuracy, sensitivity, specificity, negative and positive predictive value, and area under the receiver operating curve (AUC).
Results: Performance of predictive models improved significantly by adding chest CT features to clinical evaluation and laboratory test features. Without (model CL) and with inclusion of chest CT (model RCL), sensitivity was 0.82 and 0.89 (p < 0.0001), specificity was 0.84 and 0.89 (p < 0.0001), negative predictive value was 0.96 and 0.97 (p < 0.0001), AUC was 0.92 and 0.95 (p < 0.0001), and proportion of false negative classifications was 2.6 % and 1.7 % (p < 0.0001), respectively.
Conclusions: Addition of chest CT features to machine learning-based predictive models improves the effectiveness in ruling out COVID-19 before inpatient admission to regular wards.
Keywords: Artificial intelligence; COVID-19; Computed tomography; Machine learning; Predictive value of tests.