Dataset Information

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality.

ABSTRACT:

Rationale and objectives

The burden of coronavirus disease 2019 (COVID-19) airspace opacities is time consuming and challenging to quantify on computed tomography. The purpose of this study was to evaluate the ability of a deep convolutional neural network (dCNN) to predict inpatient outcomes associated with COVID-19 pneumonia.

Materials and methods

A previously trained dCNN was tested on an external validation cohort of 241 patients who presented to the emergency department and received a chest computed tomography scan, 93 with COVID-19 and 168 without. Airspace opacity scoring systems were defined by the extent of airspace opacity in each lobe, totaled across the entire lungs. Expert and dCNN scores were concurrently evaluated for interobserver agreement, while both dCNN identified airspace opacity scoring and raw opacity values were used in the prediction of COVID-19 diagnosis and inpatient outcomes.

Results

Interobserver agreement for airspace opacity scoring was 0.892 (95% CI 0.834-0.930). Probability of each outcome behaved as a logistic function of the opacity scoring (25% intensive care unit admission at score of 13/25, 25% intubation at 17/25, and 25% mortality at 20/25). Length of hospitalization, intensive care unit stay, and intubation were associated with larger airspace opacity score (p = 0.032, 0.039, 0.036, respectively).

Conclusion

The tested dCNN was highly predictive of inpatient outcomes, performs at a near expert level, and provides added value for clinicians in terms of prognostication and disease severity.

SUBMITTER: Chamberlin JH

PROVIDER: S-EPMC8977389 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality.

Chamberlin Jordan H JH Aquino Gilberto G Schoepf Uwe Joseph UJ Nance Sophia S Godoy Franco F Carson Landin L Giovagnoli Vincent M VM Gill Callum E CE McGill Liam J LJ O'Doherty Jim J Emrich Tilman T Burt Jeremy R JR Baruah Dhiraj D Varga-Szemes Akos A Kabakus Ismail M IM

Academic radiology 20220404 8

<h4>Rationale and objectives</h4>The burden of coronavirus disease 2019 (COVID-19) airspace opacities is time consuming and challenging to quantify on computed tomography. The purpose of this study was to evaluate the ability of a deep convolutional neural network (dCNN) to predict inpatient outcomes associated with COVID-19 pneumonia.<h4>Materials and methods</h4>A previously trained dCNN was tested on an external validation cohort of 241 patients who presented to the emergency department and r ...[more]

PMID: 35610114

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Dataset Information

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality.

Rationale and objectives

Materials and methods

Results

Conclusion

Publications

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality.

Similar Datasets

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