tetano
Editor, Senior Moderator
Sci Rep
. 2022 Feb 2;12(1):1716.
doi: 10.1038/s41598-022-05532-0.
Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes
Anthony Ortiz[SUP] #[/SUP][SUP] 1 [/SUP], Anusua Trivedi[SUP] #[/SUP][SUP] 1 [/SUP], Jocelyn Desbiens[SUP] #[/SUP][SUP] 2 [/SUP], Marian Blazes[SUP] 3 [/SUP], Caleb Robinson[SUP] 1 [/SUP], Sunil Gupta[SUP] 2 [/SUP], Rahul Dodhia[SUP] 1 [/SUP], Pavan K Bhatraju[SUP] 4 [/SUP], W Conrad Liles[SUP] 4 [/SUP], Aaron Lee[SUP] 5 [/SUP], Juan M Lavista Ferres[SUP] 6 [/SUP]
Affiliations
Abstract
The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients' clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients.
. 2022 Feb 2;12(1):1716.
doi: 10.1038/s41598-022-05532-0.
Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes
Anthony Ortiz[SUP] #[/SUP][SUP] 1 [/SUP], Anusua Trivedi[SUP] #[/SUP][SUP] 1 [/SUP], Jocelyn Desbiens[SUP] #[/SUP][SUP] 2 [/SUP], Marian Blazes[SUP] 3 [/SUP], Caleb Robinson[SUP] 1 [/SUP], Sunil Gupta[SUP] 2 [/SUP], Rahul Dodhia[SUP] 1 [/SUP], Pavan K Bhatraju[SUP] 4 [/SUP], W Conrad Liles[SUP] 4 [/SUP], Aaron Lee[SUP] 5 [/SUP], Juan M Lavista Ferres[SUP] 6 [/SUP]
Affiliations
- PMID: 35110593
- DOI: 10.1038/s41598-022-05532-0
Abstract
The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients' clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients.