Journal article
Contrastive learning and subtyping of post-COVID-19 lung computed tomography images
Frontiers in physiology, Vol.13, 999263
10/01/2022
DOI: 10.3389/fphys.2022.999263
PMCID: PMC9593072
PMID: 36304574
Abstract
Patients who recovered from the novel coronavirus disease 2019 (COVID-19) may experience a range of long-term symptoms. Since the lung is the most common site of the infection, pulmonary sequelae may present persistently in COVID-19 survivors. To better understand the symptoms associated with impaired lung function in patients with post-COVID-19, we aimed to build a deep learning model which conducts two tasks: to differentiate post-COVID-19 from healthy subjects and to identify post-COVID-19 subtypes, based on the latent representations of lung computed tomography (CT) scans. CT scans of 140 post-COVID-19 subjects and 105 healthy controls were analyzed. A novel contrastive learning model was developed by introducing a lung volume transform to learn latent features of disease phenotypes from CT scans at inspiration and expiration of the same subjects. The model achieved 90% accuracy for the differentiation of the post-COVID-19 subjects from the healthy controls. Two clusters (C1 and C2) with distinct characteristics were identified among the post-COVID-19 subjects. C1 exhibited increased air-trapping caused by small airways disease (4.10%, p = 0.008) and diffusing capacity for carbon monoxide %predicted (DLCO %predicted, 101.95%, p < 0.001), while C2 had decreased lung volume (4.40L, p < 0.001) and increased ground glass opacity (GGO%, 15.85%, p < 0.001). The contrastive learning model is able to capture the latent features of two post-COVID-19 subtypes characterized by air-trapping due to small airways disease and airway-associated interstitial fibrotic-like patterns, respectively. The discovery of post-COVID-19 subtypes suggests the need for different managements and treatments of long-term sequelae of patients with post-COVID-19.
Details
- Title: Subtitle
- Contrastive learning and subtyping of post-COVID-19 lung computed tomography images
- Creators
- Frank Li - University of IowaXuan Zhang - University of IowaAlejandro P. Comellas - University of IowaEric A. Hoffman - University of IowaTianbao Yang - University of IowaChing-Long Lin - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Frontiers in physiology, Vol.13, 999263
- DOI
- 10.3389/fphys.2022.999263
- PMID
- 36304574
- PMCID
- PMC9593072
- NLM abbreviation
- Front Physiol
- ISSN
- 1664-042X
- eISSN
- 1664-042X
- Publisher
- Frontiers Media S.A
- Grant note
- DOI: 10.13039/100000002, name: National Institutes of Health, award: U01-HL114494 R01-HL112986 S10-RR022421 T32-HL-144461; DOI: 10.13039/100000138, name: United States. Department of Education, award: P116S210005
- Language
- English
- Date published
- 10/01/2022
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Pulmonary, Critical Care, and Occupational Medicine; ICTS; IIHR--Hydroscience and Engineering; Computer Science; Mechanical Engineering; Internal Medicine
- Record Identifier
- 9984305989502771
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