ArticleFrontiers in physiology2022
Contrastive learning and subtyping of post-COVID-19 lung computed tomography images.
Article in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Discriminative feature learning for multiclass lung disease classification using contrastive learning.Frontiers in radiology · 2026Article
- From Stagnation to Strategy: Challenges in Advancing Long COVID Research.Journal of evaluation in clinical practice · 2025Review
- Investigating distributions of inhaled aerosols in the lungs of post-COVID-19 clusters through a unified imaging and modeling approach.European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences · 2024Article
- Is Pulmonary Involvement a Distinct Phenotype of Post-COVID-19?Biomedicines · 2023Article
- Lung pathophysiology in patients with long COVID-19: one size definitely does not fit all.ERJ open research · 2023Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
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%,
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Registered trials
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