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Contrastive learning and subtyping of post-COVID-19 lung computed tomography images 2022 Li et al

Discussion in 'Long Covid research' started by John Mac, Nov 2, 2022.

  1. John Mac

    John Mac Senior Member (Voting Rights)

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    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.

    https://www.frontiersin.org/articles/10.3389/fphys.2022.999263/full

    News article on the study:
    https://www.eurekalert.org/news-releases/969978
     

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