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October 18, 2025Journal of Thoracic Imaging

Quantitative Chest Computed Tomography and Machine Learning for Subphenotyping Small Airways Disease in Long COVID

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Authors

RCRodrigo Caruso ChateCCCarlos Roberto Ribeiro de CarvalhoMSMárcio Valente Yamada Sawamura

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Overview

Cross-sectional study identifies imaging phenotypes in long COVID patients, suggesting significant pulmonary implications.

Key Points

  • Integration of quantitative chest CT and machine learning revealed distinct phenotypes in long COVID patients, including small airway disease.
  • Among the 257 evaluated patients, a dedicated SAD cluster exhibited functional impairments, particularly in pulmonary function tests.
  • This study utilized hierarchical clustering of imaging data to differentiate between four distinct pulmonary phenotypes in survivors.
  • The findings underscore the significance of integrating quantitative imaging approaches to better understand long COVID complications.

Cite This Study

Chate et al. (2025) studied this question.

synapsesocial.com/papers/68f3d0c11cb4135751d12c8fhttps://doi.org/10.1097/rti.0000000000000861
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