Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.creatorAlves, Allan Felipe Fattori-
Autor(es): dc.creatorMiranda, José Ricardo Arruda-
Autor(es): dc.creatorReis, Fabiano-
Autor(es): dc.creatorOliveira, Abner Alves-
Autor(es): dc.creatorSouza, Sérgio Augusto Santana-
Autor(es): dc.creatorFortaleza, Carlos Magno Castelo Branco-
Autor(es): dc.creatorTanni, Suzana Erico-
Autor(es): dc.creatorCastro, José Thiago Souza-
Autor(es): dc.creatorPina, Diana Rodrigues-
Data de aceite: dc.date.accessioned2025-08-21T22:48:43Z-
Data de disponibilização: dc.date.available2025-08-21T22:48:43Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2021-06-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1371/journal.pone.0251783-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/228960-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/228960-
Descrição: dc.descriptionIn this work, we aimed to develop an automatic algorithm for the quantification of total volume and lung impairments in four different diseases. The quantification was completely automatic based upon high resolution computed tomography exams. The algorithm was capable of measuring volume and differentiating pulmonary involvement including inflammatory process and fibrosis, emphysema, and ground-glass opacities. The algorithm classifies the percentage of each pulmonary involvement when compared to the entire lung volume. Our algorithm was applied to four different patients groups: no lung disease patients, patients diagnosed with SARS-CoV-2, patients with chronic obstructive pulmonary disease, and patients with paracoccidioidomycosis. The quantification results were compared with a semi-automatic algorithm previously validated. Results confirmed that the automatic approach has a good agreement with the semi-automatic. Bland-Altman (B&A) demonstrated a low dispersion when comparing total lung volume, and also when comparing each lung impairment individually. Linear regression adjustment achieved an R value of 0.81 when comparing total lung volume between both methods. Our approach provides a reliable quantification process for physicians, thus impairments measurements contributes to support prognostic decisions in important lung diseases including the infection of SARS-CoV-2.-
Descrição: dc.descriptionBotucatu Medical School Clinics Hospital Medical Physics and Radioprotection Nucleus-
Descrição: dc.descriptionInstitute of Bioscience Sao Paulo State University Julio de Mesquita Filho-
Descrição: dc.descriptionRadiology and Medical Imaging State University of Campinas-
Descrição: dc.descriptionMedical School Sao Paulo State University Julio de Mesquita Filho-
Descrição: dc.descriptionBotucatu Medical School Clinics Hospital Medical Physics and Radioprotection Nucleus-
Descrição: dc.descriptionInstitute of Bioscience Sao Paulo State University Julio de Mesquita Filho-
Descrição: dc.descriptionMedical School Sao Paulo State University Julio de Mesquita Filho-
Idioma: dc.languageen-
Relação: dc.relationPLoS ONE-
???dc.source???: dc.sourceScopus-
Título: dc.titleAutomatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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