Deep-learning algorithm helps to standardise ATS/ERS spirometric acceptability and usability criteria.

Das, Nilakash; Verstraete, Kenneth; Stanojevic, Sanja; Topalovic, Marko; Aerts, Jean-Marie; Janssens, Wim

Das, Nilakash; Verstraete, Kenneth; Stanojevic, Sanja; Topalovic, Marko; Aerts, Jean-Marie; Janssens, Wim.

Afiliação

Das N; Laboratory of Respiratory Diseases and Thoracic Surgery, Dept of Chronic Diseases, Metabolism and Ageing, Katholieke Universiteit Leuven, Leuven, Belgium.
Verstraete K; Laboratory of Respiratory Diseases and Thoracic Surgery, Dept of Chronic Diseases, Metabolism and Ageing, Katholieke Universiteit Leuven, Leuven, Belgium.
Stanojevic S; Translational Medicine, Division of Respiratory Medicine, Hospital for Sick Children, Toronto, ON, Canada.
Topalovic M; Laboratory of Respiratory Diseases and Thoracic Surgery, Dept of Chronic Diseases, Metabolism and Ageing, Katholieke Universiteit Leuven, Leuven, Belgium.
Aerts JM; ArtiQ NV, Leuven, Belgium.
Janssens W; Division Animal and Human Health Engineering, Dept of Biosystems, Katholieke Universiteit Leuven, Leuven, Belgium.

Eur Respir J ; 56(6)2020 12.

Article em En | MEDLINE | ID: mdl-32527741

Assuntos

Aprendizado Profundo; Algoritmos; Expiração; Volume Expiratório Forçado; Humanos; Espirometria; Estados Unidos; Capacidade Vital

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans País/Região como assunto: America do norte Idioma: En Ano de publicação: 2020 Tipo de documento: Article

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