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Ten conditions where lung ultrasonography may fail: limits, pitfalls and lessons learned from a computer-aided algorithmic approach.
Corradi, Francesco; Vetrugno, Luigi; Isirdi, Alessandro; Bignami, Elena; Boccacci, Patrizia; Forfori, Francesco.
Afiliação
  • Corradi F; Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy - francesco.corradi@unipi.it.
  • Vetrugno L; Anaesthesia and Intensive Care Unit, Galliera Hospital, Genoa, Italy - francesco.corradi@unipi.it.
  • Isirdi A; Department of Medical, Oral and Biotechnological Sciences, University of Chieti-Pescara, Chieti, Italy.
  • Bignami E; Department of Anesthesiology, Critical Care Medicine and Emergency, SS. Annunziata Hospital, Chieti, Italy.
  • Boccacci P; Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy.
  • Forfori F; Section of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy.
Minerva Anestesiol ; 88(4): 308-313, 2022 04.
Article em En | MEDLINE | ID: mdl-35164490
Lung ultrasonography provides relevant information on morphological and functional changes occurring in the lungs. However, it correlates weakly with pulmonary congestion and extra vascular lung water. Moreover, there is lack of consensus on scoring systems and acquisition protocols. The automation of this technique may provide promising easy-to-use clinical tools to reduce inter- and intra-observer variability and to standardize scores, allowing faster data collection without increased costs and patients risks.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Computadores / Pulmão Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Computadores / Pulmão Idioma: En Ano de publicação: 2022 Tipo de documento: Article