Ten conditions where lung ultrasonography may fail: limits, pitfalls and lessons learned from a computer-aided algorithmic approach.
Journal
Minerva anestesiologica
ISSN: 1827-1596
Titre abrégé: Minerva Anestesiol
Pays: Italy
ID NLM: 0375272
Informations de publication
Date de publication:
04 2022
04 2022
Historique:
pubmed:
16
2
2022
medline:
14
4
2022
entrez:
15
2
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 35164490
pii: S0375-9393.22.16195-X
doi: 10.23736/S0375-9393.22.16195-X
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM