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. (Cite this article as: Corradi F, Vetrugno L, Isirdi A, Bignami E, Boccacci P, Forfori F. Ten conditions where lung ultrasonography may fail: limits, pitfalls and lessons learned from a computer-aided algorithmic approach. Minerva Anestesiol 2022;88:308-13. DOI: 10.23736/S0375-9393.22.16195-X)

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. - In: MINERVA ANESTESIOLOGICA. - ISSN 0375-9393. - 88:4(2022), pp. 308-313. [10.23736/S0375-9393.22.16195-X]

Ten conditions where lung ultrasonography may fail: limits, pitfalls and lessons learned from a computer-aided algorithmic approach

Bignami, Elena;
2022

Abstract

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. (Cite this article as: Corradi F, Vetrugno L, Isirdi A, Bignami E, Boccacci P, Forfori F. Ten conditions where lung ultrasonography may fail: limits, pitfalls and lessons learned from a computer-aided algorithmic approach. Minerva Anestesiol 2022;88:308-13. DOI: 10.23736/S0375-9393.22.16195-X)
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. - In: MINERVA ANESTESIOLOGICA. - ISSN 0375-9393. - 88:4(2022), pp. 308-313. [10.23736/S0375-9393.22.16195-X]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2933708
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