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Minerva Anestesiologica 2022 April;88(4):308-13

DOI: 10.23736/S0375-9393.22.16195-X

Copyright © 2022 EDIZIONI MINERVA MEDICA

language: English

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

Francesco CORRADI ^{1, 2} ✉, Luigi VETRUGNO ^{3, 4}, Alessandro ISIRDI ¹, Elena BIGNAMI ⁵, Patrizia BOCCACCI ⁶, Francesco FORFORI ¹

¹ Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy; ² Anaesthesia and Intensive Care Unit, Galliera Hospital, Genoa, Italy; ³ Department of Medical, Oral and Biotechnological Sciences, University of Chieti-Pescara, Chieti, Italy; ⁴ Department of Anesthesiology, Critical Care Medicine and Emergency, SS. Annunziata Hospital, Chieti, Italy; ⁵ Section of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy; ⁶ Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy

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.

KEY WORDS: Computer-assisted diagnosis; Artificial intelligence; Respiratory insufficiency; Differential diagnosis; COVID-19; Extravascular lung water