Lung Ultrasound Segmentation and Adaptation Between COVID-19 and Community-Acquired Pneumonia

Mason, Harry; Cristoni, Lorenzo; Walden, Andrew; Lazzari, Roberto; Pulimood, Thomas; Grandjean, Louis; Wheeler-Kingshott, C; Hu, Yipeng; Baum, Zachary M. C.

doi:10.1007/978-3-030-87583-1_5

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…One encouraging point about these LUS artifacts, such as B-lines, is that though they are related to pathology in various lung diseases, deep learning techniques could identify COVID-19 from other types of pneumonia [ 96 ]. Over a combined B-lines dataset consisting of 612 LUS videos from 243 patients with COVID-19, non-COVID acute respiratory distress syndrome, and hydrostatic pulmonary edema, the trained CNNs (Xception architecture) classifiers showed AUCs of 1.0, 0.934, and 1.0, respectively, much better than physician differentiation ability between these lung diseases [ 97 ].…”

Section: Machine Learning In Covid-19 Lusmentioning

confidence: 99%

Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic

et al. 2022

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Ultrasound imaging of the lung has played an important role in managing patients with COVID-19–associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its unique imaging capability and logistical advantages over chest X-ray and CT. Pneumonia/ARDS is associated with the sonographic appearances of pleural line irregularities and B-line artefacts, which are caused by interstitial thickening and inflammation, and increase in number with severity. Artificial intelligence (AI), particularly machine learning, is increasingly used as a critical tool that assists clinicians in LUS image reading and COVID-19 decision making. We conducted a systematic review from academic databases (PubMed and Google Scholar) and preprints on arXiv or TechRxiv of the state-of-the-art machine learning technologies for LUS images in COVID-19 diagnosis. Openly accessible LUS datasets are listed. Various machine learning architectures have been employed to evaluate LUS and showed high performance. This paper will summarize the current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques.

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Section: Machine Learning In Covid-19 Lusmentioning

confidence: 99%