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

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.

Ultrasound Elastography for Lung Disease Assessment.

Ultrasound elastography (US-E) is a noninvasive, safe, cost-effective and reliable technique to assess the mechanical properties of soft tissue and provide imaging biomarkers for pathological processes. Many lung diseases such as acute respiratory distress syndrome, chronic obstructive pulmonary disease, and interstitial lung disease are associated with dramatic changes in mechanical properties of lung tissues. Nevertheless, US-E is rarely used to image the lung because it is filled with air. The large difference in acoustic impedance between air and lung tissue results in the reflection of the ultrasound wave at the lung surface and, consequently, the loss of most ultrasound energy. In recent years, there has been an increasing interest in US-E applications in evaluating lung diseases. This article provides a comprehensive review of the technological advances of US-E research on lung disease diagnosis. We introduce the basic principles and major techniques of US-E and provide information on various applications in lung disease assessment. Finally, the potential applications of US-E to the diagnosis of COVID-19 pneumonia is discussed.