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COVID-19 is an ongoing global health pandemic. Although COVID-19 can be diagnosed with various tests such as PCR, these tests do not establish pulmonary disease burden. Whereas point-of-care lung ultrasound (POCUS) can directly assess the severity of characteristic pulmonary findings of COVID-19, the advantage of using US is that it is inexpensive, portable, and widely available for use in many clinical settings. For automated assessment of pulmonary findings, we have developed an unsupervised learning technique termed the calculated lung ultrasound (CLU) index. The CLU can quantify various types of lung findings, such as A or B lines, consolidations, and pleural effusions, and it uses these findings to calculate a CLU index score, which is a quantitative measure of pulmonary disease burden. This is accomplished using an unsupervised, patient-specific approach that does not require training on a large dataset. The CLU was tested on 52 lung ultrasound examinations from several institutions. CLU demonstrated excellent concordance with radiologist findings in different pulmonary disease states. Given the global nature of COVID-19, the CLU would be useful for sonographers and physicians in resource-strapped areas with limited ultrasound training and diagnostic capacities for more accurate assessment of pulmonary status. [ABSTRACT FROM AUTHOR]

The purpose of the work described here was to determine if the diagnostic performance of point and 2-D shear wave elastography (pSWE; 2-DSWE) using shear wave velocity (SWV) with a new machine learning (ML) technique applied to systems from different vendors is comparable to that of magnetic resonance elastography (MRE) in distinguishing non-significant (

The question of whether ultrasound point shear wave elastography can differentiate renal cell carcinoma (RCC) from angiomyolipoma (AML) is controversial. This study prospectively enrolled 51 patients with 52 renal tumors (42 RCCs, 10 AMLs). We obtained 10 measurements of shear wave velocity (SWV) in the renal tumor, cortex and medulla. Median SWV was first used to classify RCC versus AML. Next, the prediction accuracy of 4 machine learning algorithms-logistic regression, naïve Bayes, quadratic discriminant analysis and support vector machines (SVMs)-was evaluated, using statistical inputs from the tumor, cortex and combined statistical inputs from tumor, cortex and medulla. After leave-one-out cross validation, models were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). Tumor median SWV performed poorly (AUC = 0.62; p = 0.23). Except logistic regression, all machine learning algorithms reached statistical significance using combined statistical inputs (AUC = 0.78-0.98; p < 7.1 × 10-3). SVMs demonstrated 94% accuracy (AUC = 0.98; p = 3.13 × 10-6) and clearly outperformed median SWV in differentiating RCC from AML (p = 2.8 × 10-4). [ABSTRACT FROM AUTHOR]

The aim of this study was to assess whether the number of liver point shear wave elastography (pSWE) measurements could be reduced compared with the currently recommended 10 valid measurements. Three thousand four hundred one pSWE examinations in patients with liver disease were performed with 10 consecutive valid measurements in liver segment 8. Liver fibrosis grading using published cutoff values were compared retrospectively using the median of 10 versus the first 1-9 measurements with Kendall's τ coefficient. Overall and binary (clinically significant [≥F2] versus non-significant [F0/F1]) fibrosis grading highly correlated when using 5-9 versus 10 valid measurements (τ = 0.96/0.95, p < 0.001). With the use of 5 valid measurements, a change in binary grading was observed in 87 of 3401 (2.6%) exams and only when velocities measured between 1.1 and 1.5 m/s. Therefore, using 5-9 valid measurements in pSWE of the liver results in a small portion of liver fibrosis grading misclassifications compared with use of 10 measurements and could help decrease scanning time, cost and discomfort in sonographers and patients. [ABSTRACT FROM AUTHOR]