Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound.

With the 2019 coronavirus disease (COVID-19) pandemic, there is an increasing demand for remote monitoring technologies to reduce patient and provider exposure. One field that has an increasing potential is teleguided ultrasound, where telemedicine and point-of-care ultrasound (POCUS) merge to create this new scope. Teleguided POCUS can minimize staff exposure while preserving patient safety and oversight during bedside procedures.

Machine learning improves early detection of liver fibrosis by quantitative ultrasound radiomics.

Progression of liver fibrosis to cirrhosis, a severe non-reversible process, is one of the most critical risk factors in developing hepatocellular carcinoma and liver failure. Detection of liver fibrosis at an early stage is therefore essential for better patient management. Ultrasound (US) imaging can provide a noninvasive alternative to biopsies.

Contrast-enhanced ultrasound for assessing blood flow modulation of hepatocellular carcinoma by hydralazine.

Modulating aberrant tumor microvasculature provides unique opportunities for enhancing ultrasound imaging of hepatocellular carcinoma (HCC). This study aims to use contrast-enhanced ultrasound to evaluate the potential of a potent vasodilator, hydralazine, to attenuate blood flow in HCC while enhancing it in the surrounding liver tissue. The "steel effect," where blood flow is diverted from the lesion to the surrounding tissue aims to enhance lesion-tissue contrast.

Hydralazine-augmented contrast ultrasound imaging improves the detection of hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) detection with B-mode and contrast-enhanced ultrasound (CUS) imaging often varies between subjects, especially in patients with background cirrhosis. Various factors contribute to this variability, including the tumor blood flow, tumor size, internal echoes, and its location in livers with diffuse fibro-cirrhotic changes.

Objective: Towards improving lesion detection, this study evaluates a vasodilator, hydralazine, to enhance the visibility of HCC by reducing its blood flow relative to the surrounding liver tissue.

Ultrasound Radiomics for the Detection of Early-Stage Liver Fibrosis.

Objective: The study evaluates quantitative ultrasound (QUS) texture features with machine learning (ML) to enhance the sensitivity of B-mode ultrasound (US) for the detection of fibrosis at an early stage and distinguish it from advanced fibrosis. Different ML methods were evaluated to determine the best diagnostic model. Methods: 233 B-mode images of liver lobes with early and advanced-stage fibrosis induced in a rat model were analyzed.

Can Sequential Images from the Same Object Be Used for Training Machine Learning Models? A Case Study for Detecting Liver Disease by Ultrasound Radiomics.

Machine learning for medical imaging not only requires sufficient amounts of data for training and testing but also that the data be independent. It is common to see highly interdependent data whenever there are inherent correlations between observations. This is especially to be expected for sequential imaging data taken from time series.

Quantitative pleural line characterization outperforms traditional lung texture ultrasound features in detection of COVID-19.

Background And Objective: Lung ultrasound is an inherently user-dependent modality that could benefit from quantitative image analysis. In this pilot study we evaluate the use of computer-based pleural line (p-line) ultrasound features in comparison to traditional lung texture (TLT) features to test the hypothesis that p-line thickening and irregularity are highly suggestive of coronavirus disease 2019 (COVID-19) and can be used to improve the disease diagnosis on lung ultrasound.

Methods: Twenty lung ultrasound images, including normal and COVID-19 cases, were used for quantitative analysis.

Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer.

Color Doppler is used in the clinic for visually assessing the vascularity of breast masses on ultrasound, to aid in determining the likelihood of malignancy. In this study, quantitative color Doppler radiomics features were algorithmically extracted from breast sonograms for machine learning, producing a diagnostic model for breast cancer with higher performance than models based on grayscale and clinical category from the Breast Imaging Reporting and Data System for ultrasound (BI-RADS). Ultrasound images of 159 solid masses were analyzed.

Brachial flow-mediated dilation by continuous monitoring of arterial cross-section with ultrasound imaging.

Objective: Impairment of flow-mediated dilation of the brachial artery is a marker of endothelial dysfunction and often predisposes atherosclerosis and cardiovascular events. In this study, we propose a user-guided automated approach for monitoring arterial cross-section during hyperemic response to improve reproducibility and sensitivity of flow-mediated dilation.

Material And Methods: Ultrasound imaging of the brachial artery was performed in 11 volunteers in cross-sectional and in 5 volunteers in longitudinal view.

Machine learning for diagnostic ultrasound of triple-negative breast cancer.

Purpose: Early diagnosis of triple-negative (TN) breast cancer is important due to its aggressive biological characteristics, poor clinical outcomes, and limited options for therapy. The goal of this study is to evaluate the potential of machine learning with quantitative ultrasound image features for the diagnosis of TN breast cancer.

Methods: Ultrasonic and clinical data of 140 surgically confirmed breast cancer cases were analyzed retrospectively for the diagnosis of TN and non-TN (NTN) subtypes.

Machine learning to improve breast cancer diagnosis by multimodal ultrasound.

Despite major advances in breast cancer imaging there is compelling need to reduce unnecessary biopsies by improving characterization of breast lesions. This study demonstrates the use of machine learning to enhance breast cancer diagnosis with multimodal ultrasound. Surgically proven solid breast lesions were studied using quantitative features extracted from grayscale and Doppler ultrasound images.

The diagnostic performance of leak-plugging automated segmentation versus manual tracing of breast lesions on ultrasound images.

Purpose: To assess the diagnostic performance of a leak-plugging segmentation method that we have developed for delineating breast masses on ultrasound images.

Materials And Methods: Fifty-two biopsy-proven breast lesion images were analyzed by three observers using the leak-plugging and manual segmentation methods. From each segmentation method, grayscale and morphological features were extracted and classified as malignant or benign by logistic regression analysis.

Brachial artery vasomotion and transducer pressure effect on measurements by active contour segmentation on ultrasound.

Purpose: To use feed-forward active contours (snakes) to track and measure brachial artery vasomotion on ultrasound images recorded in both transverse and longitudinal views; and to compare the algorithm's performance in each view.

Methods: Longitudinal and transverse view ultrasound image sequences of 45 brachial arteries were segmented by feed-forward active contour (FFAC). The segmented regions were used to measure vasomotion artery diameter, cross-sectional area, and distention both as peak-to-peak diameter and as area.

In vivo noninvasive temperature measurement by B-mode ultrasound imaging.

Objective: This study investigated the use of ultrasound image analysis in quantifying temperature changes in tissue, both ex vivo and in vivo, undergoing local hyperthermia.

Methods: Temperature estimation is based on the thermal dependence of the acoustic speed in a heated medium. Because standard beam-forming algorithms on clinical ultrasound scanners assume a constant acoustic speed, temperature-induced changes in acoustic speed produce apparent scatterer displacements in B-mode images.

Delta-projection imaging on contrast-enhanced ultrasound to quantify tumor microvasculature and perfusion.

Rationale And Objectives: The aim of this study was to assess the Delta-projection image processing technique for visualizing tumor microvessels and for quantifying the area of tissue perfused by them on contrast-enhanced ultrasound images.

Materials And Methods: The Delta-projection algorithm was implemented to quantify perfusion by tracking the running maximum of the difference (Delta) between the contrast-enhanced ultrasound image sequence and a baseline image. Twenty-five mice with subcutaneous K1735 melanomas were first imaged with contrast-enhanced grayscale and then with minimum-exposure contrast-enhanced power Doppler (minexCPD) ultrasound.

Teaching medical students diagnostic sonography.

Objective: The purpose of this pilot project was to train medical students in sonography.

Methods: Thirty-three medical students participated in a pilot sonography course, which included exposure to ultrasound physics, knobology of a compact ultrasound scanner, training in scanning and anatomy of the aorta and right kidney, and reading assignments in these areas. Pretraining and posttraining examinations were given in these areas to analyze the degree of knowledge gained by these methods.

Computer-based margin analysis of breast sonography for differentiating malignant and benign masses.

Objective: To evaluate the role of quantitative margin features in the computer-aided diagnosis of malignant and benign solid breast masses using sonographic imaging.

Methods: Sonographic images from 56 patients with 58 biopsy-proven masses were analyzed quantitatively for the following features: margin sharpness, margin echogenicity, and angular variation in margin. Of the 58 masses, 38 were benign and 20 were malignant.

Multigated contrast-enhanced power Doppler to measure blood flow in mice tumors.

This study evaluated an image-gating method using contrast-enhanced power Doppler ultrasound (US) to estimate blood perfusion in mice tumors. A mathematical model that compensates for the effect of bubble destruction by US pulses was used to determine contrast flow through an image plane. Multigated power Doppler images were obtained following contrast injection.