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INFORMATION FOR

Imaging

Overview

Our faculty have been funded by ACEP, SAEM, AIUM, AHRQ, and the AHA, resulting in over 100 peer-reviewed publications. Our focus is to highlight the utility of point of care ultrasound (POCUS) in the early diagnosis of aortic dissection, push the envelope on using POCUS for early diagnosis and prognosis of pulmonary embolism, and sought to reduce unnecessary CT imaging in renal colic.

Current Research and Projects

Point of Care Ultrasound Lung AI Validation Data Collection

Philips Healthcare

Investigators: Chris Moore, MD and Cristiana Baloescu, MD

This study involves the collection of lung ultrasound images for patients with lower respiratory symptoms to train and test the performance of an Artificial Intelligence (AI) algorithm to detect lung pathology.


Quantitative Assessment of B-Lines

Biomedical Advanced Research and Development Authority (BARDA)

Investigators: Chris Moore, MD and Cristiana Baloescu, MD

This study investigates how automated software calculating the severity of the B-line patterns as viewed on lung ultrasound compares to expert visual quantification of B-line severity for conditions such pulmonary edema, COVID-19, and pneumonia.


Ultrasound Lung Guidance and Interpretation

Caption Health

Investigators: Chris Moore, MD and Cristiana Baloescu, MD

This is a prospective study on emergency department patients who have potential or confirmed lung pathology to determine the feasibility and performance of an automated system to obtain and interpret lung ultrasound images, particularly by ultrasound novices.


FAST Imaging for Development of Automated Ultrasound Image Acquisition and Interpretation

Biomedical Advanced Research and Development Authority (BARDA)

Investigators: Chris Moore, MD and Cristiana Baloescu, MD

This is an observational ultrasound study designed to obtain a library of high-quality positive and negative ultrasound images acquired from the Focused Assessment with Sonography for Trauma (FAST) imaging protocol in order to train a machine learning algorithm.