Validation of machine learning models for estimation of left ventricular ejection fraction on point-of-care ultrasound: insights on features that impact performance.

Background: Machine learning (ML) algorithms can accurately estimate left ventricular ejection fraction (LVEF) from echocardiography, but their performance on cardiac point-of-care ultrasound (POCUS) is not well understood.

Objectives: We evaluate the performance of an ML model for estimation of LVEF on cardiac POCUS compared with Level III echocardiographers' interpretation and formal echo reported LVEF.

Methods: Clinicians at a tertiary care heart failure clinic prospectively scanned 138 participants using hand-carried devices.

Automated Atrial Fibrillation Diagnosis by Echocardiography without ECG: Accuracy and Applications of a New Deep Learning Approach.

Background: Automated rhythm detection on echocardiography through artificial intelligence (AI) has yet to be fully realized. We propose an AI model trained to identify atrial fibrillation (AF) using apical 4-chamber (AP4) cines without requiring electrocardiogram (ECG) data.

Methods: Transthoracic echocardiography studies of consecutive patients ≥ 18 years old at our tertiary care centre were retrospectively reviewed for AF and sinus rhythm.

Machine learning derived echocardiographic image quality in patients with left ventricular systolic dysfunction: insights on the echo views of greatest image quality.

We sought to determine the cardiac ultrasound view of greatest quality using a machine learning (ML) approach on a cohort of transthoracic echocardiograms (TTE) with abnormal left ventricular (LV) systolic function. We utilize an ML model to determine the TTE view of highest quality when scanned by sonographers. A random sample of TTEs with reported LV dysfunction from 09/25/2017-01/15/2019 were downloaded from the regional database.

A Novel Continuous Left Ventricular Diastolic Function Score Using Machine Learning.

Background: Unlike left ventricular (LV) ejection fraction, which provides a precise, reliable, and prognostically valuable measure of systolic function, there is no single analogous measure of LV diastolic function.

Objectives: We aimed to develop a continuous score to grade LV diastolic function using machine learning modeling of echocardiographic data.

Methods: Consecutive echo studies performed at a tertiary-care center between February 1, 2010, and March 31, 2016, were assessed, excluding studies containing features that would interfere with diastolic function assessment as well as studies in which 1 or more parameters within the contemporary diastolic function assessment algorithm were not reported.

Impact of the updated diastolic function guidelines in the real world.

Background: Echocardiographic assessment of diastolic function is complex but can aid in the diagnosis of heart failure, particularly in patients with preserved ejection fraction. In 2016, the American Society of Echocardiography (ASE) and European Association of Cardiovascular Imaging (EACVI) published an updated algorithm for the evaluation of diastolic function. The objective of our study was to assess its impact on diastolic function assessment in a real-world cohort of echo studies.

Cardiac Phase Detection in Echocardiograms With Densely Gated Recurrent Neural Networks and Global Extrema Loss.

Accurate detection of end-systolic (ES) and end-diastolic (ED) frames in an echocardiographic cine series can be difficult but necessary pre-processing step for the development of automatic systems to measure cardiac parameters. The detection task is challenging due to variations in cardiac anatomy and heart rate often associated with pathological conditions. We formulate this problem as a regression problem and propose several deep learning-based architectures that minimize a novel global extrema structured loss function to localize the ED and ES frames.

Comparison of Patient Position and Midline Lumbar Neuraxial Access Via Statistical Model Registration to Ultrasound.

Patient positioning and needle puncture site are important for lumbar neuraxial anesthesia. We sought to identify optimal patient positioning and puncture sites with a novel ultrasound registration. We registered a statistical model to volumetric ultrasound data acquired from volunteers (n = 10) in three positions: (i) prone; (ii) seated with thoracic and lumbar flexion; and (iii) seated as in position ii, with a 10° dorsal tilt.

Model-based registration of preprocedure MR and intraprocedure US of the lumbar spine.

Purpose: Epidural and spinal needle insertions, as well as facet joint denervation and injections are widely performed procedures on the lumbar spine for delivering anesthesia and analgesia. Ultrasound (US)-based approaches have gained popularity for accurate needle placement, as they use a non-ionizing, inexpensive and accessible modality for guiding these procedures. However, due to the inherent difficulties in interpreting spinal US, they yet to become the clinical standard-of-care.

Joint registration of ultrasound, CT and a shape+pose statistical model of the lumbar spine for guiding anesthesia.

Purpose: Facet joint injections and epidural needle insertions are widely used for spine anesthesia. Accurate needle placement is important for effective therapy delivery and avoiding complications arising from damage of soft tissue and nerves. Needle guidance is usually performed by fluoroscopy or palpation, resulting in radiation exposure and multiple needle re-insertions.