Publications by authors named "V Mor-Avi"
Background: The expansion of tricuspid valve (TV) interventions has underscored the need for accurate and reproducible three-dimensional (3D) transthoracic echocardiographic (TTE) tools for evaluating the tricuspid annulus and for 3D normal values of this structure. The aims of this study were to develop new semi-automated software for 3D TTE analysis of the tricuspid annulus, compare its accuracy and reproducibility against those of multiplanar reconstruction (MPR) reference, and determine normative values.
Methods: Three-dimensional TTE images of 113 patients with variable degrees of tricuspid regurgitation were analyzed using the new semiautomated software and conventional MPR methodology (as the reference standard), each by three independent readers.
Article Synopsis
- Researchers developed a deep learning (DL) model to detect regional wall motion abnormalities (RWMA) in transthoracic echocardiography, addressing issues like interobserver variability.
- The model was trained using a large dataset of echocardiography videos and showed high accuracy in identifying RWMA, scoring 0.96 on the area under the curve.
- While the DL model performed similarly to expert readers in most regions, it surpassed novice readers in RWMA detection, suggesting its potential to enhance both efficiency and educational aspects in RWMA assessment.
Article Synopsis
- * Researchers analyzed data from 544 CA patients, 200 controls with similar symptoms, and 174 healthy subjects using AI software and echocardiographic assessments.
- * Despite identifying higher impairment in LV global longitudinal strain and ASR in CA patients, the ASR was only moderately effective at distinguishing CA from controls, highlighting its limitations as a specific biomarker for CA.
Background: We aimed to assess in a prospective multicenter study the quality of echocardiographic exams performed by inexperienced users guided by a new artificial intelligence software and evaluate their suitability for diagnostic interpretation of basic cardiac pathology and quantitative analysis of cardiac chamber and function.
Methods: The software (UltraSight, Ltd) was embedded into a handheld imaging device (Lumify; Philips). Six nurses and 3 medical residents, who underwent minimal training, scanned 240 patients (61±16 years; 63% with cardiac pathology) in 10 standard views.
Machine learning techniques designed to recognize views and perform measurements are increasingly used to address the need for automation of the interpretation of echocardiographic images. The current study was designed to determine whether a recently developed and validated deep learning (DL) algorithm for automated measurements of echocardiographic parameters of left heart chamber size and function can improve the reproducibility and shorten the analysis time, compared to the conventional methodology. The DL algorithm trained to identify standard views and provide automated measurements of 20 standard parameters, was applied to images obtained in 12 randomly selected echocardiographic studies.
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