Improving ultrasound video classification: an evaluation of novel deep learning methods in echocardiography

Echocardiography is the commonest medical ultrasound examination, but automated interpretation is challenging and hinges on correct recognition of the ‘view’ (imaging plane and orientation). Current state-of-the-art methods for identifying the view computationally involve 2-dimensional convolutional neural networks (CNNs), but these merely classify individual frames of a video in isolation, and ignore information describing the movement of structures throughout the cardiac cycle. Here we explore the efficacy of novel CNN architectures, including time-distributed networks and two-stream networks, which are inspired by advances in human action recognition. We demonstrate that these new architectures more than halve the error rate of traditional CNNs from 8.1% to 3.9%. These advances in accuracy may be due to these networks’ ability to track the movement of specific structures such as heart valves throughout the cardiac cycle. Finally, we show the accuracies of these new state-of-the-art networks are approaching expert agreement (3.6% discordance), with a similar pattern of discordance between views

Prevalence, predictors, and outcomes of patient prosthesis mismatch in women undergoing TAVI for severe aortic stenosis: Insights from the WIN-TAVI registry

Objective: To evaluate the incidence, predictors and outcomes of female patients with patient-prosthesis mismatch (PPM) following transcatheter aortic valve intervention (TAVI) for severe aortic stenosis (AS). Background: Female AS TAVI recipients have a significantly lower mortality than surgical aortic valve replacement (SAVR) recipients, which could be attributed to the potentially lower PPM rates. TAVI has been associated with lower rates of PPM compared to SAVR. PPM in females post TAVI has not been investigated to date. Methods: The WIN-TAVI (Women's INternational Transcatheter Aortic Valve Implantation) registry i