Neo-LVOT and Transcatheter Mitral Valve Replacement: Expert Recommendations

With the advent of transcatheter mitral valve replacement (TMVR), the concept of the neo-left ventricular outflow tract (LVOT) was introduced and remains an essential component of treatment planning. This paper describes the LVOT anatomy and provides a step-by-step computed tomography methodology to segment and measure the neo-LVOT while discussing the current evidence and outstanding challenges. It also discusses the technical and hemodynamic factors that play a major role in assessing the neo-LVOT. A summary of expert-based recommendations about the overall risk of LVOT obstruction in different scenarios is presented along with the currently available methods to reduce the risk of LVOT obstruction and other post-procedural complications

Diabetic Retinopathy Grading with Deep Visual Attention Network

Diabetic Retinopathy is a serious complication arising in diabetes afflicted patients. Its effective treatment depends on early detection, and the course of action varies decisively with the intensity of the affliction. Computer-aided diagnosis helps to detect not only the presence or absence of the disease but also the severity, making it easier for ophthalmologists to construct a treatment plan. Diabetic retinopathy grading is the task of classifying images of the eye's fundus of diabetic patients into 5 different grades ranging from 0-4 based on the severity of the disease. In this work, we propose a deep neural network architecture to address the grading problem. The method utilizes an additional attention layer in the neural network model to capture the spatial relationship between the region of interest in the images during the training process to better discriminate between the different severity stages of the disease. Also, we analyze the impact of different image processing techniques on the classification results. We assessed the performance of our proposed method using a dataset of eye fundus images and obtained a classification accuracy of 89.20% on average. This performance surpasses that reported for other state-of-the-art methods on the same dataset. The effectiveness of the proposed method will facilitate the procedural workflow of identifying severe cases of diabetic retinopath