IMPORTANCEMachine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied.OBJECTIVES To assess the utility of machine learning systems for automatic discrimination of TTS and AMI.
DESIGN, SETTINGS, AND PARTICIPANTSThis cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry and patients with TTS obtained from 7 cardiovascular centers in the International Takotsubo Registry.
Mitral valve segmentation is a crucial first step to establish a machine learning pipeline that can support practitioners into performing the diagnosis of mitral valve diseases, surgical planning, and intraoperative procedures. To this end, we propose a totally automated and unsupervised mitral valve segmentation algorithm, based on a neural network low-dimension matrix factorization of the echocardiography video. The method is evaluated in a collection of echocardiography video of patients with a variety of mitral valve diseases and exceeds the state-of-the-art method in all the metrics considered.
Generative models for networks with communities have been studied extensively for being a fertile ground to establish information-theoretic and computational thresholds. In this paper we propose a new toy model for planted generative models called planted Random Energy Model (REM), inspired by Derrida's REM. For this model we provide the asymptotic behaviour of the probability of error for the maximum likelihood estimator and hence the exact recovery threshold. As an application, we further consider the 2 non-equally sized community Weighted Stochastic Block Model (2-WSBM) on huniform hypergraphs, that is equivalent to the P-REM on both sides of the spectrum, for high and low edge cardinality h. We provide upper and lower bounds for the exact recoverability for any h, mapping these problems to the aforementioned P-REM. To the best of our knowledge these are the first consistency results for the 2-WSBM on graphs and on hypergraphs with non-equally sized community.
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