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Leveraging AI To Predict Atrial Fibrillation

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Axel Loewe PhD and colleagues at the Institute of Biomedical Engineering at Karlsruhe Institute of Technology in Germany are developing new ways to predict cardiovascular diseases earlier and more accurately. Dr. Loewe leads an interdisciplinary team that is developing computer models of the human heart using software engineering, algorithmics, numerics, signal processing, data analysis, and machine learning. The group applies the models in simulation studies and brings them into clinical application by creating individualized digital twins of patients. Researchers use digital twins to optimize diagnostic approaches and personalize therapies. They use AI methods based on simulated data and clinical information to help decipher disease mechanisms.


V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentation

arXiv.org Machine Learning

Atrial Fibrillation (AF) is a common electro-physiological cardiac disorder that causes changes in the anatomy of the atria. A better characterization of these changes is desirable for the definition of clinical biomarkers, and thus there is a need of its fully automatic segmentation from clinical images. In this work we present an architecture based in 3D-convolution kernels, a Volumetric Fully Convolution Neural Network (V-FCNN), able to segment the entire volume in one-shot, and consequently integrate the implicit spatial redundancy present in high resolution images. A loss function based on the mixture of both Mean Square Error (MSE) and Dice Loss (DL) is used, in an attempt to combine the ability to capture the bulk shape and the reduction of local errors products by over segmentation. Results demonstrate a reasonable performance in the middle region of the atria, and the impact of the challenges of capturing the variability of the pulmonary veins or the identification of the valve plane that separates the atria to the ventricle.


Volta Medical VX1 AI Software to be Featured at Heart Rhythm 2022

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MARSEILLE, France and PROVIDENCE, R.I., April 27, 2022 (GLOBE NEWSWIRE) -- Volta Medical, a pioneering medtech startup advancing novel artificial intelligence (AI) algorithms to treat cardiac arrhythmias, today announced it will participate at Heart Rhythm 2022, where Volta VX1 digital AI companion technology will be featured in several venues, including a poster session, podium presentation, Rhythm Theater program and the Volta exhibit booth. VX1 is a machine and deep learning-based algorithm designed to assist operators in the real-time manual annotation of 3D anatomical and electrical maps of the human atria during atrial fibrillation (AF) or atrial tachycardia. It is the first FDA cleared AI-based tool in interventional cardiac electrophysiology (EP). On Friday, April 29, VX1 will be highlighted in two scientific sessions: session DH-202, "Machine Learning Applications for Arrhythmia Detection and Treatment" from 10:30-11:30 a.m. Volta's Rhythm Theater presentation, "Can AI Solve the Persistent AF Paradigm?," will be held Saturday, April 30 from 10:00-11:00 a.m.


Scientists reveal stunning video of beating heart tissue made from HUMAN stem cells

Daily Mail - Science & tech

A piece of heart tissue that beats like a real organ has been created using human stem cells. Video footage shows the cardiac material twitching regularly and researchers say it is similar in structure to the upper chambers of the heart, known as the atria. They claim the lab-grown heart tissue functions and responds to drugs like a real heart and it could be used to treat atrial fibrillation - the most common type of arrhythmia. More than 33 million people worldwide suffer from the condition which can cause blood clots and heart failure. 'This is the first time that human atrial heart tissue has been generated in vitro from a principally unlimited source of hiPSCs,' says first author Marta Lemme, a PhD student at the University Medical Center Hamburg-Eppendorf.


Artificial intelligence in the diagnosis and management of arrhythmias

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The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intelligence advances coupled with simultaneous rapid growth in computational power, sensor technology, and availability of web-based platforms have seen the rapid growth of AI-aided applications and big data research. Changing lifestyles with an expansion of the concept of internet of things and advancements in telecommunication technology have opened doors to population-based detection of atrial fibrillation in ways, which were previously unimaginable. Artificial intelligence-aided advances in 3D cardiac imaging heralded the concept of virtual hearts and the simulation of cardiac arrhythmias. Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP. In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care. As artificial intelligence (AI) has entered the medical field in recent years, machine learning (ML) approaches have made progress in assisting healthcare professionals in optimizing personalized treatment in a given situation, in particular in electrocardiography and image interpretation. Artificial intelligence methodologies are increasingly being adopted into all aspects of patient care and are paving the way to minimally invasive or non-invasive treatment modalities. This article offers a state-of-the-art overview on milestones achieved, but also on future integration of this information into diagnostic and therapeutic measures, and its likely impact on all aspects of arrhythmia care.