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Deep-learning model could predict AF after 24-hour ECG monitoring - Cardiac Rhythm News

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A study, published in the European Heart Journal–Digital Health, shows the predictive potential of a deep-learning model in identifying patients at risk of atrial fibrillation (AF) following monitoring with a 24-hour ambulatory electrocardiogram (ECG), despite no documented prior AF, according to researchers. Led by Jagmeet Singh (Harvard Medical School, Boston, USA) the study involved training Cardiologs' deep neural network to predict the near-term presence or absence of AF by only using the first 24 hours of an extended Holter recording. Results showed that the network was able to predict whether AF would occur in the near future with an area under the receiver operating curve, sensitivity, and specificity of 79.4%, 76%, and 69%, respectively, and outperformed ECG features previously shown to be predictive of AF. These results showed a ten-point improvement compared to a baseline model using age and sex, researchers suggested. The study is the first of its kind to demonstrate the capability of artificial intelligence in predicting AF in the short-term using 24-hour Holter compared to resting 12-lead ECGs, the developer of the deep-learning model, Cardiologs, said in a press release.


Cedars-Sinai Secures NIH Grant to Use AI in Measuring Cardiac Risk

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Cedars-Sinai researchers have received a federal grant to study how AI can be used to help predict heart attacks and other cardiac concerns. A team from the Los Angeles health system's Smidt Heart Institute and Division of Artificial Intelligence in Medicine is using a $7 million grant from the National Institutes of Health's National Heart, Lung and Blood Institute to set up the new program, which will use data from positron emission tomography and CT scans to analyze a patient's risk of cardiac issues. "Advanced imaging data could help predict patients' risk of serious cardiac events, but is so complex that clinicians aren't always able to use it," Piotr Slomka, PhD, director of Innovation in Imaging and professor of Cardiology and Medicine in the Division of Artificial Intelligence in Medicine at Cedars-Sinai and the lead researcher in the project, said in a press release. "This grant will allow us to create artificial intelligence tools that help physicians everywhere identify high-risk patients who would benefit from targeted therapy." According to the American Heart Association, more than 18 million people died of cardiovascular disease in 2019.


Artificial intelligence: a new clinical support tool for stress echocardiography

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The first applications of artificial intelligence in healthcare were reported over three decades ago [5]. However it is only in the last few years, as artificial intelligence has become embedded within multiple areas of life, that there has been an exponential growth of interest in whether it can assist in automated diagnosis and personalized patient management. Artificial intelligence includes computational techniques that'learn' from existing data to make future decisions. Deep learning is a method composed of many layers of highly interconnected processing elements, which are able to represent high levels of abstraction. The use of deep learning with imaging data is usually based on convolutional neural networks that mimic, to some extent, how the human ventral stream is structured [6]. These techniques facilitate rapid analysis of massive amounts of data [7].


ScImage and DiA Imaging Analysis Team Up to Infuse AI into Echocardiography Labs

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ScImage Inc., a leading provider of Enterprise Imaging solutions and DiA Imaging Analysis, a global leading provider of AI-based cardiac ultrasound software, announced a commercial partnership to combine ScImage's unique Cloud architecture with DiA's AI-based automated cardiac ultrasound solution, LVivo Seamless. The collaboration leverages each company's strengths to give echocardiography (echo) labs greater access to the latest innovations in healthcare imaging technology. ScImage's intelligent Cloud computing infrastructure together with DiA's AI-based algorithms, will now be available to more echocardiologists and other imaging specialists, enabling them to maximize workflow efficiency in the echo lab environment and improve patient care. "ScImage prides itself on delivering the most progressive, secure, True Cloud offering in healthcare today. By combining the compute power of PICOM365 with DiA's LVivo Seamless, clinicians will be able to enjoy the highest level of quantitative image analysis and longitudinal measurement accuracy," said Sai Raya, Ph.D., ScImage's Founder and CEO.


How America's 5 Top Hospitals are Using Machine Learning Today

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We decided that this topic is worth covering in depth since any changes to the healthcare system directly impact business leaders in multiple facets such as employee insurance coverage or hospital administration policies. Industry analysts estimate that the AI health market is poised to reach $6.6 billion by 2021 and by 2026 can potentially save the U.S. healthcare economy $150 billion in annual savings. However, no sources have taken a comprehensive look at machine learning applications at America's leading hospitals. This article aims to present a succinct picture of the implementation of machine learning by the five leading hospitals in the U.S. based on the 2016-2017 U.S. News and World Report Best Hospitals Honor Roll rankings. Through facts and figures we aim to provide pertinent insights for business leaders and professionals interested in how these top five US hospitals are being impacted by AI.


Scientists show how AI may spot unseen signs of heart failure: New self-learning algorithm may detect blood pumping problems by reading electrocardiograms

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"We showed that deep-learning algorithms can recognize blood pumping problems on both sides of the heart from ECG waveform data," said Benjamin S. Glicksberg, PhD, Assistant Professor of Genetics and Genomic Sciences, a member of the Hasso Plattner Institute for Digital Health at Mount Sinai, and a senior author of the study published in the Journal of the American College of Cardiology: Cardiovascular Imaging. "Ordinarily, diagnosing these type of heart conditions requires expensive and time-consuming procedures. We hope that this algorithm will enable quicker diagnosis of heart failure." The study was led by Akhil Vaid, MD, a postdoctoral scholar who works in both the Glicksberg lab and one led by Girish N. Nadkarni, MD, MPH, CPH, Associate Professor of Medicine at the Icahn School of Medicine at Mount Sinai, Chief of the Division of Data-Driven and Digital Medicine (D3M), and a senior author of the study. Affecting about 6.2 million Americans, heart failure, or congestive heart failure, occurs when the heart pumps less blood than the body normally needs.


Machine Learning with Signal Processing Techniques

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Stochastic Signal Analysis is a field of science concerned with the processing, modification and analysis of (stochastic) signals. Anyone with a background in Physics or Engineering knows to some degree about signal analysis techniques, what these technique are and how they can be used to analyze, model and classify signals. Data Scientists coming from a different fields, like Computer Science or Statistics, might not be aware of the analytical power these techniques bring with them. In this blog post, we will have a look at how we can use Stochastic Signal Analysis techniques, in combination with traditional Machine Learning Classifiers for accurate classification and modelling of time-series and signals. At the end of the blog-post you should be able understand the various signal-processing techniques which can be used to retrieve features from signals and be able to classify ECG signals (and even identify a person by their ECG signal), predict seizures from EEG signals, classify and identify targets in radar signals, identify patients with neuropathy or myopathyetc from EMG signals by using the FFT, etc etc. In this blog-post we'll discuss the following topics: You might often have come across the words time-series and signals describing datasets and it might not be clear what the exact difference between them is.


How amalgamated learning could scale medical AI

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AI shows tremendous promise in discovering new patterns buried in mountains of data. Yet, some data remains isolated across various silos for technical, ethical and commercial reasons. A promising new AI and machine learning technique called amalgamated learning might help overcome these silos to find new cures for diseases, prevent fraud and improve industrial equipment. It may also provide a way to construct digital twins from inconsistent forms of data. At the Imec Future Summits conference, Roel Wuyts detailed how amalgamated learning works and how it compares to related techniques like federated learning and homomorphic encryption in an exclusive interview with VentureBeat.


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.


Kaggle Master with Heart Attack Prediction Kaggle Project

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Kaggle Master with Heart Attack Prediction Kaggle Project - Kaggle is Machine Learning & Data Science community. Become Kaggle master with real machine learning kaggle project Preview this Course Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle is a platform where data scientists can compete in machine learning challenges. These challenges can be anything from predicting housing prices to detect Machine learning describes systems that make predictions using a model trained on real-world data. Machine learning is constantly being applied to new industries and ne Data science includes preparing, analyzing, and processing data.