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AI-guided screening uses electrocardiogram data to detect a hidden risk factor for stroke


Researchers at Mayo Clinic have used artificial intelligence (AI) to evaluate patients' electrocardiograms (ECGs) in a targeted strategy to screen for atrial fibrillation, a common heart rhythm disorder. Atrial fibrillation is an irregular heartbeat that can lead to blood clots that may travel to the brain and cause a stroke, but it is largely underdiagnosed. In the digitally-enabled, decentralized study, AI identified new cases of atrial fibrillation that would not have come to clinical attention during routine care. Earlier research had already developed an AI algorithm to identify patients with a high likelihood of previously unknown atrial fibrillation. "We believe that atrial fibrillation screening has great potential, but currently the yield is too low and the cost is too high to make widespread screening a reality," says Peter Noseworthy, M.D., a cardiac electrophysiologist at Mayo Clinic and lead author of the study.

This skin-like computing chip uses AI to monitor health data


What if wearable electronics could monitor your health and detect diseases even before symptoms appear? "With this work we've bridged wearable technology with artificial intelligence and machine learning to create a powerful device which can analyze health data right on our own bodies," Wang says. The assistant professor and his team envision a future where wearable biosensors can track indicators of health, including sugar, oxygen, and metabolites in people's blood. With this purpose in mind, they have developed a chip that can collect data from multiple biosensors and draw conclusions about a person's health using machine learning. One of the biggest challenges, according to Wang, was creating a device that integrates seamlessly with the skin.

Stretchy computing device feels like skin--but analyzes health data with brain-mimicking artificial intelligence


Prof. Sihong Wang shows a single neuromorphic device with three electrodes. Researchers at the University of Chicago's Pritzker School of Molecular Engineering (PME) have developed a flexible, stretchable computing chip that processes information by mimicking the human brain. The device, described in the journal Matter, aims to change the way health data is processed. "With this work we've bridged wearable technology with artificial intelligence and machine learning to create a powerful device which can analyze health data right on our own bodies," said Sihong Wang, a materials scientist and Assistant Professor of Molecular Engineering. Today, getting an in-depth profile about your health requires a visit to a hospital or clinic. In the future, Wang said, people's health could be tracked continuously by wearable electronics that can detect disease even before symptoms appear.

Artificial intelligence in cardiovascular medicine


Artificial intelligence (AI) is a rapidly evolving transdisciplinary field employing machine learning (ML) techniques, which aim to simulate human intuition to offer cost-effective and scalable solutions to better manage CVD. ML algorithms are increasingly being developed and applied in various facets of cardiovascular medicine, including and not limited to heart failure, electrophysiology, valvular heart disease and coronary artery disease. Within heart failure, AI algorithms can augment diagnostic capabilities and clinical decision-making through automated cardiac measurements. Occult cardiac disease is increasingly being identified using ML from diagnostic data. Improved diagnostic and prognostic capabilities using ML algorithms are enhancing clinical care of patients with valvular heart disease and coronary artery disease. The growth of AI techniques is not without inherent challenges, most important of which is the need for greater external validation through multicenter, prospective clinical trials.

Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks


The study population were patients with dilated cardiomyopathy, in which an explainable pre-trained deep neural network (FactorECG) was trained for the outcome of life-threatening ventricular arrhythmias. This network encoded the median beat ECG into 21 factors to generate an ECG using only these factors, allowing to evaluate most characteristics that make up an ECG automatically, in a relatively small dataset. LVAD, left ventricular assist device.

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


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.

Leveraging AI To Predict Atrial Fibrillation


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.

AI-powered stethoscope from Eko spots heart failure in seconds


A study has shown that a'smart' stethoscope developed by US digital health company Eko can be used to screen people for heart failure in a few second during a standard physical examination. The device tested in the independent study combines a stethoscope used to listen to heart sounds with a single-lead electrocardiogram (ECG). It uses an algorithm to detects low left ventricular ejection fraction (LVEF) – reduced outflow of blood from the heart to the aorta – which is a common characteristic of heart failure. If LVEF is 40% or less a patient is considered to have heart failure with reduced ejection fraction (HFrEF), sometimes known as systolic heart failure, which is generally diagnosed in hospital. HFrEF accounts for around half of all heart failure patients, and around 80% of patients are diagnosed after an emergency hospital admission, even though 40% of patients have symptoms that should be detectable in primary care and allow earlier treatment.

How to Use Artificial Intelligence for Chronic Diseases Management


Helping patients following a stroke: In emergency rooms, when patients come in with a stroke called an intracerebral hemorrhage, they undergo a CT scan. That scan is examined by a computer trained to analyze CT data, cutting the time to diagnosis and limiting brain damage. Preventing heart problems: Applying AI to ECGs has resulted in a low-cost test that can be widely used to detect the presence of a weak heart pump, which can lead to heart failure if left untreated. Mayo Clinic is well situated to advance this use of AI because it has a database of more than 7 million ECGs. First, all identifying patient information is removed to protect privacy.

A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging Artificial Intelligence

Automatic Sleep Staging study is presently done with the help of Electroencephalogram (EEG) signals. Recently, Deep Learning (DL) based approaches have enabled significant progress in this area, allowing for near-human accuracy in automated sleep staging. However, EEG based sleep staging requires an extensive as well as an expensive clinical setup. Moreover, the requirement of an expert for setup and the added inconvenience to the subject under study renders it unfavourable in a point of care context. Electrocardiogram (ECG), an unobtrusive alternative to EEG, is more suitable, but its performance, unsurprisingly, remains sub-par compared to EEG-based sleep staging. Naturally, it would be helpful to transfer knowledge from EEG to ECG, ultimately enhancing the model's performance on ECG based inputs. Knowledge Distillation (KD) is a renowned concept in DL that looks to transfer knowledge from a better but potentially more cumbersome teacher model to a compact student model. Building on this concept, we propose a cross-modal KD framework to improve ECG-based sleep staging performance with assistance from features learned through models trained on EEG. Additionally, we also conducted multiple experiments on the individual components of the proposed model to get better insight into the distillation approach. Data of 200 subjects from the Montreal Archive of Sleep Studies (MASS) was utilized for our study. The proposed model showed a 14.3\% and 13.4\% increase in weighted-F1-score in 4-class and 3-class sleep staging, respectively. This demonstrates the viability of KD for performance improvement of single-channel ECG based sleep staging in 4-class(W-L-D-R) and 3-class(W-N-R) classification.