cardiology


How AI Can Predict Heart Attacks and Strokes

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Artificial intelligence is making its way into health care, and one of its first stops is making sense of all of those scans that doctors order. Already, studies have shown that AI-based tools can, in some cases, pick out abnormal growths that could be cancerous tumors better than doctors can, mainly because digesting and synthesizing huge volumes of information is what AI does best. In a study published Feb. 14 in Circulation, researchers in the U.K. and the U.S. report that an AI program can reliably predict heart attacks and strokes. Kristopher Knott, a research fellow at the British Heart Foundation, and his team conducted the largest study yet involving cardiovascular magnetic resonance imaging (CMR) and AI. CMR is a scan that measures blood flow to the heart by detecting how much of a special contrast agent heart muscle picks up; the stronger the blood flow, the less likely there will be blockages in the heart vessels.


The Prognostic Significance of Quantitative Myocardial Perfusion: An Artificial Intelligence Based Approach Using Perfusion Mapping

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Background: Myocardial perfusion reflects the macro- and microvascular coronary circulation. Recent quantitation developments using cardiovascular magnetic resonance (CMR) perfusion permit automated measurement clinically. We explored the prognostic significance of stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR, the ratio of stress to rest MBF). Methods: A two center study of patients with both suspected and known coronary artery disease referred clinically for perfusion assessment. Image analysis was performed automatically using a novel artificial intelligence approach deriving global and regional stress and rest MBF and MPR.


Cardiac MRI Plus Artificial Intelligence Improves Heart Attack, Stroke Prediction

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"Artificial intelligence is moving out of the computer labs and into the real world of healthcare, carrying out some tasks better than doctors could do …


Cardiac MRI Plus Artificial Intelligence Improves Heart Attack, Stroke Prediction

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Doctors may be able to predict the chances of death, heart attack, and stroke in patients by using cardiac magnetic resonance imaging (MRI) paired with artificial intelligence (AI), potentially making treatment recommendations that will improve patient blood flow and outcomes. The study, conducted by researchers with University College London, was published Friday in the journal Circulation. It was the largest study of its kind to date. Investigators examined and compared the AI-generated blood flow results in patients collected from cardiac MRI scans to assess their risk for an adverse cardiac episode. This analysis showed patients with limited blood flow were more likely to experience negative heart-related outcomes.


Active Learning Applied to Patient-Adaptive Heartbeat Classification

Neural Information Processing Systems

While clinicians can accurately identify different types of heartbeats in electrocardiograms (ECGs) from different patients, researchers have had limited success in applying supervised machine learning to the same task. The problem is made challenging by the variety of tasks, inter- and intra-patient differences, an often severe class imbalance, and the high cost of getting cardiologists to label data for individual patients. We address these difficulties using active learning to perform patient-adaptive and task-adaptive heartbeat classification. When tested on a benchmark database of cardiologist annotated ECG recordings, our method had considerably better performance than other recently proposed methods on the two primary classification tasks recommended by the Association for the Advancement of Medical Instrumentation. Additionally, our method required over 90% less patient-specific training data than the methods to which we compared it.


Identifying Patients at Risk of Major Adverse Cardiovascular Events Using Symbolic Mismatch

Neural Information Processing Systems

Cardiovascular disease is the leading cause of death globally, resulting in 17 million deaths each year. Despite the availability of various treatment options, existing techniques based upon conventional medical knowledge often fail to identify patients who might have benefited from more aggressive therapy. In this paper, we describe and evaluate a novel unsupervised machine learning approach for cardiac risk stratification. The key idea of our approach is to avoid specialized medical knowledge, and assess patient risk using symbolic mismatch, a new metric to assess similarity in long-term time-series activity. We hypothesize that high risk patients can be identified using symbolic mismatch, as individuals in a population with unusual long-term physiological activity.


World's first AI can predict when patients will have a heart attack or stroke better than a DOCTOR

Daily Mail - Science & tech

Artificial intelligence has accurately predicted the possibility of heart attack or stroke in a world's first. A study led by Barts Health NHS Trust and University College London used AI to analyse cardiac scans of more than 1,000 patients. Researchers said it's the first time blood flow scans, which reveal problems with the heart, have been read by a computer. The technology was more accurate at predicting major cardiovascular events within a 19-month follow-up than a doctor using traditional means. Researchers said it could be used by medical teams to recommend treatments.


Machine learning to predict venous thrombosis in acutely ill medical patients

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Patients with an acute medical illness have an increased risk of venous thromboembolism (VTE) during hospitalization that persists following discharge.1, 2 Several randomized trials have demonstrated the efficacy of VTE prophylaxis with direct oral anticoagulants (DOACs) compared to low‐molecular‐weight heparin for 6 to 14 days.3-5 Based on the results of the APEX trial, the US Food and Drug Administration has licensed betrixaban for first‐line thromboprophylaxis in acute medically ill patients at high risk for VTE. The identification of these high‐risk patients may be determined clinically or by use of risk assessment models (RAMs) that rely on integer‐based scoring systems of known risk factors.6, These RAMs demonstrated modest performance in validation data sets.11-13 Machine learning algorithms are constructed to search for patterns in data that provide maximum predictive ability.14, 15 These learning methods have demonstrated superiority to traditional diagnostic and prognostic tools in various domains.16-19


FDA OKs first-of-a-kind AI that guides cardiac imaging - MedCity News

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The FDA has cleared what it describes as the first software that uses AI to guide family doctors, registered nurses and other clinicians in taking cardiac ultrasounds. Developed by Brisbane, California-based Caption Health, the software communicates instructions via prompts on a screen-based interface. The prompts allow non-experts to capture images and videos of diagnostic quality. "This is especially important because it demonstrates the potential for artificial intelligence and machine learning technologies to increase access to safe and effective cardiac diagnostics that can be life-saving for patients," Robert Ochs, a deputy director in the FDA's Center for Devices and Radiological Health, said in a statement. The software is called Caption Guidance and was cleared for use with a diagnostic ultrasound system developed by Teratech Corp., though the software has the potential to be used with other systems, according to the FDA.


Artificial Intelligence Detects Heart Failure From One Heartbeat With 100% Accuracy

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My work has appeared on Engadget, Lifehacker, Gizmodo, TechRadar, The Next Web, Alphr, Tech City News, Computer Weekly, Mail Online and The Telegraph. I also edit Tech Dragons, a publication covering STEM in Wales. I have a particular interest in the entrepreneurs and companies using technology to drive positive change in the world, whether it be social or environmental causes. My passion is unearthing and reporting on the change makers of tomorrow. As well as being a keen advocate of tech-for-good, I also use my writing to raise awareness of mental health.