vascular disease


Could doctors use machine learning to detect heart attacks faster?

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But Dr Louise Cullen, an emergency physician at the Royal Brisbane and Women's Hospital and one of the study's authors, said there were arbitrary cut-offs for troponin levels considered to be an indicator of a heart attack. "We see people come to hospital with heart damage and high levels of troponin, some of them are having a heart attack and some have other causes," Dr Cullen said. "There's an arbitrary cut-off point for indicating a heart attack based on a so-called normal population. "The problem is we know the older you get and whether you're male or female makes a difference on what that value should be.


Could doctors use machine learning to detect heart attacks faster?

#artificialintelligence

But Dr Louise Cullen, an emergency physician at the Royal Brisbane and Women's Hospital and one of the study's authors, said there were arbitrary cut-offs for troponin levels considered to be an indicator of a heart attack. "We see people come to hospital with heart damage and high levels of troponin, some of them are having a heart attack and some have other causes," Dr Cullen said. "There's an arbitrary cut-off point for indicating a heart attack based on a so-called normal population. "The problem is we know the older you get and whether you're male or female makes a difference on what that value should be.


First long-distance heart surgery performed via robot ZDNet

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A doctor in India has performed a series of five percutaneous coronary intervention (PCI) procedures on patients who were 20 miles away from him. The feat was pulled off using a precision vascular robot developed by Corindus. The results of the surgeries, which were successful, have just been published in EClinicalMedicine, a spin-off of medical journal The Lancet. The feat is an example of telemedicine, an emerging field that leverages advances in networking, robotics, mixed reality, and communications technologies to beam in medical experts to remote locations for everything from consultations to surgical procedures. Telemedicine, which could decentralize healthcare by distributing doctors into local communities virtually, could ease shortages of nurses and doctors and potentially cut healthcare costs.


Predicting risk of heart failure for diabetes patients with help from machine learning

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Heart failure is an important potential complication of type 2 diabetes that occurs frequently and can lead to death or disability. Earlier this month, late-breaking trial results revealed that a new class of medications known as SGLT2 inhibitors may be helpful for patients with heart failure. These therapies may also be used in patients with diabetes to prevent heart failure from occurring in the first place. However, a way of accurately identifying which diabetes patients are most at risk for heart failure remains elusive. A new study led by investigators from Brigham and Women's Hospital and UT Southwestern Medical Center unveils a new, machine-learning derived model that can predict, with a high degree of accuracy, future heart failure among patients with diabetes.


Using machine learning to estimate risk of cardiovascular death

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Humans are inherently risk-averse: We spend our days calculating routes and routines, taking precautionary measures to avoid disease, danger, and despair. Still, our measures for controlling the inner workings of our biology can be a little more unruly. With that in mind, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with a new system for better predicting health outcomes: a machine learning model that can estimate, from the electrical activity of their heart, a patient's risk of cardiovascular death. The system, called "RiskCardio," focuses on patients who have survived an acute coronary syndrome (ACS), which refers to a range of conditions where there's a reduction or blockage of blood to the heart. Using just the first 15 minutes of a patient's raw electrocardiogram (ECG) signal, the tool produces a score that places patients into different risk categories.



New Biomarker 'Fingerprint' with AI Technology Can Now Predict Future Heart Attacks

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Technology and AI are increasingly being used to improve our lives, especially in the medical field. Now, researchers at the University of Oxford have used machine learning to help estimate the health of arteries and have developed a new biomarker to predict heart disease, and prevent future heart attacks. The researchers claim it can identify people at risk five years before it strikes. The typical procedure for those with chest pain is to conduct CCTA or coronary CT angiogram -- an imaging test to check the arteries. "If there is no significant narrowing of the artery, which accounts for about 75 per cent of scans, people are sent home, yet some of them will still have a heart attack at some point in the future," the press release claims.


Using machine learning to estimate risk of cardiovascular death

#artificialintelligence

Humans are inherently risk-averse: We spend our days calculating routes and routines, taking precautionary measures to avoid disease, danger, and despair. Still, our measures for controlling the inner workings of our biology can be a little more unruly. With that in mind, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with a new system for better predicting health outcomes: a machine learning model that can estimate, from the electrical activity of their heart, a patient's risk of cardiovascular death. The system, called "RiskCardio," focuses on patients who have survived an acute coronary syndrome (ACS), which refers to a range of conditions where there's a reduction or blockage of blood to the heart. Using just the first 15 minutes of a patient's raw electrocardiogram (ECG) signal, the tool produces a score that places patients into different risk categories.


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

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The researchers claim that clinical practitioners and health systems "urgently require efficient detection processes" as a result of "high prevalence, significant mortality rates and sustained healthcare costs".


New Data Shows Artificial Intelligence Technology Can Help Doctors Better Determine Which Patients are Having a Heart Attack

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ABBOTT PARK, Ill., Sept. 12, 2019 -- Abbott announced that new research, published in the journal Circulation, found its algorithm could help doctors in hospital emergency rooms more accurately determine if someone is having a heart attack or not, so that they can receive faster treatments or be safely discharged.1 In this study, researchers from the U.S., Germany, U.K., Switzerland, Australia and New Zealand looked at more than 11,000 patients to determine if Abbott's technology developed using artificial intelligence (AI) could provide a faster, more accurate determination that someone is having a heart attack or not. The study found that the algorithm provided doctors a more comprehensive analysis of the probability that a patient was having a heart attack or not, particularly for those who entered the hospital within the first three hours of when their symptoms started. "With machine learning technology, you can go from a one-size-fits-all approach for diagnosing heart attacks to an individualized and more precise risk assessment that looks at how all the variables interact at that moment in time," said Fred Apple, Ph.D., Hennepin HealthCare/ Hennepin County Medical Center, professor of Laboratory Medicine and Pathology at the University of Minnesota, and one of the study authors. "This could give doctors in the ER more personalized, timely and accurate information to determine if their patient is having a heart attack or not." A team of physicians and statisticians at Abbott developed the algorithm* using AI tools to analyze extensive data sets and identify the variables most predictive for determining a cardiac event, such as age, sex and a person's specific troponin levels (using a high sensitivity troponin-I blood test**) and blood sample timing.