The majority of experts and opinion leaders believe that artificial intelligence (AI) is going to revolutionise many industries, including healthcare . In the short term, the power and potential of AI appear most suitable for complementing human expertise. In other words, machines will help humans do a better job. Consequently, it is anticipated that AI will help with repetitive tasks, in-depth quantification and classification of findings, improved patient and disease phenotyping and, ultimately, with better outcomes for patients, physicians, hospital administrators, insurance companies and governments . This focus issue of the Netherlands Heart Journal aims to help general cardiologists explore the state of the art of AI in cardiology.
Last week, Eko Devices announced a new service that matches ECG and heart sound recordings with clinical data to help pinpoint novel drug-data combinations. The Silicon Valley startup is pitching the platform, called Eko Home, as a resource for clinical trials targeting new therapies. The new platform is already seeing some action. According to the company, an ongoing Mayo Clinic study exploring how carvedilol-based cardiovascular therapies could reduce heart failure or other heart function declines among breast cancer patients undergoing chemotherapy is using the Eko Home platform to drive insights. Eko -- which is best known for its Eko Duo device, a smart remote monitor that's part stethoscope, part ECG -- also said in its announcement that it "expects to offer the drug-data combinations with other life science partners by the end of the year with additional plans to offer its SDK to hospitals and healthcare providers that wish to build the platform directly into their applications."
Although early and requiring further research before implementation, the findings could aid doctors investigating unexplained strokes or heart failure, enabling appropriate treatment. Researchers have trained an artificial intelligence model to detect the signature of atrial fibrillation in 10-second electrocardiograms (ECG) taken from patients in normal rhythm. The study, involving almost 181,000 patients and published in The Lancet, is the first to use deep learning to identify patients with potentially undetected atrial fibrillation and had an overall accuracy of 83%. Atrial fibrillation is estimated to affect 2.7–6.1 million people in the United States and is associated with increased risk of stroke, heart failure and mortality. It is difficult to detect on a single ECG because patients' hearts can go in and out of this abnormal rhythm, so atrial fibrillation often goes undiagnosed.
The academic medical center of the University of Michigan is leveraging investments in artificial intelligence, machine learning and advanced analytics to unlock the value of its health data. According to Andrew Rosenberg, MD, chief information officer for Michigan Medicine, the organization currently has 34 ongoing AI and machine leaning projects, 28 of which have principal investigators. "There's a lot of collaboration around these projects--as there should be for the diversity of thought and background needed to deal with complex problems--working with at least seven other U of M schools," Rosenberg told the Machine Learning for Health Care conference on Friday in Ann Arbor, Mich. "That's one of the powers that we enjoy." One of the machine learning projects cited by Rosenberg leverages a combination of electronic health records, monitor data and analytics to predict acute hemodynamic instability--when blood flow drops and deprives the body of oxygen--which is one of the most common causes of death for critically ill or injured patients.
Researchers have developed a machine-learning method that can pinpoint a dangerous heart condition, new research shows. With 90 percent accuracy, researchers were able to detect atrial fibrillation using artificial intelligence-guided EKG, according to a study published Friday in The Lancet. The method even worked when no symptoms were present in patients. "When people come in with a stroke, we really want to know if they had atrial fibrillation in the days before the stroke, because it guides the treatment," Paul Friedman, chair of the at Mayo Clinic's Department of Cardiovascular Medicine and study senior author, said in a news release. "Blood thinners are very effective for preventing another stroke in people with atrial fibrillation. But for those without atrial fibrillation, using blood thinners increases the risk of bleeding without substantial benefit."
Artificial intelligence (AI) can detect signs of existing or emerging A-fib in ECGs that exhibit normal sinus rhythm, Mayo Clinic researchers have found. Their retrospective analysis, published online yesterday in the Lancet, reports a high degree of accuracy with only one ECG, and this accuracy increases when AI is applied to multiple ECGs from the same patient. "A very common clinical scenario is that someone comes to the hospital with an ischemic stroke, and we want to know whether they have atrial fibrillation," senior author Paul A. Friedman, MD (Mayo Clinic, Rochester, MN), noted to TCTMD. "We have done previous work using neural networks, machine learning, that found that it was extremely powerful in detecting subtle patterns [in ECG tracings], and we wondered: If someone had atrial fibrillation yesterday, is there any way that it might leave a trace of a finding on an ECG today that's too subtle for a human to read but a computer could pick up?" To find out, the investigators, led by Zachi I. Attia, MSc, and Peter A. Noseworthy, MD, drew upon records in the Mayo Clinic Digital Data Vault.
The increasing use of Artificial intelligence, Big Data and IoT devices are changing the pattern of the healthcare business. Neha Rastogi, co-founder and COO of Agatsa, in an email interview with Zee Business online, talked about various aspects of healthcare business and innovations in this space. Do you think Budget 2019 was successful in addressing problems faced by startups? The Budget did address some major areas including simplification of tax compliances and income tax scrutiny. Further, the provision of expanding PMKY by including cutting-edge skills like AI, analytics and 3D printing, etc. are welcome steps.
According to a study published in the journal Radiology, when machine learning is combined with common heart scan, it can predict heart attacks and other cardiac events better than traditional risk models. Observing the worldwide data, heart disease is the most common and leading cause of death in both men and women, especially in the United States. Consequently, precision in risk assessment is mandatory for early intervention to say diet, exercise, drugs including cholesterol-lowering statins. In this context, CCTA (Coronary Computed Tomography Arteriography) provides with a highly detailed set of images of the heart vessels and happens to be a refining risk assessment tool. The study lead author Kevin M. Johnson, M.D., CCTA recently investigated a machine learning system which can mine the myriad details in CCTA-obtained images for a better and comprehensive prognostic picture.
Sensyne Health plc (LSE: SENS), the British clinical AI technology company, today announces that it has signed an initial two-year collaboration agreement with Bayer to accelerate the clinical development of new treatments for cardiovascular disease using Sensyne Health's proprietary clinical AI technology platform. The initial agreement will generate revenues for Sensyne Health of £5 million across the two-year collaboration. Sensyne Health's partner NHS trusts will receive a 4% share of all revenues generated by Sensyne Health under this collaboration. This will be in addition to the NHS Trust's existing shareholdings in Sensyne Health plc. The NHS Long Term Plan identifies cardiovascular disease as a clinical priority and the leading condition where lives can be saved by the NHS over the next 10 years.
Sensyne Health, a British artificial intelligence technology company, has signed an initial two-year collaboration agreement with Bayer to accelerate the clinical development of new treatments for cardiovascular disease using Sensyne Health's proprietary clinical AI technology platform. The initial agreement will generate revenue for Sensyne Health of £5 million across the two-year collaboration. Sensyne Health's partner National Health Service trusts will receive a 4% share of all revenue generated by Sensyne Health under this collaboration. This will be in addition to the NHS Trust's existing shareholdings in Sensyne Health. The NHS Long Term Plan identifies cardiovascular disease as a clinical priority and the leading condition where lives can be saved by the NHS over the next 10 years.