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Physician researcher artificial intelligence in paediatric cardiology

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The department of paediatric cardiology at the Beatrix Children's Hospital, University Medical Centre Groningen (UMCG), one of the 4 licensed centres for the treatment of congenital heart diseases in The Netherlands, is an international centre of expertise on pulmonary hypertension and right heart failure in children. The department is the national referral centre for children with pulmonary (arterial) hypertension. All Dutch children suspected to have pulmonary hypertension are referred to our centre for confirmation of diagnosis, initiation of therapy and standardized follow-up visits, in close collaboration with our network centres. Our department conducts leading clinical, fundamental, and translational research in the field of pulmonary hypertension and congenital heart disease, such as tetralogy of Fallot, Fontan circulation and right heart failure. Our clinical research focuses on the improvement of diagnostic and imaging techniques, treatment strategies and survival of these patient groups.


Deep learning with SPECT accurately predicts major adverse cardiac events

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An advanced artificial intelligence technique known as deep learning can predict major adverse cardiac events more accurately than current standard imaging protocols, according to research presented at the Society of Nuclear Medicine and Molecular Imaging 2021 Annual Meeting. Utilizing data from a registry of more than 20,000 patients, researchers developed a novel deep learning network that has the potential to provide patients with an individualized prediction of their annualized risk for adverse events such as heart attack or death. Deep learning is a subset of artificial intelligence that mimics the workings of the human brain to process data. Deep learning algorithms use multiple layers of "neurons," or non-linear processing units, to learn representations and identify latent features relevant to a specific task, making sense of multiple types of data. It can be used for tasks such as predicting cardiovascular disease and segmenting lungs, among others.


Artificial Intelligence Predicts Brain Age From EEG Signals Recorded During Sleep - Neuroscience News

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Summary: A new deep neural network can accurately predict a healthy person's brain age based on EEG data collected from a sleep study. A study shows that a deep neural network model can accurately predict the brain age of healthy patients based on electroencephalogram data recorded during an overnight sleep study, and EEG-predicted brain age indices display unique characteristics within populations with different diseases. The study found that the model predicted age with a mean absolute error of only 4.6 years. There was a statistically significant relationship between the Absolute Brain Age Index and: epilepsy and seizure disorders, stroke, elevated markers of sleep-disordered breathing (i.e., apnea-hypopnea index and arousal index), and low sleep efficiency. The study also found that patients with diabetes, depression, severe excessive daytime sleepiness, hypertension, and/or memory and concentration problems showed, on average, an elevated Brain Age Index compared with the healthy population sample.


How AI Vendors Can Navigate the Health Care Industry

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The adoption of AI in health care is being driven by an exponential growth of health data, the broad availability of computational power, and foundational advances in machine learning techniques. AI has already demonstrated the potential to create value by reducing costs, expanding access, and improving quality. But in order for AI to realize its transformative potential at scale, its proponents need business models optimized to best capture that value. AI changes the rules of business and, as ever, there are some unique considerations in health care. In order to understand these, we studied AI across 15 sets of use cases. These span five domains of health care (patient engagement, care delivery, population health, R&D, and administration) and cover three types of functions (measure, decide, and execute).


New artificial intelligence prediction model may reduce hospital readmission rates

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Artificial intelligence may be a useful tool for providers to better predict patient outcomes. It was beneficial for Amod Amritphale, M.D., the director of cardiovascular research and an interventional cardiologist at USA Health, who used computer algorithms to learn more about patients who suffered a stroke. Public data showed that some of those patients were readmitted to the hospital within 30 days, even after undergoing a surgical procedure to open a narrowed carotid artery. Carotid arteries are blood vessels located on both sides of the neck that deliver blood to the head and brain. The procedure, known as carotid artery stenting (CAS), is usually performed as a preventative method or after a stroke.


AI-Enhanced Cardiology Takes Another Step Forward

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John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article. Asymptomatic left ventricular systolic dysfunction (ALVSD) may not be the most familiar disorder in medicine, but it nonetheless increases a patient's risk of heart failure and death. Unfortunately, ALVSD is not that easily detected. Characterized by low ejection fraction (EF) -- a measure of how much blood the heart pumps out during each contraction -- it's readily diagnosed with an echocardiogram. But because the procedure is expensive, it's not recommended as routine screening for the general public.


Artificial intelligence predicts when heart will fail

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For patients, the act of getting the scan is no different, but the results are sent off to Oxford, analysed by Caristo, and returned within a handful of days. The difference between a regulation coronary CT scan and the new analysis lies in what comes after the scan itself, when the images are inputted into CaRi-Heart. Scientists say that although plaque build up is a serious problem, around half of all heart attacks do not occur in fully-blocked arteries. The other half are caused when small pieces of plaque rupture, releasing cholesterol into the blood which triggers a clot and ultimately leads to a heart attack. These are impossible to predict with current CT scans.


Artificial intelligence to predict heart disease

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A new project using artificial intelligence technology could spell a medical breakthrough for people suffering from, or at risk of, coronary artery disease, the single leading cause of death in Australia. The approach being developed by researchers at The University of Western Australia could allow for more accurate diagnosis and faster reporting across all aspects of healthcare, improving the quality and consistency of patient care. The UWA team of experts in cardiac imaging and artificial intelligence was awarded more than $896,606 through a Medical Research Future Fund Frontiers grant to develop a tool to predict the risk of coronary heart disease from heart computed tomography (CT) scans. Coronary artery disease resulting from the build-up of plaque affects more than 1.2 million Australians; however traditional methods using CT imaging of the heart are cumbersome, time-consuming and may have limited accuracy. Led by Professor Girish Dwivedi, the UWA Wesfarmers Chair in Cardiology, the team, including Professor Mohammed Bennamoun, Professor Farid Boussaid, Dr Frank Sanfilippo and Dr Abdul Ihdayhid, together with medical technology company Artrya Ltd, will create an artificial intelligence-based risk assessment tool that will better detect plaque on heart CT scans.


AI Healthcare Company Offers Software as a Medical Device

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It's probably no surprise that money is pouring into life sciences and healthcare startups during the biggest medical crisis in a century. CB Insights reported that global healthcare funding hit a new record $31.6 billion in this first quarter of 2021. It's also no shock that the two biggest trends – artificial intelligence and telehealth – also reaped record amounts of private cash. AI healthcare startups raised nearly $2.5 billion, while telehealth companies did even better by netting $4.2 billion in equity funding. That's the third consecutive quarter to hit record highs in both sectors dating back to Q3'20.


AI Algorithm Aids Early Detection of Low Ejection Fraction

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FRIDAY, May 28, 2021 (HealthDay News) -- An artificial intelligence (AI) algorithm that uses data from electrocardiography can help increase the diagnosis of low ejection fraction (EF), according to a study published online May 6 in Nature Medicine. Xiaoxi Yao, Ph.D., from the Mayo Clinic in Rochester, Minnesota, and colleagues randomly assigned 120 primary care teams, including 358 clinicians, to intervention (access to AI results from the low ejection fraction algorithm developed by Mayo and licensed to Anumana Inc.; 181 clinicians) or control (usual care; 177 clinicians) in a pragmatic trial at 45 clinics and hospitals. A total of 22,641 adult patients with echocardiography performed as part of routine care were included (11,573 in the intervention group; 11,068 controls). The researchers found positive AI results, indicating a high likelihood of low EF, in 6.0 percent of patients in both arms. More echocardiograms were obtained for patients with positive results by clinicians in the intervention group (49.6 versus 38.1 percent), but echocardiogram use was similar in the overall cohort (19.2 versus 18.2 percent).