Barking, Havering and Redbridge University Hospitals NHS Trust is improving its response to stroke care with the launch of new software which uses artificial intelligence. The Brainomix software acts as a second opinion by analysing CT images of the brain and blood vessels, and automatically highlighting blocked blood vessels to indicate possible areas of damage. It also means stroke teams will be able to easily share scanned images to aid rapid diagnosis and support clinical decisions and treatments. Amanda Martin, stroke co-clinical lead at the trust, said: "At a local level, this decision support tool will help us to speed up diagnosis and therefore patient care in a simple and safe way… we are hoping that the implementation of Brainomix will support the highly specialised thrombectomy pathway, facilitating the timely transfer of those eligible for treatment to the trust." The AI technology can be used as a mobile app, meaning clinical decisions can be made swiftly and from anywhere. It will also connect the trust's stroke team with colleagues at University College London Hospitals NHS Foundation Trust and Barts Health NHS Trust and provide a 24/7 service.
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. According to the authors, the results demonstrate that these health conditions are associated with deviations of one's predicted age from one's chronological age.
We have enough problems these days. The last thing we need to worry about is our health or the high costs of care. That's why many people are turning to digital health solutions. As noted by The Wall Street Journal, many of these solutions can reportedly help manage diabetes, improve sleep, monitor heart health, encourage weight loss, track whether patients are sticking to physical therapy regimens, and more.(3) Due to the recent health scare, governments and healthcare systems worldwide may now realize how essential digital health has become.
One of the most significant challenges to the advancement of precision medicine has been the lack of an infrastructure to support translational bioinformatics, supporting organizations as they work to uncover unique datasets to find novel associations and signals. By supporting greater interoperability and collaboration, data scientists, developers, clinicians and pharmaceutical partners have the opportunity to leverage machine learning to reduce the time it takes to move from insight to discovery, ultimately leading to the right patients receiving the right care, with the right therapeutic at the right time. To get a better understanding of challenges surrounding precision medicine and its future, Healthcare IT News sat down with Taha Kass-Hout, director of machine learning at AWS. Q: You've said that one of the most significant challenges to the advancement of precision medicine has been the lack of an infrastructure to support translational bioinformatics. Please explain this challenge in detail. A: One of the challenges in developing and utilizing storage, analytics and interpretive methods is the sheer volume of biomedical data that needs to be transformed that often resides on multiple systems and in multiple formats.
Baylor St. Luke's Is First in Houston To Adopt Artificial Intelligence for Stroke Care As one of the leaders in stroke care in Houston and the surrounding areas, Baylor St. Luke's Medical Center continues on its promise to leverage the most advanced innovations to provide the best care to its patients. Baylor St. Luke's invested in artificial intelligence technology to service the stroke care team in diagnosing stroke and providing efficient and reliable treatment. Viz.ai technology allows for rapid detection and notification of suspected large vessel occlusion (LVO) strokes. Chethan P Venkatasubba Rao, Medical Director of the Neuroscience ICU at Baylor St. Luke's With Stroke, Timing Is Everything During a stroke, timing is the most important factor for minimizing brain damage. Knowing the signs of a stroke will allow for F.A.S.T. action in the event of an emergency.
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.
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.
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.
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).
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.