Collaborating Authors

Cardiology/Vascular Diseases

Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease


The medical field has seen a rapid increase in the development of artificial intelligence (AI)-based prediction models. With the introduction of such AI-based prediction model tools and software in cardiovascular patient care, the cardiovascular researcher and healthcare professional are challenged to understand the opportunities as well as the limitations of the AI-based predictions. In this article, we present 12 critical questions for cardiovascular health professionals to ask when confronted with an AI-based prediction model. We aim to support medical professionals to distinguish the AI-based prediction models that can add value to patient care from the AI that does not. Listen to the audio abstract of this contribution. Artificial intelligence (AI) and its subdiscipline machine learning are receiving increasing attention throughout medicine, including cardiovascular medicine.1,2 Proponents promise AI will change the way medicine and healthcare is practiced, by making use of technological advancements that allow for collection of increasingly detailed and diverse data and the ever-increasing computational ability to analyse and combine such data. An important part of these promises is the development and implementation of more accurate clinical prediction models (algorithms, tools, or rules, from here onwards simply referred to as prediction models) to improve--or according to some advocates, even revolutionize--screening, diagnosis, and prognostication of diseases.

Scaling assistive healthcare technology with 5G


With recent advances in communication networks and machine learning (ML), healthcare is one of the key application domains which stands to benefit from many opportunities, including remote global healthcare, hospital services on cloud, remote diagnosis or surgeries, among others. One of those advances is network slicing, making it possible to provide high-bandwidth, low-latency and personalized healthcare services for individual users. This is important for patients using healthcare monitoring devices that capture various biological signals (biosignals) such as from the heart (ECG), muscles (EMG), brain (EEG), or activities from other parts of the body. In this blog, we discuss the challenges to building a scalable delivery platform for such connected healthcare services, and how technological advances can help to transform this landscape significantly for the benefit of both users and healthcare service providers. Our specific focus is on assistive technology devices which are increasingly being used by many individuals.

Waveform Segmentation Using Deep Learning - MATLAB & Simulink


The electrical activity in the human heart can be measured as a sequence of amplitudes away from a baseline signal. The segmentation of these regions of ECG waveforms can provide the basis for measurements useful for assessing the overall health of the human heart and the presence of abnormalities [2]. Manually annotating each region of the ECG signal can be a tedious and time-consuming task. Signal processing and deep learning methods potentially can help streamline and automate region-of-interest annotation. This example uses ECG signals from the publicly available QT Database [3] [4].

Cranberries could improve memory and ward off dementia

Daily Mail - Science & tech

Eating a small bowl of cranberries every day could help ward off dementia, research suggested today. Scientists tested giving healthy older adults the equivalent of 100g of the fruit each day. Volunteers who ate a powdered version of the fruit -- which has a notoriously bitter taste -- were found to have a better memory recall after 12 weeks. And MRI scans showed those eating cranberries had better blood flow to important parts of the brain. People given cranberries also had 9 per cent lower bad cholesterol levels, according to the University of East Anglia study.

Classification SINGLE-LEAD ECG by using conventional neural network algorithm


Cardiac disease, including atrial fibrillation (AF), is one of the biggest causes of morbidity and mortality in the world, accounting for one third of all deaths. Cardiac modelling is now a well-established field.

Spotting Heart disease with AI - How far are we?


Cardiovascular Disease has long been the number one cause of death in the U.S. and some of the stats are startling: an American will have a heart attack approximately every 40 seconds for a total of 805,000 every year, At the same time, mortality and morbidity rates of CVD are increasing year by year, especially in developing regions. Studies have shown that approximately 80% of CVD-related deaths occur in low- and middle-income countries. Besides, these deaths occur at a younger age than in high-income countries. CVD represents a significant economic cost for society, around $351.2 billion in the US, chronically affecting patients' quality of life. The EU has estimated that the overall yearly cost amounts to €210 billion, allocating around 53% to healthcare costs (€111 billion), with 26% related to productivity losses (€54 billion), and the remaining 21% (€45 billion) to the informal care of people with CVD (European Cardiovascular Disease Statistics 2017).

AI Technology Can Predict Life-Threatening Heart Trouble, Researchers Say


Researchers at Johns Hopkins University developed artificial intelligence technology that may be able to assess a patient's risk of sudden cardiac death, which is when the heart abruptly stops beating. Sometimes, modern medicine isn't enough to help keep us healthy. The Johns Hopkins University researchers said artificial intelligence can help accurately predict if and when someone's heart will stop beating years in advance. "It uses deep learning on images in combination with deep learning also on clinical data to predict the patient's risk of sudden cardiac death over a period of 10 years," said Dr. Natalia Trayanova, a professor of biomedical engineering and medicine. Trayanova's team developed the AI technology and published their work in a medical journal.

Royal Papworth leads AI study into heart valve disease


Royal Papworth Hospital NHS Foundation Trust is leading a study into the use of artificial intelligence (AI) to diagnose heart valve disease. Royal Papworth is working with the University of Cambridge on the research, which hopes to develop a screening tool powered by AI to help diagnose the disease before symptoms are first displayed. The research will involve thousands of patients having four heart recordings that are collected via a Bluetooth stethoscope, in addition to the conventional route of an echocardiogram. Recordings will be uploaded to a machine-learning programme, so that the University of Cambridge can build an audio database of the noises associated with heart valve diseases. Ultimately, the research aims to create an artificially intelligent stethoscope that can analyse heart murmurs to provide either a diagnosis or determine if further investigation is needed.

Five predictions for healthcare in 2022


Ailing healthcare systems around the world are under the spotlight and the need for universal access to quality healthcare has never been greater as many factors continue to put pressure on the sector. These include ageing populations, clinical workforce challenges, rising utilisation stemming from a growing burden of chronic diseases like cancer, diabetes and cardiovascular disease (CVD), and reimbursement-related challenges. Reassuringly, the pandemic has forced governments to reconsider every aspect of their healthcare systems. For example, workforce size and shape, digital infrastructure, models of care focusing on primary-care pathways and digitally enabled interventions and disease surveillance, research, supply chain speed and resilience, access to care, data use, regulation, and service integration. In the UAE, health spending grew from Dh45 billion in 2016 to Dh61.7 billion in 2020.