Goto

Collaborating Authors

cardiology


Artificial intelligence continues to evolve in cardiology

#artificialintelligence

Artificial intelligence continues to affect cardiology with improved capabilities to diagnose certain conditions such as atrial fibrillation, and research is underway to learn more about its use in disease management, a presenter said. Although ECG watches were patented in the early 1990s, smartwatches of today are different because of lower manufacturing costs, changes in the regulatory landscape, AI and smartphone-based data transfers, Mintu P. Turakhia, MD, MAS, associate professor and executive director of the Center for Digital Health at Stanford University School of Medicine and a Cardiology Today Next Gen Innovator, said in his presentation at the Scientific Session and Exhibition of the American Society of Nuclear Cardiology. The use of wearables today has increased due to more people having smartphones, with 81% of the population worldwide owning a smartphone. Approximately 20% of people in the U.S. now own a consumer wearable, which is increasing annually, according to the presentation. People often track several metrics using consumer wearables, including heart rate, BP and blood glucose.


Machine learning prediction in cardiovascular diseases: a meta-analysis

#artificialintelligence

Several machine learning (ML) algorithms have been increasingly utilized for cardiovascular disease prediction. We aim to assess and summarize the overall predictive ability of ML algorithms in cardiovascular diseases. A comprehensive search strategy was designed and executed within the MEDLINE, Embase, and Scopus databases from database inception through March 15, 2019. The primary outcome was a composite of the predictive ability of ML algorithms of coronary artery disease, heart failure, stroke, and cardiac arrhythmias. Of 344 total studies identified, 103 cohorts, with a total of 3,377,318 individuals, met our inclusion criteria. For the prediction of coronary artery disease, boosting algorithms had a pooled area under the curve (AUC) of 0.88 (95% CI 0.84–0.91), and custom-built algorithms had a pooled AUC of 0.93 (95% CI 0.85–0.97). For the prediction of stroke, support vector machine (SVM) algorithms had a pooled AUC of 0.92 (95% CI 0.81–0.97), boosting algorithms had a pooled AUC of 0.91 (95% CI 0.81–0.96), and convolutional neural network (CNN) algorithms had a pooled AUC of 0.90 (95% CI 0.83–0.95). Although inadequate studies for each algorithm for meta-analytic methodology for both heart failure and cardiac arrhythmias because the confidence intervals overlap between different methods, showing no difference, SVM may outperform other algorithms in these areas. The predictive ability of ML algorithms in cardiovascular diseases is promising, particularly SVM and boosting algorithms. However, there is heterogeneity among ML algorithms in terms of multiple parameters. This information may assist clinicians in how to interpret data and implement optimal algorithms for their dataset.


Fitbit Sense review: A half-baked smartwatch for the wellness warrior

Mashable

It's taken almost four years, but it feels like Fitbit has finally found its footing in the world of smartwatches and the Fitbit Sense is proof -- sort of. At this point, it's no secret that Fitbit is extremely capable of manufacturing accurate, easy-to-use, sleek, and affordable fitness trackers. But when it comes to smartwatches, it's safe to say the journey hasn't been as smooth. Between 2016 and 2017, Fitbit released two devices that straddled the line between smartwatch and fitness tracker: the Blaze and Ionic. While both packed every sensor necessary to track your daily fitness needs, each one was just as clunky and unattractive as the one before it. These just weren't wrist-worn accessories anyone really wanted to wear on a daily basis.


Machine Learning Biases Might Define Minority Health Outcomes

#artificialintelligence

Whether or not you're aware, your Google searches, questions posed to Siri, and Facebook timeline all rely on artificial intelligence (AI) to perform effectively. Artificial intelligence is the simulation of human intelligence processes by machines. The goal of artificial intelligence is to build models that can perform specific tasks as intelligently as humans can, if not better. Much of the AI you encounter on a daily basis uses a technique known as machine learning, which uses predictive modeling to generate accurate predictions when given random quantities of data. Because predictive models are built to find relational patterns in data, they learn to favor efficiency over fairness.


Machine learning in Medical field

#artificialintelligence

In this post, I will test the effectiveness of machine learning in the medical field especially in classifying whether or not a person has heart disease. AS well, I will guide you through building some classifiers from the scikit-learn library then, we will highlight the best accuracy. According to the World health organization, 17.9 million deaths caused by Cardiovascular diseases each year. Our objective here is help doctors diagnose heart disease faster, and also inform patient who are at high risk. Through this post we will try to solve these important questions: 1- Who are more likely to have heart disease?


Can Machine Learning Make Fecal Testing Part of CVD Screening?

#artificialintelligence

Machine learning analysis of stool samples may provide a helpful first pass for the mass screening for any type of cardiovascular disease (CVD) in patients, researchers claimed. Various machine learning algorithms were fed gut microbiota data and, with training, were subsequently able to distinguish between people with and without CVD, with ROC curves as high as 0.70, reported a group led by Sachin Aryal, an MS student in bioinformatics at the University of Toledo, Ohio. "While this demonstrates the promising potential of applying microbiome-based ML [machine learning] for predicting CVD, in the future, it will be of interest to further calibrate and improve predictive capability of ML modeling by including more samples from different sources or stratifying specific types of CVD incorporated with combinatorial features such as health records, in addition to gut microbiome data," the authors said. Their study was presented as a poster at the virtual Hypertension meeting, sponsored by the American Heart Association, and was simultaneously published online in the November 2020 issue of Hypertension. Investigators claimed theirs as the first study to apply existing knowledge of dysbiosis of gut microbiota in CVD patients to a machine learning approach to CVD screening.


AI standards launched to help tackle problem of overhyped studies

#artificialintelligence

The first international standards for the design and reporting of clinical trials involving artificial intelligence have been announced in a move experts hope will tackle the issue of overhyped studies and prevent harm to patients. While the possibility that AI could revolutionise healthcare has fuelled excitement, in particular around screening and diagnosis, researchers have previously warned that the field is strewn with poor-quality research. Now an international team of experts has launched a set of guidelines under which clinical trials involving AI will be expected to meet a stringent checklist of criteria before being published in top journals. The new standards are being simultaneously published in the BMJ, Nature Medicine and Lancet Digital Health, expanding on existing standards for clinical trials – put in place more than a decade ago for drugs, diagnostic tests, and other interventions – to make them more suitable for AI-based systems. Prof Alastair Denniston of the University of Birmingham, an expert in the use of AI in healthcare and member of the team, said the guidelines were crucial to making sure AI systems were safe and effective for use in healthcare settings.


AI tech use by NHS to be sped up with £50m investment

#artificialintelligence

NHS patients will benefit from new artificial intelligence (AI) technologies thanks to a £50 million boost. A range of AI-powered innovations which can analyse breast cancer screening scans and assess emergency stroke patients will be tested and scaled. Take-home technology could also see patients given devices and software that can turn their smartphone into a clinical grade medical device for monitoring kidney disease, or a wearable patch to detect irregular heartbeats, one of the leading causes of strokes and heart attacks. The award is managed by the Accelerated Access Collaborative in partnership with NHSX and the National Institute for Health Research. The package also includes funding to support the research, development and testing of promising ideas which could be used in the NHS in future to help speed up diagnosis or improve care for a range of conditions including sepsis, cancer and Parkinson's.


AI standards launched to help tackle problem of overhyped studies

The Guardian

The first international standards for the design and reporting of clinical trials involving artificial intelligence have been announced in a move experts hope will tackle the issue of overhyped studies and prevent harm to patients. While the possibility that AI could revolutionise healthcare has fuelled excitement, in particular around screening and diagnosis, researchers have previously warned that the field is strewn with poor-quality research. Now an international team of experts has launched a set of guidelines under which clinical trials involving AI will be expected to meet a stringent checklist of criteria before being published in top journals. The new standards are being simultaneously published in the BMJ, Nature Medicine and Lancet Digital Health, expanding on existing standards for clinical trials – put in place more than a decade ago for drugs, diagnostic tests, and other interventions – to make them more suitable for AI-based systems. Prof Alastair Denniston of the University of Birmingham, an expert in the use of AI in healthcare and member of the team, said the guidelines were crucial to making sure AI systems were safe and effective for use in healthcare settings.


Artificial intelligence in health care: preparing for the fifth Industrial Revolution

#artificialintelligence

AI has arrived, with the potential for enormous change in the delivery of health care, but are we ready? Artificial intelligence (AI) is the trigger for the next great transformation of society: the fifth Industrial Revolution. AI has already arrived in health care, but are we ready for the kind of changes that it will introduce? In this article, we map out the current areas where AI has begun to permeate and make predictions about the kind of changes it will make to health care. AI comprises any digital system "that mimics human reasoning capabilities, including pattern recognition, abstract reasoning and planning".1 It includes the concept of machine learning, where machines are able to learn from experience in ways that mimic human behaviour, but with the ability to assimilate much more data and with potential for greater accuracy and speed.