Board-certified cardiothoracic surgeon Dr. Jeremy London, based in Savannah, Georgia, explains why VO2 max and muscle mass are the main indicators of longevity. The first artificial intelligence (AI) stethoscope has gone beyond listening to a heartbeat. Researchers at Imperial College London and Imperial College Healthcare NHS Trust discovered that an AI stethoscope can detect heart failure at an early stage. The TRICORDER study results, published in BMJ Journals, found that the AI-enabled stethoscope can help doctors identify three heart conditions in just 15 seconds. According to the British Heart Foundation (BHF), which partially funded the study, the researchers analyzed data from more than 1.5 million patients, focusing on people with heart failure symptoms like breathlessness, swelling and fatigue.

Fox News anchor Bret Baier has the latest on the Murdoch Children's Research Institute's partnership with the Gladstone Institutes for the'Decoding Broken Hearts' initiative on'Special Report.' Australia's Murdoch Children's Research Institute is helping scientists use stem cell medicine and artificial intelligence to develop precision therapies for pediatric heart disease, the leading cause of death and disability in children. Around 260,000 children die from heart disease around the world each year. In the U.S., a child is born with a heart defect every 15 minutes. "We're really interested in understanding how kids develop heart disease and where we can interfere to stop it progressing," Murdoch Children's Research Institute (MCRI) Heart Disease Group Leader David Elliott said.

A common heart condition that plagues small dogs can be picked up by AI, experts have found. Mitral valve disease regularly affects breeds such as King Charles spaniels, miniature poodles, Pomeranians and chihuahuas. It occurs when one of the heart's valves becomes distorted and leaky. It can progress to become fatal if not treated early on. A research team, led by the University of Cambridge, adapted an algorithm originally designed for humans and found it could automatically detect and grade heart murmurs in dogs - one of the main indicators of the disease.

Did you know in 2.17mins you can discover your present heart condition? I think you'll agree with me when I say: Artificial Intelligence is the future of computing. The study of AI has improved over the years with multiple universities teaching the fundamentals and transcending verbal courses to real life sectors with the health sector being one of the foremost benefactor. Artificial intelligence in the health sector is gradually taking the world by surprise. From a history of the earliest notable work in AI by Alan Mathison Turing in the mid-20th century, now, we have over 400% increase in student study on AI according to Will Hazell an education correspondent in 2021.

Detecting heart disease early is one of the key steps in treating the condition. But until now, doctors have had difficulty differentiating cardiovascular conditions through ultrasounds alone. In February 2022, scientists at the Smidt Heart Institute at Cedars-Sinai announced the creation of artificial intelligence that can not only detect heart disease early, but can tell the difference between conditions that look almost similar to the naked eye. More than half of U.S. adults suffer with some form of heart disease, according to an American Heart Association report in 2019. There are several causes of heart disease, including obesity, smoking, poor diet, lack of exercise, hypertension and genetics.

Nearly 30% of the people in the rural areas of Bangladesh are below the poverty level. Moreover, due to the unavailability of modernized healthcare-related technology, nursing and diagnosis facilities are limited for rural people. Therefore, rural people are deprived of proper healthcare. In this perspective, modern technology can be facilitated to mitigate their health problems. ECG sensing tools are interfaced with the human chest, and requisite cardiovascular data is collected through an IoT device. These data are stored in the cloud incorporates with the MQTT and HTTP servers. An innovative IoT-based method for ECG monitoring systems on cardiovascular or heart patients has been suggested in this study. The ECG signal parameters P, Q, R, S, T are collected, pre-processed, and predicted to monitor the cardiovascular conditions for further health management. The machine learning algorithm is used to determine the significance of ECG signal parameters and error rate. The logistic regression model fitted the better agreements between the train and test data. The prediction has been performed to determine the variation of PQRST quality and its suitability in the ECG Monitoring System. Considering the values of quality parameters, satisfactory results are obtained. The proposed IoT-based ECG system reduces the health care cost and complexity of cardiovascular diseases in the future.

The first-of-its-kind AI tool, described in a new paper in the Journal of Cardiovascular Magnetic Resonance, analyses heart MRI scans in just 20 seconds whilst the patient is in the scanner. This compares to the 13 minutes or more it would take for a doctor to manually analyse the images after the MRI scan has been performed. Each year, around 120,000 heart MRI scans are performed in the UK. The researchers say that the AI will free-up valuable time of healthcare professionals – saving around 3,000 clinician days every year – so their attention can be directed to seeing more patients on NHS waiting lists, which will ultimately help with the backlog in vital heart care. The AI will also give patients and doctors more confidence in the results so that they can make better decisions about a person's treatment and possible surgeries.

"These two heart conditions are challenging for even expert cardiologists to accurately identify, and so patients often go on for years to decades before receiving a correct diagnosis," said David Ouyang, MD, a cardiologist in the Smidt Heart Institute and senior author of the study. "Our AI algorithm can pinpoint disease patterns that can't be seen by the naked eye, and then use these patterns to predict the right diagnosis." The two-step, novel algorithm was used on over 34,000 cardiac ultrasound videos from Cedars-Sinai and Stanford Healthcare's echocardiography laboratories. When applied to these clinical images, the algorithm identified specific features - related to the thickness of heart walls and the size of heart chambers - to efficiently flag certain patients as suspicious for having the potentially unrecognized cardiac diseases. "The algorithm identified high-risk patients with more accuracy than the well-trained eye of a clinical expert," said Ouyang.

Physician-scientists in the Smidt Heart Institute at Cedars-Sinai have created an artificial intelligence (AI) tool that can effectively identify and distinguish between two life-threatening heart conditions that are often easy to miss: hypertrophic cardiomyopathy and cardiac amyloidosis. The new findings were published in JAMA Cardiology. "These two heart conditions are challenging for even expert cardiologists to accurately identify, and so patients often go on for years to decades before receiving a correct diagnosis," said David Ouyang, MD, a cardiologist in the Smidt Heart Institute and senior author of the study. "Our AI algorithm can pinpoint disease patterns that can't be seen by the naked eye, and then use these patterns to predict the right diagnosis." The two-step, novel algorithm was used on over 34,000 cardiac ultrasound videos from Cedars-Sinai and Stanford Healthcare's echocardiography laboratories.

It still remains to be seen whether the sci-fi genre is correct and artificial intelligence will one day rise up against the human race, but in the meantime, AI just might save your life. An algorithm developed by the Mayo Clinic can significantly increase the number of cases of low ejection fraction caught in its earliest stages, when it's still most treatable, according to a study published this month in Nature Medicine. The condition, in which the heart is unable to pump enough blood from its chamber with each contraction, is associated with cardiomyopathy and heart failure and is often symptomless in its early stages. Traditionally, the only way to diagnose low ejection fraction is with the use of an echocardiogram, a time-consuming and expensive cardiac ultrasound. The Mayo Clinic's AI algorithm, however, can screen for low ejection fraction in a standard 12-lead electrocardiogram (EKG) reading, which is a much faster and more readily available tool. In the study, more than 22,600 patients received an EKG as part of their usual primary care checkups, then were randomly assigned to have their results analyzed by the AI or by a physician as usual.