Doctors have successfully developed an artificial intelligence-led stethoscope that can detect three heart conditions in 15 seconds. Invented in 1816, the traditional stethoscope – used to listen to sounds within the body – has been a vital part of every medic's toolkit for more than two centuries. Now a team have designed a hi-tech upgrade with AI capabilities that can diagnose heart failure, heart valve disease and abnormal heart rhythms almost instantly. The new stethoscope developed by researchers at Imperial College London and Imperial College healthcare NHS trust can analyse tiny differences in heartbeat and blood flow undetectable to the human ear, and take a rapid ECG at the same time. Details of the breakthrough, which could boost early diagnosis of the three conditions, were presented to thousands of doctors at the European Society of Cardiology annual congress in Madrid, the world's largest heart conference.

Fox News senior medical analyst Dr. Marc Siegel joins'Fox & Friends' to detail a new study linking marijuana use to heart disease and discuss data suggesting diet and exercise could help cancer patients. You might think heart disease comes with warning signs. Now, a new artificial intelligence tool called EchoNext is changing the game. It can flag hidden heart problems that even trained cardiologists miss just by analyzing a standard ECG. A routine, five-minute heart test you've probably already had could now unlock life-saving information if AI is watching.

Johns Hopkins researchers recently received a $195,000 Rapid Response Research grant from the National Science Foundation to, using machine learning, identify which COVID-19 patients are at risk of adverse cardiac events such as heart failure, sustained abnormal heartbeats, heart attacks, cardiogenic shock and death. Increasing evidence of COVID-19's negative impacts on the cardiovascular system highlights a great need for identifying COVID-19 patients at risk for heart problems, the researchers say. However, no such predictive capabilities currently exist. "This project will provide clinicians with early warning signs and ensure that resources are allocated to patients with the greatest need," says Natalia Trayanova, the Murray B. Sachs Professor in the Department of Biomedical Engineering at The Johns Hopkins University Schools of Engineering and Medicine and the project's principal investigator. The first phase of the one-year project, which just received IRB approval for Suburban Hospital and Sibley Memorial Hospital within the Johns Hopkins Health System (JHHS), will collect the following data from more than 300 COVID-19 patients admitted to JHHS: ECG, cardiac-specific laboratory tests, continuously-obtained vital signs like heart rate and oxygen saturation, and imaging data such as CT scans and echocardiography.

Diagnosis of such problems before the baby is born, allowing for prompt treatment within a week after birth, is known to markedly improve the prognosis, so there have been many attempts to develop technology to enables accurate and rapid diagnosis. However, today, fetal diagnosis depends heavily on observations by experienced examiners using ultrasound imaging, so it is unfortunately not uncommon for children to be born without having been properly diagnosed. In recent years, machine learning techniques such as deep learning have been developing rapidly, and there is great interest in the adoption of machine learning for medical applications. Machine learning can allow diagnostic systems to detect diseases more rapidly and accurately than human beings, but this requires the availability of adequate datasets on normal and abnormal subjects for a certain disease. Unfortunately, however, since congenital heart problems in children are relatively rare, there are no complete datasets, and up until now, prediction based on machine learning was not accurate enough for practical use in the clinic.

A research group led by scientists from the RIKEN Center for Advanced Intelligence Project (AIP) have developed a novel system that can automatically detect abnormalities in fetal hearts in real-time using artificial intelligence (AI). This technology could help examiners to avoid missing severe and complex congenital heart abnormalities that require prompt treatments, leading to early diagnosis and well-planned treatment plans, and could contribute to the development of perinatal or neonatal medicine. Congenital heart problems -- which can involve abnormalities of the atrium, ventricle, valves or blood vessel connections -- can be very serious, and account for about 20% of all newborn deaths. Diagnosis of such problems before the baby is born, allowing for prompt treatment within a week after birth, is known to markedly improve the prognosis, so there have been many attempts to develop technology to enables accurate and rapid diagnosis. However, today, fetal diagnosis depends heavily on observations by experienced examiners using ultrasound imaging, so it is unfortunately not uncommon for children to be born without having been properly diagnosed.

Several studies being presented at the American College of Cardiology's 67th Annual Scientific Session demonstrate how the computer science technique known as machine learning can be used to accurately predict clinical outcomes in patients with known or potential heart problems. Collectively, the findings suggest that machine learning may usher in a new era in digital health care tools capable of enhancing health care delivery by aiding routine processes and helping physicians assess patients' risk. While clinical scoring systems and algorithms have long been used in medical practice, there has been a marked uptick in the application of machine learning to improve such tools in recent years. In contrast to traditional algorithms that require all calculations to be pre-programmed, machine learning algorithms deduce the optimal set of calculations by looking for patterns in large collections of patient data. The new studies presented at ACC.18 demonstrate how machine learning can be used to predict outcomes such as diagnosis, death or hospital readmission; improve upon standard risk assessment tools; elucidate factors that contribute to disease progression; or to advance personalized medicine by predicting a patient's response to treatment.

Artificial intelligence (AI) has already proven useful in the healthcare industry, and now, two newly developed AI diagnostics systems could change how doctors diagnose heart disease and lung cancer. Cardiologists are very good at their jobs, but they're not infallible. To determine whether or not something's wrong with a patient's heart, a cardiologist will assess the timing of their heartbeat in scans. According to a report by BBC News, 80 percent of the time, their diagnosis of various heart problems is correct, but it's the remaining 20 percent that shows the process has room for improvement. To that end, a team of researchers from the John Radcliffe Hospital in Oxford, England, developed Ultromics, an AI diagnostics system that is more accurate than doctors at diagnosing heart disease. Ultromics was trained using the heart scans of 1,000 patients treated by the company's chief medical officer, Paul Leeson, as well as information about whether or not those patients went on to suffer heart problems.