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.

Nowadays, there are already plenty of choices when it comes to pet trackers, and some can even monitor heart rate, but apparently none could detect atrial fibrillation (AFib) until now. At CES 2024, GPS tracker specialist Invoxia unveiled the Minitailz, the world's first pet tracker that can keep an eye out for early heart disease symptoms on your cats or dogs. Together with other vital stats and activity log, the companion app then uses conversational generative AI to generate easy-to-read reports on your pets. According to the French company, Minitailz is able to track respiratory and heart vitals with an accuracy of 97 to 99 percent, thanks to its advanced biometric sensors combined with AI. Using deep learning algorithms, the set of data is then used to spot digital biomarkers that indicate stress, aging and pathologies.

Over the last couple of months, we've explored how AI is or will be applied in a wide range of medical markets, from ophthalmology to dentistry to Wound Care and some have started to realise the potential of AI whilst others are still just working it out. In cardiology however, there is a problem which AI can, and will, help with immediately – Atrial Fibrillation (AF). AF is, essentially, an irregular heartbeat which occurs in sporadic'episodes' and is estimated to affect tens of millions of people around the world. On their own these episodes may not cause immediate damage, but they can be indicative of a more serious problem; the condition is one of the leading causes of strokes and comes with an increased risk of heart failure and dementia. The problem with these episodes is that a patient doesn't have access to their cardiologist during the event.

The Apple Watch is 97% accurate in detecting the most common abnormal heart rhythm, according to the findings of a new study conducted by researchers at the University of California, San Francisco, whose goal was to determine if the wearable could one day aid in stroke prevention. The UCSF Health eHeart study, which was conducted in large part via the Cardiogram app for iOS, examined 6,158 participants -- all of whom had normal echocardiogram (EKG) readings, with the exception of 200 individuals who had previously been diagnosed with paroxysmal atrial fibrillation. By implementing a self-developed, AI-based algorithm, researchers and biomedical engineers were then able to train a deep neural network to identify these abnormal heart rhythms utilizing data collected via the Apple Watch's heart rate monitor. While the overall eHeart study is more far-reaching, and examines a number of variables including heart rate, blood pressure, behavior, diet, genetics and more, the inherent portion of the Cardiogram-based inquiry was initiated back in 2016, with the primary intent of discovering whether or not the Apple Watch could detect an oncoming stroke. According to Cardiogram's co-founder and leading data scientist, Brandon Ballinger, approximately 25% of all strokes are caused by an abnormal heart rhythm such as the more commonly occurring abnormality, Atrial Fibrillation.

According to a study conducted through heartbeat measurement app Cardiogram and the University of California San Francisco, the Apple Watch is 97 percent accurate in detecting the most common abnormal heart rhythm, when paired with an AI-based algorithm. The study involved 6,158 participants recruited through the Cardiogram app on Apple Watch. Most of the participants in the UCSF Health eHeart study had normal EKG readings. However, 200 of them had been diagnosed with paroxysmal atrial fibrillation (an abnormal heartbeat). Engineers then trained a deep neural network to identify these abnormal heart rhythms from Apple Watch heart rate data.