A team of researchers at Stanford University, led by Andrew Ng, a prominent AI researcher and an adjunct professor there, has shown that a machine-learning model can identify heart arrhythmias from an electrocardiogram (ECG) better than an expert.
The automated approach could prove important to everyday medical treatment by making the diagnosis of potentially deadly heartbeat irregularities more reliable. It could also make quality care more readily available in areas where resources are scarce.
The work is also just the latest sign of how machine learning seems likely to revolutionize medicine. In recent years, researchers have shown that machine-learning techniques can be used to spot all sorts of ailments, including, for example, breast cancer, skin cancer, and eye disease from medical images.
“I’ve been encouraged by how quickly people are accepting the idea that deep learning can diagnose at an accuracy superior to doctors in select verticals,” Ng said via e-mail. He adds that it’s encouraging to see researchers looking beyond imaging to other forms of data such as ECG.
Until recently, Ng was the chief scientist at the Chinese tech giant Baidu, where he helped found an institute dedicated to applying deep learning to different business problems.
The Stanford team trained a deep-learning algorithm to identify different types of irregular heartbeats in ECG data. Some irregularities can lead to serious health complications including sudden cardiac death, but the signal can be difficult to detect, so patients are often asked to wear an ECG sensor for several weeks. Even then it can be difficult for a doctor to distinguish between irregularities that may be benign and ones that could require treatment.