Stanford Machine Learning Group, Digital Health Company Develop Deep Learning Algorithm | Healthcare Informatics

Photo: Rajiv Leventhal

"The Stanford Machine Learning Group, along with digital health company iRhythm Technologies, have developed a deep learning algorithm for the detection and diagnosis of cardiac arrhythmias, or abnormal heart rhythms" says Rajiv Leventhal, Managing Editor of Healthcare Informatics.

Stanford researchers say their algorithm could bring quick, accurate diagnoses of heart arrhythmias to people without ready access to cardiologists via Video by Kurt Hickman

According to officials, the algorithm is capable of expert level detection of 14 cardiac output classes, including 12 arrhythmias as well as sinus rhythm and noise from artifact. The collaboration leveraged the iRhythm data science and clinical teams’ knowledge in electrocardiogram (ECG) analysis, as well as iRhythm’s ECG data set to produce an arrhythmia detection algorithm.

Because deep learning models are dependent upon vast amounts of reliable data, the company provided an annotated data set of about 30,000 unique patients, 500 times larger than standards-based databases utilized in previous studies, officials noted. This enabled the Stanford researchers, in collaboration with iRhythm machine learning specialist Masoumeh Haghpanahi, Ph.D., to develop the 34-layer convolutional neural network, comparable to artificial intelligence (AI) models used in computer vision and speech recognition, officials stated...

The Stanford Statistical Machine Learning Group is a blend of faculty, students, and post-docs spanning AI, systems, theory, and statistics. “The group’s work spans the spectrum from answering deep, foundational questions in the theory of machine learning to building practical large-scale machine learning algorithms which are widely used in industry,” it states. 
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Source: Healthcare Informatics