The wearable revolution is helping doctors figure out what’s troubling your ticker — thanks to deep learning.

The problem: Arrhythmias, a range of conditions in which the heart beats too fast, too slow, or erratically, can cause heart attack or stroke. But they don’t necessarily happen when a doctor is listening.

The solution: Wearable devices from iRhythm constantly monitor a patient’s heartbeat and transmit the measurements to a neural network for analysis.

How it works: The iRhythm Zio AT is an electrocardiogram monitor about the size of a breath-mint box with two wings of peel-and-stick medical tape that fasten onto the skin over a patient’s heart. Electrodes in the monitor track each heartbeat while a separate wireless transmitter sends the data to iRhythm.

  • The system collects up to two weeks worth of continuous heartbeat data. If patients feel their heart begin to beat irregularly, they can push a button on the monitor to send a 90-second snippet to iRhythm’s headquarters immediately.
  • A neural network analyzes the data. Trained on readings from 53,000 iRhythm Zio wearers, it classifies 12 different patterns: 10 arrhythmias, a normal heartbeat, and a heartbeat distorted by other bodily noises.
  • An iRhythm technician reviews the neural network’s analysis and posts it to the patient’s electronic health record for physicians to see.

Status: The United States Food and Drug Administration approved iRhythm’s Zio AT in 2018, and the system is on the market. The company recently partnered with Verily and Apple to develop further products.

Behind the news: A 2019 review of 14 studies that compared AI with human clinicians found that deep learning models were roughly as good as human professionals at diagnosing signs of disease in medical imagery. The authors noted, however, that the studies tend to suffer from poor controls, inconsistent metrics for measuring success, and lack of independent validation. No comparable assessment of non-image AI diagnostics exists, but the fact that Apple is integrating arrhythmia detection into its smartwatch suggests that the field is maturing.

Why it matters: Arrhythmias occur sporadically enough that spotting them requires many days of data. “You’ll never catch one by running an electrocardiogram in the office,” according to Dr. Mauricio Arruda of Cleveland’s University Hospitals Harrington Heart & Vascular Institute. By combining long-term observations with short-turnaround assessment, AI enables cardiologists to intervene with precise, timely, and potentially life-saving treatments.

We’re thinking: Just the thought of AI saving somebody from a stroke makes our hearts skip a beat.

To learn how you can use AI to diagnose illnesses, check out Course 1 of the AI for Medicine Specialization from deeplearning.ai.

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