AI Model Screens ECG Readouts for Heart Defects with High Accuracy

Diagnosing ASD presents its challenges. Listening to the heart with a stethoscope can detect the most substantial defects, but this method only identifies about 30% of cases. An echocardiogram, though more precise, is labor and time-intensive, rendering it unsuitable for widespread screening. Electrocardiography (ECG), on the other hand, is quick and lends itself to screening but has traditionally suffered from limited sensitivity in detecting ASD. Researchers from Brigham and Women’s Hospital (Boston, MA, USA) have taken a significant step forward by developing an AI model that outperforms conventional methods in identifying ASD through ECG readouts. They trained a deep learning model on ECG data from 80,947 patients, aged over 18, who underwent both ECG and echocardiogram testing for ASD across three different hospitals in the U.S. and Japan. Among them, 857 were diagnosed with ASD.

The model was further validated using scans from a community hospital, where it displayed superior sensitivity compared to conventional methods, accurately detecting ASD 93.7% of the time, while human analysis based on known ECG abnormalities identified ASD only 80.6% of the time. These findings highlight the potential of using AI-driven ECG analysis for large-scale ASD screening, given the relative affordability and accessibility of ECG. This could help catch the condition before it evolves into irreversible heart damage. However, the researchers did acknowledge limitations; even the combination of echocardiogram and AI might miss some defects, particularly smaller ones that might not require surgical intervention.

"If we can deploy our model on a population-level ECG screening, we would be able to pick up many more of these patients before they have irreversible damage," said Shinichi Goto, MD, Ph.D., corresponding author on the paper and instructor in the Division of Cardiovascular Medicine at Brigham and Women’s Hospital.

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