An AI model has proven more efficient than traditional methods at detecting signs of atrial septal defect (ASD) in electrocardiograms (ECG).
Investigators from Brigham and Women's Hospital, a founding member of the Mass General Brigham healthcare system, and the Keio University School of Medicine in Japan have developed a deep learning artificial intelligence model to screen electrocardiograms (ECGs) for signs of atrial septal defects (ASD). This condition can cause heart failure and is underreported due to a lack of symptoms before irreversible complications arise. The results of this research have been published in eClinicalMedicine.