The risk scores relied upon by doctors to identify who is at risk for complications are only accurate in about 60% of cases. The team analyzed preoperative ECG data from 37,000 patients who had surgery at Beth Israel Deaconess Medical Center in Boston. The team trained two AI models to identify patients likely to have a heart attack, a stroke, or die within 30 days after their surgery. The ECG-only model predicted complications better than current risk scores, but the fusion model was even better, able to predict which patients would suffer post-surgical complications with 85% accuracy. The team would also like to determine what other information might be extracted from ECG results through AI.