ECG artificial intelligence outperforms conventional methods in coronary artery bypass grafting

According to a new study, the use of artificial intelligence to analyze the electrocardiogram, or ECG, has improved the diagnosis of severe heart attacks. This model was able to identify cases with unusual symptoms and atypical ECG patterns and at the same time significantly reduce false positive cases.

According to the medical technology section of Tekna news media, a heart attack with ST segment elevation, which is known as STEMI, is considered a very severe heart attack. In this case, a main coronary artery is blocked and prevents blood from reaching the heart muscle. The standard of care in this situation is rapid restoration of blood flow with percutaneous coronary intervention.

Unfortunately, the delay in reaching the recommended time for this intervention remains a challenge. This problem is especially seen in hospitals and rural centers that do not have expertise in this field. A delay of more than 90 minutes in restoring the blood flow increases the death rate of patients up to three times.

The study’s lead researcher, Robert Herrmann, from AZORG Hospital in Belgium, explained that ECG interpretation based on artificial intelligence can bring the best results. According to him, this method detects real heart attacks earlier and at the same time reduces unnecessary activations of the emergency system.

He emphasized that improving the accuracy of triage at the first medical call would simplify emergency care. It also reduces stress and fatigue on clinical teams and ensures that patients who really need urgent intervention receive it without delay and in a timely manner.

In one of the first large-scale evaluations of this artificial intelligence model in the emergency room, researchers retrospectively reviewed the files of 1032 patients with suspected STEMI. These patients were the ones for whom emergency protocols to restore blood flow were activated. Data were collected from three different specialized centers between January 2020 and May 2024.

Each patient’s initial ECG was analyzed by the Queen of Hearts artificial intelligence model. This model was trained to detect acute coronary artery occlusion as well as detect STEMI-equivalent cases and distinguish them from benign cases. The results of angiography and biomarkers confirmed that among these 601 patients actually had STEMI and 431 cases were falsely diagnosed.

The artificial intelligence ECG model performed much better than the standard triage method. The AI ​​was able to identify 553 of 601 real heart attacks, while standard triage only correctly identified 427. The false positive rate for the artificial intelligence model was only 7.9%, while this rate was reported to be 41.8% for the standard triage.

The study’s senior author Timothy D. Henry of Christ Hospital in Cincinnati said the results show the potential of artificial intelligence. He noted that AI-enhanced diagnosis at the first medical call could shorten the time to treatment and reduce false alarms. This technology is very valuable for optimizing the transfer of patients from non-specialized centers.

Also, in an editorial accompanying this research, Mohammad Al Khouli, a cardiologist at the Mayo Clinic, praised the researchers for developing an operational model in one of the most complex areas of the heart. However, he cautioned that caution should be used in interpreting the model because it was originally designed to detect blocked arteries. He emphasized the need for more validation in different populations and the challenge of integrating this technology as a complement to human judgment.