AI and Machine Learning Transforming Cardiovascular Medicine

​The utilization of artificial intelligence (AI) in the medical field is not a recent development. In the 1960s, early forms of AI, such as automatic risk calculators and signal annotation, were already being used. However, advances in computing power and increased access to digital data have significantly expanded the capabilities of AI in medical test interpretation.

Since early AI models were mostly image-based, it seemed natural to apply them in radiology. They were used for the automatic detection of pneumonia on a chest X-ray, organ segmentation in magnetic resonance scans and computed tomography, and tumor detection in images acquired from various sources.

In cardiology, machine-read electrocardiograms (ECGs) have been used for many years, but earlier algorithms were limited in accuracy and predictive capability. Regardless, these systems enabled researchers to collect copious volumes of digital data, ultimately facilitating the use of machine learning (ML) for ECG analysis.

Introducing AI-enhanced ECGs (or AI-ECGs) has improved automated rhythm reading and enabled the detection of a low ejection fraction using a 10-second ECG, detection of paroxysmal AF (atrial fibrillation) from an ECG recorded during sinus rhythm, and the determination of patient age and sex. In other cardiology fields, such as echocardiography and nuclear cardiology, AI is still in the early stages of application due to the complexity of the data and the reliance on human input during data acquisition.

Sanjiv Narayan

Shop