Artificial intelligence applied to electrocardiograms, echocardiography, cardiac MRI, and nuclear imaging shows potential for early prediction and diagnosis of cancer therapy-related cardiotoxicity.
Does artificial intelligence applied to cardiac imaging and ECG improve the prediction and diagnosis of cancer therapy-related cardiac dysfunction in oncology patients?
Artificial intelligence applied to multimodality cardiac imaging and ECG holds promise for the early prediction and diagnosis of cancer therapy-related cardiac dysfunction.
Cardiovascular diseases and cancer are the leading causes of morbidity and mortality in modern society. Expanding cancer therapies that have improved prognosis may also be associated with cardiotoxicity, and extended life span after survivorship is associated with the increasing prevalence of cardiovascular disease. As such, the field of cardio-oncology has been rapidly expanding, with an aim to identify cardiotoxicity and cardiac disease early in a patient who is receiving treatment for cancer or is in survivorship. Artificial intelligence is revolutionizing modern medicine with its ability to identify cardiac disease early. This article comprehensively reviews applications of artificial intelligence specifically applied to electrocardiograms, echocardiography, cardiac magnetic resonance imaging, and nuclear imaging to predict cardiac toxicity in the setting of cancer therapies, with a view to reduce early complications and cardiac side effects from cancer therapies such as chemotherapy, radiation therapy, or immunotherapy.
Scalia et al. (2025) conducted a review in Cancer therapy-related cardiac dysfunction. Artificial intelligence applications was evaluated. Artificial intelligence applied to electrocardiograms, echocardiography, cardiac MRI, and nuclear imaging shows potential for early prediction and diagnosis of cancer therapy-related cardiotoxicity.