Cardiovascular diseases (CVD) continue to be a leading cause of death worldwide, often diagnosed too late to prevent severe outcomes. Traditional methods of risk assessment, while useful, often miss the mark, leaving many at-risk individuals undetected. But what if we could harness the power of artificial intelligence (AI) and genomics to revolutionize our approach to CVD risk assessment? Dr. Luca Saba’s recent narrative review published in Reviews Cardiovascular Medicine explores a groundbreaking model that does just that.
The Power of Radiomics and Genomics
Dr. Saba’s review highlights the integration of genomic-based biomarkers (GBBM) and radiomic-based biomarkers (RBBM) to create a more accurate, non-invasive method for assessing CVD risk. Genomic biomarkers, found in plasma and serum samples, provide invaluable information about an individual’s genetic predisposition to cardiovascular conditions. Meanwhile, radiomic biomarkers—like plaque area and plaque burden measured through advanced imaging techniques—offer a visual and quantifiable glimpse into the physical manifestations of CVD.
The exciting part? These biomarkers are not just valuable in isolation. Dr. Saba proposes that they have a strong correlation and, when combined, can offer an unprecedented level of accuracy in detecting the severity of CVD and stroke. This dual approach could be the key to early and precise diagnosis, enabling timely and targeted interventions.
Artificial Intelligence: The Game Changer
The review doesn’t stop at showcasing the potential of RBBM and GBBM. It introduces an innovative AI-based model known as aiP³ (preventive, precision, and personalized) for CVD and stroke risk assessment. By leveraging deep learning (DL) algorithms, this model can analyze vast amounts of data from these biomarkers to predict cardiovascular risk with a higher degree of specificity and sensitivity than ever before.
AI adds an additional layer of sophistication by addressing elements such as explainability, pruning, and bias, ensuring that the predictions are not only accurate but also understandable and fair. The aiP³ model stands as a promising advancement, taking CVD risk assessment into a future where prevention is not just possible but precisely tailored to each individual.
A New Era of Cardiovascular Care
Dr. Saba’s review selected 246 studies to validate these hypotheses, confirming the potential of integrating radiomic and genomic biomarkers within an AI framework. This amalgamation of cutting-edge technology and medical science could redefine how healthcare professionals approach CVD risk assessment. Imagine a world where your doctor can predict your cardiovascular risk with laser precision, offering personalized preventive measures that could save your life.
In summary, the integration of AI, radiomics, and genomics represents a monumental leap forward in cardiovascular care. By embracing these innovations, we move closer to a future where heart disease can be detected early and managed effectively, ultimately saving countless lives.
