AI-ENABLED RISK ASSESSMENT OF ADVERSE HEALTH OUTCOME

Smart overview of the Invention

Systems and methods are presented for evaluating an individual's risk of near-term cardiovascular events through an artificial intelligence system (AIS). Non-invasive chest CT scan images serve as the primary input for the AIS, which analyzes various factors including coronary artery calcification (CAC) scores, plaque characteristics, cardiac chamber volumes, and ejection fraction (EF). The analysis combines these elements with additional risk factors to generate risk estimates, which can be automated through a digital application.

Importance of Early Detection

Cardiovascular disease (CVD) is a leading cause of death globally, with many individuals unaware of their risk until it is too late. Early identification of individuals at high risk for atherosclerotic cardiovascular disease (ASCVD) is essential, particularly for those who are asymptomatic. The CAC score has proven to be a significant predictor of serious coronary events and enhances risk prediction when combined with traditional risk factors.

Limitations of Current Risk Assessment Tools

While existing tools like the CAC scan are effective, they are not infallible. Research indicates that even individuals with a CAC score of zero can experience adverse coronary events. Current long-term risk prediction methods do not adequately prompt immediate preventive measures or identify asymptomatic patients who may be susceptible to near-term events. A more immediate predictive tool could lead to timely interventions and improved patient outcomes.

Challenges in Current Screening Techniques

Current screening methods for conditions like atrial fibrillation (AF) and heart failure (HF) primarily focus on long-term risk assessment rather than immediate threats. Existing tools such as CHARGE-AF and brain natriuretic peptide (BNP) lack the precision needed for individual assessments. Moreover, manual measurements in imaging techniques can be time-consuming and prone to variability, highlighting the need for more efficient and accurate methods.

Addressing Osteoporosis and Bone Health

The patent also touches on the challenges in diagnosing osteoporosis and bone mineral density (BMD) issues, which affect a significant portion of older adults. Current BMD testing methods such as DEXA have limitations that can lead to underdiagnosis. There is a pressing need for improved detection and treatment strategies to prevent fractures related to osteoporosis, emphasizing the importance of early intervention in overall health management.