Cardiovascular Disease
Understanding Cardiovascular Disease (CVD)
Cardiovascular disease (CVD) refers to a group of conditions that affect the heart and blood vessels, primarily caused by the accumulation of fatty deposits in the arteries—a process known as atherosclerosis. This condition can lead to serious complications, including heart attacks and strokes.
Globally, cardiovascular diseases (CVDs) remain the leading cause of death, responsible for approximately 20.5 million deaths in 2021. Heart attacks and strokes account for most acute cases, with ischemic heart disease as the primary contributor. While age-standardized death rates from CVDs have declined globally since 1990, the absolute number of deaths continues to rise, especially in low- and middle-income countries
Specific conditions under CVD include:
Coronary Heart Disease
Cerebrovascular Disease
Peripheral Arterial Disease
Rheumatic Heart Disease
Aortic Disease
Early detection plays a critical role in reducing mortality and improving patient outcomes, yet conventional methods may not always capture early vascular changes.
Challenges in Detecting Cardiovascular Disease
Accurate detection of CVD is complex due to the systemic nature of the disease and the need for advanced diagnostic tools. Some of the primary challenges include
-
Asymptomatic Progression
Many cardiovascular conditions remain undetected until acute events occur, delaying intervention. -
Limited Access to Advanced Diagnostics
Traditional assessments, such as angiography and cardiac MRIs, require specialized infrastructure that may not be available in all regions. -
Interpretability of Biomarkers
Identifying subtle vascular changes across large patient datasets requires advanced computational tools for consistent analysis.
Challenges in Detecting Cardiovascular Disease
Accurate detection of CVD is complex due to the systemic nature of the disease and the need for advanced diagnostic tools. Some of the primary challenges include
-
Asymptomatic Progression
Many cardiovascular conditions remain undetected until acute events occur, delaying intervention. -
Limited Access to Advanced Diagnostics
Traditional assessments, such as angiography and cardiac MRIs, require specialized infrastructure that may not be available in all regions. -
Interpretability of Biomarkers
Identifying subtle vascular changes across large patient datasets requires advanced computational tools for consistent analysis.
FH-POISE and Cardiovascular Disease Detection
Our advanced AI platform, FH-POISE, enhances cardiovascular disease detection through retinal imaging. It leverages the retina’s microvasculature as a window to systemic health, enabling non-invasive identification of cardiovascular risk factors.
How FH-POISE Supports Cardiovascular Disease Detection:
Integrated Image Insights
Analyzes retinal blood vessels to detect key indicators such as vessel caliber changes, microaneurysms, and vascular abnormalities linked to CVD.
Comprehensive Reporting
Provides detailed, AI-generated reports that give clinicians deeper insights into cardiovascular risks through retinal biomarkers.
Early and Accurate Detection
Identifies early-stage vascular changes that may indicate underlying cardiovascular conditions, facilitating timely intervention.
Advanced Biomarker Identification
Detects subtle patterns and structural changes in retinal images to reveal signs of systemic vascular health issues.
By integrating retinal imaging with deep learning algorithms, FH-POISE enables faster, more accurate detection of cardiovascular risks. This supports proactive clinical decision-making and improves patient outcomes through early intervention and personalized care.
CVD AI Work Flow
References and External Links
- Cardiovascular disease
https://www.nhs.uk/conditions/cardiovascular-disease/ - Cardiovascular diseases (CVDs)
https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) - Cardiovascular disease
https://en.wikipedia.org/wiki/Cardiovascular_disease - What to know about cardiovascular disease
https://www.medicalnewstoday.com/articles/257484 - Artificial intelligence in retinal imaging for cardiovascular disease prediction: current trends and future directions
https://pubmed.ncbi.nlm.nih.gov/35916571/ - Prediction of cardiovascular markers and diseases using retinal fundus images and deep learning: a systematic scoping review
https://academic.oup.com/ehjdh/article/5/6/660/7754720 - A foundation model for generalizable disease detection from retinal images
https://www.nature.com/articles/s41586-023-06555-x - Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning
https://www.mdpi.com/1424-8220/22/12/4310 - An Overview of Deep-Learning-Based Methods for Cardiovascular Risk Assessment with Retinal Images
https://pmc.ncbi.nlm.nih.gov/articles/PMC9818382/ - With retinal images and genetic data, researchers predict cardiovascular, metabolic, and other disease risks
https://www.broadinstitute.org/news/retinal-images-and-genetic-data-researchers-predict-cardiovascular-metabolic-and-other-disease
