Revolutionizing Cardiovascular Risk Assessment with AI

A recent position paper in the Asia-Pacific Journal of Ophthalmology explores the transformative potential of artificial intelligence (AI) in ophthalmology. Led by Lama Al-Aswad, Professor of Ophthalmology and Irene Heinz Given and John La Porte Given Research Professor of Ophthalmology II, of the Scheie Eye Institute, the work represents a collaboration among researchers from Penn Engineering, Penn Medicine, the University of Michigan Kellogg Eye Center, St. John Eye Hospital in Jerusalem, and Gyeongsang National University College of Medicine in Korea.

With fundus photography enabling the visualization of retina at the back of the eye, the potential of AI in providing systemic disease biomarkers is becoming a reality. When fundus images are of sufficient quantity and quality, it becomes possible to train AI systems to detect elevated HbA1c levels - an important marker for high blood sugar that is traditionally obtained with blood draws, which indicates a heightened risk of diabetes and cardiovascular disease. This process leverages the emerging field of oculomics, which studies ophthalmic biomarkers to gain insights into systemic health.

In their manuscript, titled "Development of Oculomics Artificial Intelligence for Cardiovascular Risk Factors: A Case Study in Fundus Oculomics for HbA1c Assessment and Clinically Relevant Considerations for Clinicians," this multi-institutional team explores the potential of oculomics and highlights pertinent topics for clinicians to consider as we move into an era where artificial intelligence has the potential to enhance systemic health through eye care.

Their discussion is supported by preliminary research results from a pilot study that trained AI models to predict HbA1c levels based on fundus images. This study evaluated various factors - such as AI model size and architecture, the presence of diabetes, and patient demographics (age and sex) - and their impact on AI performance.

One of the study observations was that biased training samples for an oculomics model, such as a pool of predominantly older patients, can degrade model performance. The results of the case study highlight the importance of developing trustworthy AI models for assessing cardiovascular risk factors while addressing the challenges and problems that must be overcome prior to clinical adoption, as well as advancing reliable oculomics technology.

"By leveraging AI to analyze retinal images for cardiovascular risk assessment," says Al-Aswad, "we aim to bridge a crucial gap in early disease detection. This method not only enhances our ability to identify at-risk individuals but also holds promise for transforming how we manage chronic conditions such as diabetes. By focusing on practical applications of this technology, we are advancing towards more personalized and preventative healthcare solutions."

"While these advancements hold promise, it is also of utmost importance for clinicians and researchers to develop and employ these techniques in a responsible manner, as this will benefit patient care the most in the end," adds Kuk Jin Jang, a postdoctoral researcher in the Penn Research in Embedded Computing and Integrated Systems Engineering (PRECISE) Center at the University of Pennsylvania.

"Our collaboration serves to further understand how we can responsibly leverage this revolutionary technology to benefit patients in the future. It is a testament to the collaborative advances formed when healthcare and engineering come together to work towards responsible AI for patient care," says Joshua Ong, a resident physician at the University of Michigan and PRECISE Center affiliate. "I am incredibly grateful for our multidisciplinary team for coming together to bring this paper and topic to the forefront."

"This collaboration reflects a deep commitment to advancing healthcare through innovative AI applications," adds PRECISE Center Director Insup Lee, Cecilia Fitler Moore Professor in Computer and Information Science at Penn Engineering. "By combining our expertise, we are paving the way for significant improvements in patient care and the overall management of long-term health challenges."

Ong J, Jang KJ, Baek SJ, Hu D, Lin V, Jang S, Thaler A, Sabbagh N, Saeed A, Kwon M, Kim JH, Lee S, Han YS, Zhao M, Sokolsky O, Lee I, Al-Aswad LA.
Development of oculomics artificial intelligence for cardiovascular risk factors: A case study in fundus oculomics for HbA1c assessment and clinically relevant considerations for clinicians.
Asia Pac J Ophthalmol (Phila). 2024 Jul-Aug;13(4):100095. doi: 10.1016/j.apjo.2024.100095

Most Popular Now

AI-Powered CRISPR could Lead to Faster G…

Stanford Medicine researchers have developed an artificial intelligence (AI) tool to help scientists better plan gene-editing experiments. The technology, CRISPR-GPT, acts as a gene-editing “copilot” supported by AI to help...

Groundbreaking AI Aims to Speed Lifesavi…

To solve a problem, we have to see it clearly. Whether it’s an infection by a novel virus or memory-stealing plaques forming in the brains of Alzheimer’s patients, visualizing disease processes...

AI Spots Hidden Signs of Depression in S…

Depression is one of the most common mental health challenges, but its early signs are often overlooked. It is often linked to reduced facial expressivity. However, whether mild depression or...

AI Model Forecasts Disease Risk Decades …

Imagine a future where your medical history could help predict what health conditions you might face in the next two decades. Researchers have developed a generative AI model that uses...

AI Tools Help Predict Severe Asthma Risk…

Mayo Clinic researchers have developed artificial intelligence (AI) tools that help identify which children with asthma face the highest risk of serious asthma exacerbation and acute respiratory infections. The study...

AI Model Indicates Four out of Ten Breas…

A project at Lund University in Sweden has trained an AI model to identify breast cancer patients who could be spared from axillary surgery. The model analyses previously unutilised information...

Smart Device Uses AI and Bioelectronics …

As a wound heals, it goes through several stages: clotting to stop bleeding, immune system response, scabbing, and scarring. A wearable device called "a-Heal," designed by engineers at the University...

AI Distinguishes Glioblastoma from Look-…

A Harvard Medical School–led research team has developed an AI tool that can reliably tell apart two look-alike cancers found in the brain but with different origins, behaviors, and treatments. The...

ChatGPT 4o Therapeutic Chatbot 'Ama…

One of the first randomized controlled trials assessing the effectiveness of a large language model (LLM) chatbot 'Amanda' for relationship support shows that a single session of chatbot therapy...

Overcoming the AI Applicability Crisis a…

Opinion Article by Harry Lykostratis, Chief Executive, Open Medical. The government’s 10 Year Health Plan makes a lot of the potential of AI-software to support clinical decision making, improve productivity, and...

Dartford and Gravesham Implements Clinis…

Dartford and Gravesham NHS Trust has taken a significant step towards a more digital future by rolling out electronic test ordering using Clinisys ICE. The trust deployed the order communications...