Using AI and Retinal Scans to Predict Stroke Risk: A Non-Invasive Leap Forward

By Campion Quinn, MD

Introduction

Artificial intelligence (AI) is transforming medicine, offering tools that provide earlier, more accurate predictions of diseases. Among these advances is the ability of AI to analyze retinal scans to predict stroke risk. Researchers have identified specific patterns in retinal blood vessels—often called “vascular fingerprints”—that correlate with the likelihood of a stroke. This essay explores the integration of AI in retinal imaging, its clinical implications, and actionable takeaways for physicians considering AI tools in practice.

How AI Retinal Scans Work

AI algorithms are trained to analyze detailed retina images captured through fundus photography or optical coherence tomography (OCT). These algorithms assess retinal vessel features, including diameter, tortuosity, and branching density, extracting parameters linked to stroke risk. In the UK Biobank study, researchers utilized a retina-based microvascular health assessment system to analyze 118 retinal parameters. Of these, 29 were significantly associated with stroke risk, such as arterial bifurcation density and arterial length-diameter ratio.

By analyzing subtle changes in these features, AI identifies risk factors that might go unnoticed in traditional assessments. For instance, a one-standard deviation decrease in arterial bifurcation density was associated with a 10.7% increase in stroke risk. This demonstrates the potential of retinal scans as a noninvasive and accessible screening tool.

Clinical Implications

Impact on Patient Care

The ability to assess stroke risk through retinal scans has far-reaching implications for patient care. This non-invasive and painless approach makes it an attractive option for patients who may avoid traditional screening methods. Early detection through AI-enabled retinal analysis allows for timely interventions, such as lifestyle modifications or medical therapy, reducing the risk of stroke and its associated morbidity.

For example, consider a 55-year-old patient with hypertension and diabetes. A routine retinal scan reveals significant vascular abnormalities. Based on the AI’s analysis, the patient’s stroke risk is elevated, prompting the physician to intensify blood pressure control and recommend aspirin therapy. Such proactive measures can significantly lower the likelihood of a stroke.

Administrative Efficiency

AI-driven retinal analysis also streamlines administrative processes. Traditional stroke risk assessments often require multiple tests and specialist consultations, increasing healthcare costs and logistical challenges. Retinal scans, however, can be performed in primary care settings and analyzed rapidly using AI. This reduces the need for extensive follow-ups and improves resource allocation within healthcare systems.

Research and Innovation

Beyond immediate clinical applications, AI retinal scans open new avenues for research. The discovery of novel retinal parameters linked to stroke risk deepens our understanding of cerebrovascular pathophysiology. These findings could guide the development of new preventive and therapeutic strategies, bridging the gap between ophthalmology and neurology.

Limitations and Challenges

Despite its promise, AI retinal analysis has limitations. The technology is not yet definitive in predicting stroke risk and must be integrated with traditional clinical factors. While the area under the receiver operating characteristic (ROC) curve improved from 0.739 to 0.752 with the addition of retinal parameters, this modest increase highlights the need for further validation.

Another challenge lies in the accessibility of advanced imaging systems in low-resource settings. Although retinal scans are more accessible than other diagnostic tools, ensuring equitable distribution remains a priority. Moreover, the ethical considerations of using AI, including data privacy and algorithmic bias, must be addressed to build trust among patients and clinicians.

Actionable Takeaways for Physicians

1. Incorporate Retinal Scans in Routine Practice: Primary care or ophthalmology physicians can incorporate retinal imaging into regular health check-ups, particularly for patients at higher stroke risk.

2. Combine AI Insights with Traditional Assessments: Use AI predictions as an adjunct to conventional risk factors like hypertension and diabetes.

3. Educate Patients About Non-Invasive Screening: To encourage participation in preventive care, emphasize the simplicity and benefits of retinal scans.

4. Stay Updated on AI Advancements: Engage with professional development opportunities to understand AI tools' evolving capabilities and limitations.

Conclusion

AI-enabled retinal scans represent a paradigm shift in stroke risk assessment, offering a non-invasive, efficient, and potentially transformative approach to prevention. While challenges remain, ongoing research and technological advancements will likely refine these tools, making them indispensable in clinical practice. By embracing this innovation, physicians can enhance patient outcomes, streamline care delivery, and contribute to the broader integration of AI in medicine.

References

1. Yusufu, M., Friedman, D. S., Kang, M., et al. (2025). Retinal vascular fingerprints predict incident stroke: findings from the UK Biobank cohort study. Heart.

2. Fayad, P., University of Nebraska Medical Center. Comments on retinal vascular analysis for stroke risk prediction. Medscape Medical News. January 24, 2025