As clinicians face the challenge of detecting systemic diseases before irreversible damage occurs, oculomics is emerging as a non-invasive window into cardiovascular, neurodegenerative, and oncologic pathology.
Recent studies have explored the potential of eye scans to detect systemic diseases by identifying specific ocular biomarkers linked to conditions such as heart disease, dementia, and cancer. However, while these findings are promising, the clinical validity of ocular biomarkers for systemic disease detection is still under investigation, and widespread clinical application requires further validation.
These advances in ocular imaging technologies, as noted in the earlier report, have enabled high-resolution visualization of retinal microvasculature and choroidal structures, revealing patterns of vessel tortuosity, microaneurysms and pigment alterations that correlate with systemic risk factors.
Building on these imaging platforms, artificial intelligence (AI) in eye diagnostics now enables pixel-level analysis of vascular patterns and neural layers. For instance, a systematic review highlighted that AI models have demonstrated effectiveness in identifying retinal image features associated with various systemic diseases, including cardiovascular and neurodegenerative conditions. However, the performance varied for different conditions, indicating that while AI enhances diagnostic capabilities, its effectiveness depends on the specific disease and imaging parameters.
Leveraging non-invasive eye testing allows point-of-care screening in both primary and ophthalmic settings, minimizing patient risk while offering a repeatable method for longitudinal monitoring of disease progression. However, it is important to note that while this approach shows promise, it is still under investigation and has not yet been endorsed by established clinical practice guidelines.
When combined into multimodal imaging workflows, structural OCT, OCT angiography and fundus autofluorescence converge to provide a panoramic view of ocular and systemic health, as highlighted in the earlier report, supporting more nuanced risk stratification and tailored referral decisions.
In one illustrative case, a 62-year-old patient with no cardiovascular symptoms underwent a routine OCT angiography that revealed capillary rarefaction and flow voids. Prompt cardiology referral uncovered subclinical coronary artery disease, enabling early initiation of statin therapy and lifestyle modification that likely altered the patient’s disease trajectory.
As these technologies become more integrated across ophthalmology and primary care settings, they may redefine screening protocols and referral patterns. Ophthalmologists and general practitioners should prepare for collaborative workflows that harness non-invasive eye scanning and AI-driven analysis to identify early markers of systemic disease.
Key Takeaways:- Oculomics provides innovative, non-invasive methods to detect systemic diseases early through eye scans.
- Advanced ocular imaging technologies are crucial for comprehensive disease prediction and monitoring.
- AI in eye diagnostics significantly improves the accuracy and efficiency of systemic disease detection.
- The incorporation of multimodal imaging allows for detailed assessments of both ocular and systemic health.