Update on imaging biomarkers in uveitis

Area of Science:

• Ophthalmology
• Medical Imaging
• Inflammatory Diseases

Background:

• Uveitis diagnosis and monitoring rely heavily on imaging, but standardized, clinically useful biomarkers are lacking.
• Current imaging techniques provide valuable data but often lack disease-specific precision and validation for clinical decision-making.

Purpose of the Study:

• To assess the clinical utility of current uveitis imaging biomarkers.
• To identify imaging biomarkers that are incompletely validated.
• To explore the role of consensus frameworks and artificial intelligence in standardizing uveitis assessment.

Main Methods:

• Review of quantitative optical coherence tomography (OCT) and OCT angiography (OCTA) metrics.
• Analysis of anterior segment OCT particle-size analysis and OCTA vessel density.
• Evaluation of choroidal vascularity index and multimodal uveitis (MUV) international taskforce consensus sets.
• Assessment of artificial intelligence (AI) applications in uveitis imaging.

Main Results:

• Quantitative OCT and OCTA metrics demonstrate disease-specific precision.
• Anterior segment OCT particle-size analysis effectively differentiates inflammatory cells from pigment.
• OCTA vessel density tracks chronic changes in specific conditions like birdshot chorioretinopathy.
• The MUV taskforce established consensus imaging sets for white dot syndromes.
• AI shows proof-of-concept but requires larger datasets and prospective validation.

Conclusions:

• Most uveitis imaging biomarkers are disease-specific and require further validation.
• Quantitative imaging and international consensus frameworks are improving biomarker utility.
• The primary challenges have shifted from image acquisition to validation, standardization, and clinical outcome integration.