Purpose: The relationship between retinal morphology, as assessed by optical coherence tomography (OCT), and retinal function in microperimetry (MP) has not been well studied, despite its increasing importance as an essential functional endpoint for clinical trials and emerging therapies in retinal diseases. Normative databases of healthy ageing eyes are largely missing from literature.
Methods: Healthy subjects above 50 years were examined using two MP devices, MP-3 (NIDEK) and MAIA (iCare). An identical grid, encompassing 45 stimuli was used for retinal sensitivity (RS) assessment. Deep-learning-based algorithms performed automated segmentation of ellipsoid zone (EZ), outer nuclear layer (ONL), ganglion cell layer (GCL) and retinal nerve fibre layer (RNFL) from OCT volumes (Spectralis, Heidelberg). Pointwise co-registration between each MP stimulus and corresponding location on OCT was performed via registration algorithm. Effect of age, eccentricity and layer thickness on RS was assessed by mixed effect models.
Results: Three thousand six hundred stimuli from twenty eyes of twenty patients were included. Mean patient age was 68.9 ± 10.9 years. Mean RS for the first exam was 28.65 ± 2.49 dB and 25.5 ± 2.81 dB for MP-3 and MAIA, respectively. Increased EZ thickness, ONL thickness and GCL thickness were significantly correlated with increased RS (all p < 0.001). Univariate models showed lower RS with advanced age and higher eccentricity (both p < 0.05).
Conclusion: This work provides reference values for healthy age-related EZ and ONL-thicknesses without impairment of visual function and evidence for RS decrease with eccentricity and increasing age. This information is crucial for interpretation of future clinical trials in disease.
Keywords: Microperimetry; artificial intelligence; deep‐learning; ellipsoid zone; optical coherence tomography.
© 2024 The Author(s). Acta Ophthalmologica published by John Wiley & Sons Ltd on behalf of Acta Ophthalmologica Scandinavica Foundation.