Topographic and quantitative correlation of structure and function using deep learning in subclinical biomarkers of intermediate age-related macular degeneration
Klaudia Birner, Gregor S. Reiter, Irene Steiner, Gábor Deák, Hamza Mohamed, Simon Schürer‐Waldheim, Markus Gumpinger, Hrvoje Bogunović, Ursula Schmidt‐Erfurth
Abstract
To examine the morphological impact of deep learning (DL)-quantified biomarkers on point-wise sensitivity (PWS) using microperimetry (MP) and optical coherence tomography (OCT) in intermediate AMD (iAMD). Patients with iAMD were examined by OCT (Spectralis). DL-based algorithms quantified ellipsoid zone (EZ)-thickness, hyperreflective foci (HRF) and drusen volume. Outer nuclear layer (ONL)-thickness and subretinal drusenoid deposits (SDD) were quantified by human experts. All patients completed four MP examinations using an identical custom 45 stimuli grid on MP-3 (NIDEK) and MAIA (CenterVue). MP stimuli were co-registered with corresponding OCT using image registration algorithms. Multivariable mixed-effect models were calculated. 3.600 PWS from 20 eyes of 20 patients were analyzed. Decreased EZ thickness, decreased ONL thickness, increased HRF and increased drusen volume had a significant negative effect on PWS (all p < 0.001) with significant interaction with eccentricity (p < 0.001). Mean PWS was 26.25 ± 3.43 dB on MP3 and 22.63 ± 3.69 dB on MAIA. Univariate analyses revealed a negative association of PWS and SDD (p < 0.001). Subclinical changes in EZ integrity, HRF and drusen volume are quantifiable structural biomarkers associated with reduced retinal function. Topographic co-registration between structure on OCT volumes and sensitivity in MP broadens the understanding of pathognomonic biomarkers with potential for evaluation of quantifiable functional endpoints.