Abstract
Spatially-resolved mapping of rod- and cone-function may facilitate monitoring of macular diseases and serve as a functional outcome parameter. However, mesopic and dark-adapted two-color fundus-controlled perimetry (FCP, also called “microperimetry”) constitute laborious examinations. We have devised a machine-learning-based approach to predict mesopic and dark-adapted (DA) retinal sensitivity in eyes with neovascular age-related macular degeneration (nAMD). Extensive psychophysical testing and volumetric multimodal retinal imaging data were acquired including mesopic, DA red and DA cyan FCP, spectral-domain optical coherence tomography and confocal scanning laser ophthalmoscopy infrared reflectance and fundus autofluorescence imaging. With patient-wise leave-one-out cross-validation, we have been able to achieve prediction accuracies of (mean absolute error, MAE [95% CI]) 3.94 dB [3.38, 4.5] for mesopic, 4.93 dB [4.59, 5.27] for DA cyan and 4.02 dB [3.63, 4.42] for DA red testing. Partial addition of patient-specific sensitivity data decreased the cross-validated MAE to 2.8 dB [2.51, 3.09], 3.71 dB [3.46, 3.96], and 2.85 dB [2.62, 3.08]. The most important predictive feature was outer nuclear layer thickness. This artificial intelligence-based analysis strategy, termed “inferred sensitivity”, herein, enables to estimate differential effects of retinal structural abnormalities on cone- and rod-function in nAMD, and may be used as quasi-functional surrogate endpoint in future clinical trials.
Highlights
Age-related macular degeneration (AMD) is the most common cause for severe visual loss in industrialized countries[1]
Demonstrating therapeutic benefits of emerging combined treatment approaches tackling diferent pathways simultaneously constitutes a challenge, especially given that visual outcomes in patients with neovascular AMD were markedly improved with the introduction of anti-VEGF therapy
The present study outlines the possibility to predict retinal function, when (a) volumetric, multimodal retinal imaging data is obtained only or (b) a short FCP exam is performed. For this artificial intelligence (AI)-based analysis strategy, we have introduced the term “inferred sensitivity” that may serve as a functional surrogate endpoint in future clinical trials
Summary
Age-related macular degeneration (AMD) is the most common cause for severe visual loss in industrialized countries[1]. It has limited accuracy with regard to subtle therapeutic effects, as it only measures photopic function at central retinal fixation and exhibits considerable retest-variability[4,5]. In this regard, fundus-controlled perimetry (FCP, ‘microperimetry’) offers information over and beyond BCVA. For example in neovascular AMD, a decrease in central (full) retinal thickness could represent both, positive (e.g. reduction of macular edema) or negative (e.g. outer retinal atrophy) treatment effects It has recently been demonstrated, that artificial intelligence (AI) algorithms, including machine learning techniques such as random forest regression, may be applied in neovascular AMD to predict future BCVA based on previous BCVA and structural SD-OCT data[16]. We introduce the term “inferred sensitivity” to describe the spatially-resolved prediction of retinal sensitivity based on clinically feasible multimodal retinal imaging and with subsequent application of AI algorithms
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