Dr Danuta Sampson

Adaptive optics flood illumination ophthalmoscopy (AO-FIO) is an established imaging tool in the investigation of retinal diseases. However, the clinical interpretation of AO-FIO images can be challenging due to varied image quality. Therefore, image quality assessment is essential before interpretation. An image assessment tool will also assist further work on improving the image quality, either during acquisition or post processing. In this paper, we describe, validate and compare two automated image quality assessment methods; the energy of Laplacian focus operator (LAPE; not commonly used but easily implemented) and convolutional neural network (CNN; effective but more complex approach). We also evaluate the effects of subject age, axial length, refractive error, fixation stability, disease status and retinal location on AO-FIO image quality. Based on analysis of 10,250 images of 50 × 50 μm size, at 41 retinal locations, from 50 subjects we demonstrate that CNN slightly outperforms LAPE in image quality assessment. CNN achieves accuracy of 89%, whereas LAPE metric achieves 73% and 80% (for a linear regression and random forest multiclass classifier methods, respectively) compared to ground truth. Furthermore, the retinal location, age and disease are factors that can influence the likelihood of poor image quality.

Purpose: Biallelic crumbs cell polarity complex component 1 (CRB1) mutations can present as Leber congenital amaurosis (LCA), retinitis pigmentosa (RP), or cystic maculopathy. This study reports a novel phenotype of asymptomatic fenestrated slit maculopathy (AFSM) and examines macular volume profile and microperimetry as clinical trial end points in CRB1-associated retinopathies. Methods: Twelve patients from nine families with CRB1 mutation were recruited. Ultra-widefield (UWF) color fundus photography and autofluorescence (AF), spectral-domain optical coherence tomography (SD-OCT), microperimetry, and adaptive optics (AO) imaging were performed. Macular volume profiles were compared with age-matched healthy controls. Genotyping was performed using APEX genotyping microarrays, targeted next-generation sequencing, and Sanger sequencing. Results: We identified one patient with LCA, five patients with RP, and four patients with macular dystrophy (MD) with biallelic CRB1 mutations. Two siblings with compound heterozygote genotype (c.[2843G>A]; [498_506del]) had AFSM characterized by localized outer retinal disruption on SD-OCT and parafoveal cone loss on AO imaging despite normal fundus appearance, visual acuity, and foveal sensitivity. UWF AF demonstrated preserved para-arteriolar retinal pigment epithelium (PPRPE) in all patients with RP. Microperimetry documented preserved central retinal function in six patients. The ratio of perifoveal-to-foveal retinal volume was greater than controls in 89% (8/9) of patients with RP or MD, whereas central subfield and total macular volume were outside normal limits in 67% (6/9). Conclusions: AO imaging was helpful in detecting parafoveal cone loss in asymptomatic patients. Macular volume profile and microperimetry parameters may have utility as CRB1 trials end points. Translational Relevance: Macular volume and sensitivity can be used as structural and functional end points for trials on CRB1-associated RP and MD.