Association of CRX genotypes and retinal phenotypes confounded by variable expressivity and electronegative electroretinogram

IMPORTANCE

A framework for understanding the phenotypic features of CRX retinopathy was established.

BACKGROUND

To perform a phenotype-genotype correlation analysis in two groups of patients with heterozygous mutations in distinct locations of the CRX gene, encoding the cone-rod homeobox.

DESIGN

Multicentre retrospective study.

PARTICIPANTS

Twenty-one Japanese patients from 14 families with a heterozygous CRX mutation.

METHODS

Retrospective data analysis.

MAIN OUTCOME MEASURES

Clinical records on CRX mutation, symptoms, best-corrected visual acuity (BCVA), visual field, fundus photography, fundus auto-fluorescence, optical coherence tomography and electroretinograms (ERGs).

RESULTS

Six different CRX heterozygous mutations were identified in the subjects. Twelve patients from 9 families shared the p.R41W mutation and 1 patient had the p.R43C mutation, both of which affect the homeobox domain of CRX. These patients often displayed adult-onset retinal dystrophy with macular degeneration. In contrast, five patients with downstream mutations (p.S204fs, p.S213fs, p.G243X and p.L299F) displayed retinal degeneration or macular degeneration with bone-spicule pigmentation. Three asymptomatic carriers with different mutations (p.R41W, p.S213fs and p.G243X) were present in both groups. Nearly all patients and carriers had an electronegative ERG in response to a bright flash under dark adaptation. There was no cross-sectional association between patients' age and BCVA, despite progressive decline in BCVA.

CONCLUSIONS AND RELEVANCE

Heterozygous mutations within or downstream of the homeobox domain in CRX relate to the difference associated retinal phenotypes, which was confounded by variable expressivity and electronegative ERGs. CRX mutations should be considered in patients with an electronegative ERG with minimal or no macular changes.

Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs

IMPORTANCE

Detection of early onset neovascular age-related macular degeneration (AMD) is critical to protecting vision.

BACKGROUND

To describe the development and validation of a deep-learning algorithm (DLA) for the detection of neovascular age-related macular degeneration.

DESIGN

Development and validation of a DLA using retrospective datasets.

PARTICIPANTS

We developed and trained the DLA using 56 113 retinal images and an additional 86 162 images from an independent dataset to externally validate the DLA. All images were non-stereoscopic and retrospectively collected.

METHODS

The internal validation dataset was derived from real-world clinical settings in China. Gold standard grading was assigned when consensus was reached by three individual ophthalmologists. The DLA classified 31 247 images as gradable and 24 866 as ungradable (poor quality or poor field definition). These ungradable images were used to create a classification model for image quality. Efficiency and diagnostic accuracy were tested using 86 162 images derived from the Melbourne Collaborative Cohort Study. Neovascular AMD and/or ungradable outcome in one or both eyes was considered referable.

MAIN OUTCOME MEASURES

Area under the receiver operating characteristic curve (AUC), sensitivity and specificity.

RESULTS

In the internal validation dataset, the AUC, sensitivity and specificity of the DLA for neovascular AMD was 0.995, 96.7%, 96.4%, respectively. Testing against the independent external dataset achieved an AUC, sensitivity and specificity of 0.967, 100% and 93.4%, respectively. More than 60% of false positive cases displayed other macular pathologies. Amongst the false negative cases (internal validation dataset only), over half (57.2%) proved to be undetected detachment of the neurosensory retina or RPE layer.

CONCLUSIONS AND RELEVANCE

This DLA shows robust performance for the detection of neovascular AMD amongst retinal images from a multi-ethnic sample and under different imaging protocols. Further research is warranted to investigate where this technology could be best utilized within screening and research settings.

Diagnostic accuracy of disorganization of the retinal inner layers in detecting macular capillary non-perfusion in diabetic retinopathy

BACKGROUND

Disorganization of the retinal inner layers (DRIL) on optical coherence tomography (OCT) is thought to represent retinal capillary non-perfusion (CNP) in eyes with diabetic retinopathy. This study was designed to evaluate the ability of DRIL to accurately predict CNP.

DESIGN

Retrospective masked reliability and diagnostic accuracy study performed in the National Institute for Health Research (NIHR) Moorfields Biomedical Research Centre, London, UK SAMPLES: Retinal images of patients with diabetic retinopathy

METHODS

The OCT images from 90 separate areas of angiographically confirmed perfused and non-perfused areas of the macula from 37 eyes of 31 patients were anonymized and coded. Two masked graders independently graded these OCT scans for the presence or absence of DRIL to determine the intergrader reliability. The diagnostic accuracy of DRIL in identifying CNP was evaluated from the results obtained.

MAIN OUTCOME MEASURES

Sensitivity and specificity of DRIL in accurately detecting CNP RESULTS: The intergrader agreement was high with a Cohen's kappa of 0.909. DRIL was present in 84.4% (38/45) of non-perfused retina and none in perfused retina (0/45). The sensitivity and specificity of DRIL in detecting angiographic evidence of CNP was 84.4% and 100%, respectively. The positive predictive value was 100% and the negative predictive value was 86.5%.

CONCLUSIONS

The presence of DRIL is a reliable predictor of areas of macular CNP. However, DRIL is not a universal finding of non-perfusion, with some cases exhibiting absence of DRIL despite angiographic CNP.