Purpose

The purpose of this study was to report retinal dystrophy as a novel clinical feature and expand the ocular phenotype in patients harboring biallelic candidate FDXR variants.

Methods

Patients carrying biallelic candidate FDXR variants were identified by whole genome sequencing (WGS) as part of the National Institute for Health Research BioResource rare-disease and the UK's 100,000 Genomes Project (100KGP) with an additional case identified by exome sequencing. Retrospective clinical data were collected from the medical records. Haplotype reconstruction was performed in families harboring the same missense variant.

Results

Ten individuals from 8 unrelated families with biallelic candidate variants in FDXR were identified. In addition to bilateral optic atrophy and variable extra-ocular findings, 7 of 10 individuals manifested retinal dystrophy comprising dysfunction and degeneration of both rod and cone photoreceptors. Five of 10 subjects had sensorineural hearing loss. The previously unreported missense variant (c.1115C > A, p.(Pro372His)) was found in 5 of 8 (62.5%) study families. Haplotype reconstruction using WGS data demonstrated a likely ancestral haplotype.

Conclusions

FDXR-associated disease is a phenotypically heterogeneous disorder with retinal dystrophy being a major clinical feature observed in this cohort. In addition, we hypothesize that a number of factors are likely to drive the pathogenesis of optic atrophy, retinal degeneration, and perhaps the associated systemic manifestations.

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.