Artificial intelligence for diagnosis of inherited retinal disease: an exciting opportunity and one step forward

Applications of artificial intelligence to inherited retinal diseases: A systematic review

Artificial intelligence(AI)-based methods have been extensively used for the detection and management of various common retinal conditions, but their targeted development for inherited retinal diseases (IRD) is still nascent. In the context of limited availability of retinal subspecialists, genetic testing and genetic counseling, there is a high need for accurate and accessible diagnostic methods. The currently available AI studies, aiming for detection, classification, and prediction of IRD, remain mainly retrospective and include relatively limited numbers of patients due to their scarcity. We summarize the latest findings and clinical implications of machine-learning algorithms in IRD, highlighting the achievements and challenges of AI to assist ophthalmologists in their clinical practice.

Deep learning aided measurement of outer retinal layer metrics as biomarkers for inherited retinal degenerations: opportunities and challenges

PURPOSE OF REVIEW

The purpose of this review was to provide a summary of currently available retinal imaging and visual function testing methods for assessing inherited retinal degenerations (IRDs), with the emphasis on the application of deep learning (DL) approaches to assist the determination of structural biomarkers for IRDs.

RECENT FINDINGS

(clinical trials for IRDs; discover effective biomarkers as endpoints; DL applications in processing retinal images to detect disease-related structural changes).

SUMMARY

Assessing photoreceptor loss is a direct way to evaluate IRDs. Outer retinal layer structures, including outer nuclear layer, ellipsoid zone, photoreceptor outer segment, RPE, are potential structural biomarkers for IRDs. More work may be needed on structure and function relationship.

Phenotyping and genotyping inherited retinal diseases: Molecular genetics, clinical and imaging features, and therapeutics of macular dystrophies, cone and cone-rod dystrophies, rod-cone dystrophies, Leber congenital amaurosis, and cone dysfunction syndromes

Inherited retinal diseases (IRD) are a leading cause of blindness in the working age population and in children. The scope of this review is to familiarise clinicians and scientists with the current landscape of molecular genetics, clinical phenotype, retinal imaging and therapeutic prospects/completed trials in IRD. Herein we present in a comprehensive and concise manner: (i) macular dystrophies (Stargardt disease (ABCA4), X-linked retinoschisis (RS1), Best disease (BEST1), PRPH2-associated pattern dystrophy, Sorsby fundus dystrophy (TIMP3), and autosomal dominant drusen (EFEMP1)), (ii) cone and cone-rod dystrophies (GUCA1A, PRPH2, ABCA4, KCNV2 and RPGR), (iii) predominant rod or rod-cone dystrophies (retinitis pigmentosa, enhanced S-Cone syndrome (NR2E3), Bietti crystalline corneoretinal dystrophy (CYP4V2)), (iv) Leber congenital amaurosis/early-onset severe retinal dystrophy (GUCY2D, CEP290, CRB1, RDH12, RPE65, TULP1, AIPL1 and NMNAT1), (v) cone dysfunction syndromes (achromatopsia (CNGA3, CNGB3, PDE6C, PDE6H, GNAT2, ATF6), X-linked cone dysfunction with myopia and dichromacy (Bornholm Eye disease; OPN1LW/OPN1MW array), oligocone trichromacy, and blue-cone monochromatism (OPN1LW/OPN1MW array)). Whilst we use the aforementioned classical phenotypic groupings, a key feature of IRD is that it is characterised by tremendous heterogeneity and variable expressivity, with several of the above genes associated with a range of phenotypes.

Metabolomics facilitates differential diagnosis in common inherited retinal degenerations by exploring their profiles of serum metabolites

The diagnosis of inherited retinal degeneration (IRD) is challenging owing to its phenotypic and genotypic complexity. Clinical information is important before a genetic diagnosis is made. Metabolomics studies the entire picture of bioproducts, which are determined using genetic codes and biological reactions. We demonstrated that the common diagnoses of IRD, including retinitis pigmentosa (RP), cone-rod dystrophy (CRD), Stargardt disease (STGD), and Bietti's crystalline dystrophy (BCD), could be differentiated based on their metabolite heatmaps. Hundreds of metabolites were identified in the volcano plot compared with that of the control group in every IRD except BCD, considered as potential diagnosing markers. The phenotypes of CRD and STGD overlapped but could be differentiated by their metabolomic features with the assistance of a machine learning model with 100% accuracy. Moreover, EYS-, USH2A-associated, and other RP, sharing considerable similar characteristics in clinical findings, could also be diagnosed using the machine learning model with 85.7% accuracy. Further study would be needed to validate the results in an external dataset. By incorporating mass spectrometry and machine learning, a metabolomics-based diagnostic workflow for the clinical and molecular diagnoses of IRD was proposed in our study.

Genetic Testing Experiences of People Living with Inherited Retinal Degenerations: Results of a Global Survey

INTRODUCTION

Obtaining a genetic diagnosis via genetic testing (GT) is a fundamental step in determining the eligibility of a patient to be enrolled in emerging clinical trials and research studies. Besides, the knowledge of genetic outcome allows patients to plan for significant life choices. However, critical barriers exist to an equitable access to genetic services globally. The objective of this study was to explore patient experiences while seeking genomic services for inherited retinal degenerations (IRDs).

METHODS

An online survey was designed based on a focus group conducted by Retina International and including people affected by IRDs and their families living in different regions around the world. The survey was then circulated to 43 Retina International member organisations globally via email newsletters and social networks. The survey involved questions in relation to the accessibility, affordability, and timeliness of genomic services for IRDs as well as patient perceived awareness of genomic services for IRDs among healthcare professionals.

RESULTS

A total of 410 respondents (IRD patients and caregivers) from over 30 countries across all continents responded to the survey. A considerable number of the patients had to go through a long and arduous journey to access GT and counselling services, wherein 40% had to visit more than 5 physicians, 27% had to visit more than 5 clinics, and 57% had to wait for more than 3 years before obtaining a genetic diagnosis. Furthermore, 46% of respondents reported not receiving genetic counselling prior to undergoing GT, and 39% reported not receiving genetic counselling after undergoing GT. Over 3/4th of the participants reported that they did not have to pay for their genomic services for IRDs. Thirty-seven percent of the respondents reported that their eye care professionals (ECPs) were either not aware of GT, remained neutral, or did not encourage them to undergo GT.

CONCLUSION

Patients with IRDs do not have equitable access to best practice GT and counselling services. Greater awareness and training regarding IRDs and the benefits of GT and genetic counselling for patients and families are needed among ECPs. A best practice model on access to genomic services for IRDs is required.

CRB1-Associated Retinal Dystrophies: Genetics, Clinical Characteristics, and Natural History

PURPOSE

To analyze the clinical characteristics, natural history, and genetics of CRB1-associated retinal dystrophies.

DESIGN

Multicenter international retrospective cohort study.

METHODS

Review of clinical notes, ophthalmic images, and genetic testing results of 104 patients (91 probands) with disease-causing CRB1 variants. Macular optical coherence tomography (OCT) parameters, visual function, fundus characteristics, and associations between variables were the main outcome measures.

RESULTS

The mean age of the cohort at the first visit was 19.8 ± 16.1 (median 15) years, with a mean follow-up of 9.6 ± 10 years. Based on history, imaging, and clinical examination, 26 individuals were diagnosed with retinitis pigmentosa (RP; 25%), 54 with early-onset severe retinal dystrophy / Leber congenital amaurosis (EOSRD/LCA; 52%), and 24 with macular dystrophy (MD; 23%). Severe visual impairment was most frequent after 40 years of age for patients with RP and after 20 years of age for EOSRD/LCA. Longitudinal analysis revealed a significant difference between baseline and follow-up best-corrected visual acuity in the 3 subcohorts. Macular thickness decreased in most patients with EOSRD/LCA and MD, whereas the majority of patients with RP had increased perifoveal thickness.

CONCLUSIONS

A subset of individuals with CRB1 variants present with mild, adult-onset RP. EOSRD/LCA phenotype was significantly associated with null variants, and 167_169 deletion was exclusively present in the MD cohort. The poor OCT lamination may have a degenerative component, as well as being congenital. Disease symmetry and reasonable window for intervention highlight CRB1 retinal dystrophies as a promising target for trials of novel therapeutics.

Gene-Based Therapeutics for Acquired Retinal Disease: Opportunities and Progress

Acquired retinal diseases such as age-related macular degeneration and diabetic retinopathy rank among the leading causes of blindness and visual loss worldwide. Effective treatments for these conditions are available, but often have a high treatment burden, and poor compliance can lead to disappointing real-world outcomes. Development of new treatment strategies that provide more durable treatment effects could help to address some of these unmet needs. Gene-based therapeutics, pioneered for the treatment of monogenic inherited retinal disease, are being actively investigated as new treatments for acquired retinal disease. There are significant advantages to the application of gene-based therapeutics in acquired retinal disease, including the presence of established therapeutic targets and common pathophysiologic pathways between diseases, the lack of genotype-specificity required, and the larger potential treatment population per therapy. Different gene-based therapeutic strategies have been attempted, including gene augmentation therapy to induce in vivo expression of therapeutic molecules, and gene editing to knock down genes encoding specific mediators in disease pathways. We highlight the opportunities and unmet clinical needs in acquired retinal disease, review the progress made thus far with current therapeutic strategies and surgical delivery techniques, and discuss limitations and future directions in the field.