Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of More Than 3500 Inherited Retinal Disease Patients from the United Kingdom

Author:

Woof WilliamORCID,de Guimarães Thales A. C.ORCID,Al-Khuzaei SaoudORCID,Daich Varela MalenaORCID,Sen SagnikORCID,Bagga PallaviORCID,Mendes BernardoORCID,Shah MitalORCID,Burke Paula,Parry David,Lin SiyingORCID,Naik GunjanORCID,Ghoshal BirajaORCID,Liefers BartORCID,Fu Dun JackORCID,Georgiou MichalisORCID,Nguyen QuangORCID,da Silva Alan SousaORCID,Liu YichenORCID,Fujinami-Yokokawa YuORCID,Sumodhee DayyanahORCID,Patel PraveenORCID,Furman Jennifer,Moghul IsmailORCID,Moosajee MariyaORCID,Sallum JulianaORCID,De Silva Samantha R.ORCID,Lorenz BirgitORCID,Holz FrankORCID,Fujinami KaoruORCID,Webster Andrew RORCID,Mahroo OmarORCID,Downes Susan M.ORCID,Madhusudhan SavitaORCID,Balaskas KonstantinosORCID,Michaelides MichelORCID,Pontikos NikolasORCID

Abstract

AbstractPurposeTo quantify relevant fundus autofluorescence (FAF) image features cross-sectionally and longitudinally in a large cohort of inherited retinal diseases (IRDs) patients.DesignRetrospective study of imaging data (55-degree blue-FAF on Heidelberg Spectralis) from patients.ParticipantsPatients with a clinical and molecularly confirmed diagnosis of IRD who have undergone FAF 55-degree imaging at Moorfields Eye Hospital (MEH) and the Royal Liverpool Hospital (RLH) between 2004 and 2019.MethodsFive FAF features of interest were defined: vessels, optic disc, perimacular ring of increased signal (ring), relative hypo-autofluorescence (hypo-AF) and hyper-autofluorescence (hyper-AF). Features were manually annotated by six graders in a subset of patients based on a defined grading protocol to produce segmentation masks to train an AI model, AIRDetect, which was then applied to the entire MEH imaging dataset.Main Outcome MeasuresQuantitative FAF imaging features including area in mm2and vessel metrics, were analysed cross-sectionally by gene and age, and longitudinally to determine rate of progression. AIRDetect feature segmentation and detection were validated with Dice score and precision/recall, respectively.ResultsA total of 45,749 FAF images from 3,606 IRD patients from MEH covering 170 genes were automatically segmented using AIRDetect. Model-grader Dice scores for disc, hypo-AF, hyper-AF, ring and vessels were respectively 0.86, 0.72, 0.69, 0.68 and 0.65. The five genes with the largest hypo-AF areas wereCHM,ABCC6,ABCA4,RDH12, andRPE65, with mean per-patient areas of 41.5, 30.0, 21.9, 21.4, and 15.1 mm2. The five genes with the largest hyper-AF areas wereBEST1,CDH23,RDH12,MYO7A, andNR2E3, with mean areas of 0.49, 0.45, 0.44, 0.39, and 0.34 mm2respectively. The five genes with largest ring areas wereCDH23,NR2E3,CRX,EYSandMYO7A,with mean areas of 3.63, 3.32, 2.84, 2.39, and 2.16 mm2. Vessel density was found to be highest inEFEMP1,BEST1,TIMP3,RS1, andPRPH2(10.6%, 10.3%, 9.8%, 9.7%, 8.9%) and was lower in Retinitis Pigmentosa (RP) and Leber Congenital Amaurosis genes. Longitudinal analysis of decreasing ring area in four RP genes (RPGR, USH2A, RHO, EYS) foundEYSto be the fastest progressor at -0.18 mm2/year.ConclusionsWe have conducted the first large-scale cross-sectional and longitudinal quantitative analysis of FAF features across a diverse range of IRDs using a novel AI approach.

Publisher

Cold Spring Harbor Laboratory

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