Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of More Than 3500 Inherited Retinal Disease Patients from the United Kingdom
Authors: Woof William , de Guimarães Thales A. C. , Al-Khuzaei Saoud , Daich Varela Malena , Sen Sagnik , Bagga Pallavi , Mendes Bernardo , Shah Mital , Burke Paula , Parry David , Lin Siying , Naik Gunjan , Ghoshal Biraja , Liefers Bart , Fu Dun Jack , Georgiou Michalis , Nguyen Quang , da Silva Alan Sousa , Liu Yichen , Fujinami-Yokokawa Yu , Sumodhee Dayyanah , Patel Praveen , Furman Jennifer , Moghul Ismail , Moosajee Mariya , Sallum Juliana , De Silva Samantha R. , Lorenz Birgit , Holz Frank , Fujinami Kaoru , Webster Andrew R , Mahroo Omar , Downes Susan M. , Madhusudhan Savita , Balaskas Konstantinos , Michaelides Michel , Pontikos Nikolas 
Published: March 2024

Purpose

To quantify relevant fundus autofluorescence (FAF) image features cross-sectionally and longitudinally in a large cohort of inherited retinal diseases (IRDs) patients.

Design

Retrospective study of imaging data (55-degree blue-FAF on Heidelberg Spectralis) from patients.

Participants

Patients 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.

Methods

Five 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 Measures

Quantitative 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.

Results

A 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.

Conclusions

We 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.