Deep Learning to Distinguish ABCA4-Related Stargardt Disease from PRPH2-Related Pseudo-Stargardt Pattern Dystrophy.

Alexandra Miere,Olivia Zambrowski,Francesca Amoroso,Arthur Kessler,Eric Petit,Carl-Joe Mehanna,Carlotta Pallone,Eric H Souied,Daniel Seknazi,Paul Denys

doi:10.3390/jcm10245742

Abstract

(1) Background: Recessive Stargardt disease (STGD1) and multifocal pattern dystrophy simulating Stargardt disease (“pseudo-Stargardt pattern dystrophy”, PSPD) share phenotypic similitudes, leading to a difficult clinical diagnosis. Our aim was to assess whether a deep learning classifier pretrained on fundus autofluorescence (FAF) images can assist in distinguishing ABCA4-related STGD1 from the PRPH2/RDS-related PSPD and to compare the performance with that of retinal specialists. (2) Methods: We trained a convolutional neural network (CNN) using 729 FAF images from normal patients or patients with inherited retinal diseases (IRDs). Transfer learning was then used to update the weights of a ResNet50V2 used to classify the 370 FAF images into STGD1 and PSPD. Retina specialists evaluated the same dataset. The performance of the CNN and that of retina specialists were compared in terms of accuracy, sensitivity, and precision. (3) Results: The CNN accuracy on the test dataset of 111 images was 0.882. The AUROC was 0.890, the precision was 0.883 and the sensitivity was 0.883. The accuracy for retina experts averaged 0.816, whereas for retina fellows it averaged 0.724. (4) Conclusions: This proof-of-concept study demonstrates that, even with small databases, a pretrained CNN is able to distinguish between STGD1 and PSPD with good accuracy.

Highlights

The adenosine triphosphate-binding cassette, subfamily A, member 4 (ABCA4) gene encodes for a membrane-associated protein located in the outer segment (OS) disc membranes of rod and cone photoreceptors [1,2]
(4) Conclusions: This proof-of-concept study demonstrates that, even with small databases, a pretrained convolutional neural network (CNN) is able to distinguish between STGD1 and PseudoStargardt pattern dystrophy (PSPD) with good accuracy
Our objective was to consider whether a deep learning classifier, pretrained using various FAF images, can assist in distinguishing ABCA4-related STGD1 from PSPD caused by mutations in PRPH2/RDS, and to compare these findings with the grading of retinal specialists

Summary

Introduction

The adenosine triphosphate-binding cassette, subfamily A, member 4 (ABCA4) gene encodes for a membrane-associated protein located in the outer segment (OS) disc membranes of rod and cone photoreceptors [1,2]. Mutations in the ABCA4 gene are a known cause for recessive Stargardt disease (STGD1). STGD1 follows an autosomal recessive pattern of inheritance, with usual disease onset in the second decade of life. The association of these findings is considered pathognomonic to STGD1; the phenotype is not necessarily exclusive to STGD1. Multifocal pattern dystrophy simulating STGD1 (“pseudo-Stargardt pattern dystrophy”) is an autosomal-dominant inherited retinal disease, caused by a PRPH2/RDS mutation that may simulate STGD1 [7]. PseudoStargardt pattern dystrophy (PSPD) patients may display yellowish fundus flecks and chorioretinal atrophy [7]

Objectives

Methods

Conclusion