Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier

Abstract

For the first time, this study describes the use of a deep learning-based algorithm to automatically detect and classify GA in FAF. Hereby, the created classifiers showed excellent results. With further developments, this model may be a tool to predict the individual progression risk of GA and give relevant information for future therapeutic approaches.

Artificial Intelligence Algorithms for Analysis of Geographic Atrophy: A Review and Evaluation

“…A synopsis of the literature can be found in Table 3 . 23 – 49 The complete review dataset, which included in-depth information regarding all computing processes used and results obtained, can be found in Supplementary Table S1 .…”

Deep Learning-Based Classification of Inherited Retinal Diseases Using Fundus Autofluorescence

“…FAF imaging has been used in a deep learning-based algorithm to automatically detect and classify GA by Treder et al [ 21 ] as well as to detect chorioretinal atrophy by Ometto et al [ 26 ] and to distinguish GA from Stargardt disease by Wang et al [ 27 ]. In the study performed by Treder et al, two classifiers were built to differentiate between GA and healthy-eye FAF images and between GA and a group named other retinal diseases (ORD), with a training and validation set of 200 GA FAF images, 200 healthy-eye FAF images, and 200 FAF images of ORD.…”

“…Following the ImageNet Large Scale Visual Recognition Challenge, Russakovsky and collaborators demonstrated that the object classification capabilities of CNN architectures can surpass those of humans [ 37 ]. While the applications of artificial intelligence have mainly focused on diabetic retinopathy and age-related macular degeneration or glaucoma [ 19 , 20 , 21 , 22 , 23 ], IRDs would make an interesting candidate due to the typical, symmetrical phenotype of these disorders.…”

“…Given their rarity, these conditions are often difficult to diagnose and patients can endure a long journey involving many ophthalmologists. Over the past years, machine learning and more recently deep learning have been increasingly applied to a vast array of ophthalmological diseases from diabetic retinopathy (DR) [ 19 ] to age-related macular degeneration (AMD) [ 20 , 21 ] and glaucoma [ 22 ], with sensitivities and specificities above 90% [ 19 , 20 , 21 , 22 , 23 , 24 ]. Deep learning approaches were also in used in less frequent conditions, for instance to detect and to analyze to progression of chorioretinal atrophy [ 25 ] or to distinguish GA from Stargardt diseases using FAF imaging [ 26 ].…”

Chancen von künstlicher Intelligenz und Big Data für die Diagnostik und Behandlung der altersabhängigen Makuladegeneration

“…Unsere Arbeitsgruppe nutzte ein vortrainiertes DCNN, um eine geografische Atrophie (GA) anhand einer Fundusautofluoreszenzaufnahme automatisch zu erkennen und von gesunder Netzhaut und anderen retinalen Erkrankungen abzugrenzen. In einem weiteren Schritt wurde dasselbe DCNN dahingehend trainiert, die GA-Bilder nach in der Literatur definierten Mustern mit verschiedenen Progressionsraten zu klassifizieren [51,58,59].…”