Prediction of causative genes in inherited retinal disorder from fundus photography and autofluorescence imaging using deep learning techniques

Abstract

Background/AimsTo investigate the utility of a data-driven deep learning approach in patients with inherited retinal disorder (IRD) and to predict the causative genes based on fundus photography and fundus autofluorescence (FAF) imaging.MethodsClinical and genetic data from 1302 subjects from 729 genetically confirmed families with IRD registered with the Japan Eye Genetics Consortium were reviewed. Three categories of genetic diagnosis were selected, based on the high prevalence of their causative genes: Stargardt disease (ABCA4), retinitis pigmentosa (EYS) and occult macular dystrophy (RP1L1). Fundus photographs and FAF images were cropped in a standardised manner with a macro algorithm. Images for training/testing were selected using a randomised, fourfold cross-validation method. The application program interface was established to reach the learning accuracy of concordance (target: >80%) between the genetic diagnosis and the machine diagnosis (ABCA4, EYS, RP1L1 and normal).ResultsA total of 417 images from 156 Japanese subjects were examined, including 115 genetically confirmed patients caused by the three prevalent causative genes and 41 normal subjects. The mean overall test accuracy for fundus photographs and FAF images was 88.2% and 81.3%, respectively. The mean overall sensitivity/specificity values for fundus photographs and FAF images were 88.3%/97.4% and 81.8%/95.5%, respectively.ConclusionA novel application of deep neural networks in the prediction of the causative IRD genes from fundus photographs and FAF, with a high prediction accuracy of over 80%, was highlighted. These achievements will extensively promote the quality of medical care by facilitating early diagnosis, especially by non-specialists, access to care, reducing the cost of referrals, and preventing unnecessary clinical and genetic testing.

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

“…Furthermore, the literature has also recently focused on distinguishing STGD1 from healthy controls, using OCT as the imaging technique for the DL classification, and obtaining high accuracies despite the relatively small datasets [20]. Other groups have also focused on the prediction of causative genes in IRDs from color fundus photography, FAF imaging [21], or SD-OCT [22].…”

Development of preprocessing methods and revised EfficientNet for diabetic retinopathy detection

“…API is a set of definitions and protocols for building and integrating application software 46 . Thus, API is a way for two or more computer programs to communicate with each other.…”

“…API is a set of definitions and protocols for building and integrating application software. 46 Thus, API is a way for two or more computer programs to communicate with each other. This study implemented the proposed system using API, so that users could upload the fundus image to the API and obtain the DR results.…”

Federated Learning in Ocular Imaging: Current Progress and Future Direction

“…Connecting multiple institutions on a global scale could improve clinical decisions regardless of patients’ location and demographic environment. Fujinami-Yokokawa Y et al [ 79 ] trained and validated a DL system for automated classification among ABCA4-, EYS-, and RP1L1-associated retinal dystrophies using a Japanese Eye Genetics Consortium dataset of 417 images (fundus photographs and FAF images). Although the DL system could provide an accurate diagnosis of three inherited retinal diseases, there is limited phenotypic heterogeneity within each group, and the dataset is from a specific ethnic population.…”