Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding

Abstract

Optical coherence tomography (OCT) is a useful technique to monitor retinal damage. We present an automatic method to accurately classify rodent OCT images in healthy and pathological (before and after 14 days of intravitreal injection of Endothelin-1, respectively) making use of the DenseNet-201 architecture fine-tuned and a customized top-model. We validated the performance of the method on 1912 OCT images yielding promising results (AU C = 0.99 ± 0.01 in a P = 15 leave-Pout cross-validation). Besides, we also compared the results of the fine-tuned network with those achieved training the network from scratch, obtaining some interesting insights. The presented method poses a step forward in understanding pathological rodent OCT retinal images, as at the moment there is no known discriminating characteristic which allows classifying this type of images accurately. The result of this work is a very accurate and robust automatic method to distinguish between healthy and a rodent model of glaucoma, which is the backbone of future works dealing with human OCT images.

Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging

“…In a recent study by Fuentes-Hurtado, et al. a DenseNet CNN was further trained to discriminate between glaucoma and healthy eyes in OCT imaging acquired in a rodent (rat) model, achieving an AUC of 0.99 ( 90 ). More recently, Choy et al.…”

“…An article about a DL algorithm trained to predict age from OCT RNFL was also reserved for discussion of the implications for aging research ( 89 ). In addition, an article about a DL model developed in rodent models of glaucoma was retained and combined with three other articles about application of DL in the basic sciences for the discussion of future directions ( 90 – 93 ).…”

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

“…Such clinically informed POAG sub-phenotypes provide opportunities for new frontiers of reverse translation. Existing AI studies in animal models are mainly limited to discriminating between glaucomatous and healthy eyes via OCT ( 22 ). With the reverse translation of AI findings from clinical studies, novel animal models can be developed or identified to understand the distinctive disease mechanisms for each of the POAG subtypes.…”