A new platform designed for glaucoma screening: identifying the risk of glaucomatous optic neuropathy using fundus photography with deep learning architecture together with intraocular pressure measurements

CORRESPONDING AUTHOR Jacek Pniewski, PhD, DSc, Faculty of Physics, University of Warsaw, 5 Pasteura St., 02-093 Warsaw, Poland, e-mail: j.pniewski@uw.edu. pl INTRODUCTION Glaucoma is a progressive optic nerve neuropathy, which is one of the leading causes of irreversible blindness worldwide. It affects approximately 70 million people and the number will increase to 112 million in 2040 [1]. Even in advanced stages glaucoma may remain asymptomatic, especially if asymmetric glaucomatous optic nerve damage is present. Thus, there is a great need for screening programmes for glaucoma to diagnose the disease at early levels without changes in visual fields [2, 3]. The main diagnostic tools for glaucoma typically include optic nerve head (ONH) evaluation, detection of visual field defects and elevated intraocular pressure. Glaucomatous optic neuropathy (GON) is characterised by some typical changes in the appearance of the optic disc: enlarged vertical cup to disc ratio (vCDR), notching and/or thinning of neuroretinal rim, parapapillary atrophy, nasalization of central ONH vessels, disc haemorrhages, asymmetric vCDR [4-6]. These changes can be detected in fundus images and are one of the most important aspects of glaucoma diagnosis. Increased intraocular pressure (IOP) is still considered the strongest risk factor for glaucoma development and progression, but is not necessary for glaucoma diagnosis. Clinical trials confirmed that 30-50% of glaucoma patients have IOP within normal limits [2]. ABSTRACT Aim of the study: To develop a platform designed for glaucoma screening, based on deep learning algorithms, for the diagnosis of glaucomatous optic neuropathy from colour fundus images and intraocular pressure, not requiring medical staff. Material and methods: A modular platform for glaucoma screening is developed which uses classifiers that independently evaluate the parameters of the visual system. The fundus image classifier is based on trainable mathematical models, while an intraocular pressure classifier is a threshold classifier. Performance analysis is conducted in terms of the statistical parameters: sensitivity, accuracy, precision, and specificity. Glaucoma images were classified by two experts. The cut-off of vertical cup to disc ratio (vCDR) for glaucoma was set at ≥ 0.7. In the training stage 933 healthy and 754 glaucoma images were used. If the intraocular pressure (IOP) was ≥ 24 mmHg in at least one eye the patient was classified in the glaucoma category independently of the fundus image category. During the training stage the following parameters of the image classifier were achieved: sensitivity 0.82 and specificity 0.63. Results and conclusions: For the test data from two campaigns were used (total 1104 fundus images). For the image classifier, sensitivity 0.73 and specificity 0.83 were obtained for the first campaign, while sensitivity 0.84 and specificity 0.67 were obtained for the second campaign. The final achieved parameters of the platform are: sensitivity 0.79 and specificity 0.67 for the first campaign, sensitivity 0.92 and specificity 0.42 for the second campaign. The results are in accordance with other studies and the platform proved its usability and good performance.