Abstract
Background: The aim of this paper is to implement a system to facilitate the diagnosis of multiple sclerosis (MS) in its initial stages. It does so using a convolutional neural network (CNN) to classify images captured with swept-source optical coherence tomography (SS-OCT). Methods: SS-OCT images from 48 control subjects and 48 recently diagnosed MS patients have been used. These images show the thicknesses (45 × 60 points) of the following structures: complete retina, retinal nerve fiber layer, two ganglion cell layers (GCL+, GCL++) and choroid. The Cohen distance is used to identify the structures and the regions within them with greatest discriminant capacity. The original database of OCT images is augmented by a deep convolutional generative adversarial network to expand the CNN’s training set. Results: The retinal structures with greatest discriminant capacity are the GCL++ (44.99% of image points), complete retina (26.71%) and GCL+ (22.93%). Thresholding these images and using them as inputs to a CNN comprising two convolution modules and one classification module obtains sensitivity = specificity = 1.0. Conclusions: Feature pre-selection and the use of a convolutional neural network may be a promising, nonharmful, low-cost, easy-to-perform and effective means of assisting the early diagnosis of MS based on SS-OCT thickness data.
Highlights
In recent years, the usefulness of deep learning (DL) techniques has been demonstrated in many applications, including in medicine [1]
The main contributions of this paper can be summarized as follows: first, we propose applying convolutional neural network (CNN) to the diagnosis of early-stage multiple sclerosis (MS) using the most discriminant retina layer thicknesses measured by OCT; to our knowledge, this is the first time that a CNN has been used to classify retinal layer thickness data obtained using OCT
In order to build an effective diagnostic tool, the sample size needed to detect differences of at least 5 μm in ganglion cell layer (GCL)+ thicknesses measured by Triton OCT—applying a bilateral test with α = 5% risk, β = 10% risk, and an unexposed/exposed ratio of 0.5—amounts to at least 86 eyes (43 from healthy subjects and 43 from MS patients) [76]
Summary
The usefulness of deep learning (DL) techniques has been demonstrated in many applications, including in medicine [1]. Several recent reviews analyze ophthalmologic applications of DL [17,18] These include the diagnosis and classification of glaucoma based on disc photos, the segmentation of retinal layers using optical coherence tomography (OCT), forecasting future Humphrey visual fields [19], the diagnosis of diabetic retinopathy [20], macular degeneration progression [21], retinopathy of prematurity [22] or estimation of retinal sensitivity in macular telangiectasia type 2 [23]. Methods: SS-OCT images from 48 control subjects and 48 recently diagnosed MS patients have been used These images show the thicknesses (45 × 60 points) of the following structures: complete retina, retinal nerve fiber layer, two ganglion cell layers (GCL+, GCL++) and choroid. Results: The retinal structures with greatest discriminant capacity are the GCL++ (44.99% of image points), complete retina (26.71%) and GCL+ (22.93%) Thresholding these images and using them as inputs to a CNN comprising two convolution modules and one classification module obtains sensitivity = specificity = 1.0. Conclusions: Feature pre-selection and the use of a convolutional neural network may be a promising, nonharmful, low-cost, easy-to-perform and effective means of assisting the early diagnosis of MS based on SS-OCT thickness data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
