Abstract
ObjectiveTo compare axonal loss in ganglion cells detected with swept-source optical coherence tomography (SS-OCT) in eyes of patients with multiple sclerosis (MS) versus healthy controls using different machine learning techniques. To analyze the capability of machine learning techniques to improve the detection of retinal nerve fiber layer (RNFL) and the complex Ganglion Cell Layer–Inner plexiform layer (GCL+) damage in patients with multiple sclerosis and to use the SS-OCT as a biomarker to early predict this disease.MethodsPatients with relapsing-remitting MS (n = 80) and age-matched healthy controls (n = 180) were enrolled. Different protocols from the DRI SS-OCT Triton system were used to obtain the RNFL and GCL+ thicknesses in both eyes. Macular and peripapilar areas were analyzed to detect the zones with higher thickness decrease. The performance of different machine learning techniques (decision trees, multilayer perceptron and support vector machine) for identifying RNFL and GCL+ thickness loss in patients with MS were evaluated. Receiver-operating characteristic (ROC) curves were used to display the ability of the different tests to discriminate between MS and healthy eyes in our population.ResultsMachine learning techniques provided an excellent tool to predict MS disease using SS-OCT data. In particular, the decision trees obtained the best prediction (97.24%) using RNFL data in macular area and the area under the ROC curve was 0.995, while the wide protocol which covers an extended area between macula and papilla gave an accuracy of 95.3% with a ROC of 0.998. Moreover, it was obtained that the most significant area of the RNFL to predict MS is the macula just surrounding the fovea. On the other hand, in our study, GCL+ did not contribute to predict MS and the different machine learning techniques performed worse in this layer than in RNFL.ConclusionsMeasurements of RNFL thickness obtained with SS-OCT have an excellent ability to differentiate between healthy controls and patients with MS. Thus, the use of machine learning techniques based on these measures can be a reliable tool to help in MS diagnosis.
Highlights
Multiple sclerosis (MS) is a neurodegenerative disease of the central nervous system that disrupts the flow of information within the brain, and between the brain and body
The decision trees obtained the best prediction (97.24%) using retinal nerve fiber layer (RNFL) data in macular area and the area under the receiver-operating characteristic (ROC) curve was 0.995, while the wide protocol which covers an extended area between macula and papilla gave an accuracy of 95.3% with a ROC of 0.998
Measurements of RNFL thickness obtained with swept-source optical coherence tomography (SS-optical coherence tomography (OCT)) have an excellent ability to differentiate between healthy controls and patients with multiple sclerosis (MS)
Summary
Multiple sclerosis (MS) is a neurodegenerative disease of the central nervous system that disrupts the flow of information within the brain, and between the brain and body. Most people with MS are diagnosed between the ages of 20 and 50, with at least two to three times more women than men being diagnosed with the disease. An early diagnosis is determinant to slow down the progression of the disease [1]. MS is diagnosed on the basis of clinical findings and supporting evidence, such as magnetic resonance imaging (MRI) of the brain and spinal cord and cerebrospinal fluid examination [2], but these tests are invasive and expensive, so they are performed when there are evidences of the disease but not in a routine way. In the last years, different studies suggest that retinal optical coherence tomography (OCT) should be used as a complement to MRI for the analysis of neurodegeneration process in MS [3][4]
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