Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques

• Published in: PloS one Vol. 14; no. 5; p. e0216410
• Main Authors: Pérez del Palomar, Amaya, Cegoñino, José, Montolío, Alberto, Orduna, Elvira, Vilades, Elisa, Sebastián, Berta, Pablo, Luis E., Garcia-Martin, Elena
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 06.05.2019; Public Library of Science (PLoS)
• Subjects: Adult; Artificial intelligence; Autoimmune diseases; Biological markers; Biology and Life Sciences; Biomarkers; Biomedical materials; Brain research; Cancer; Computer and Information Sciences; Cross-Sectional Studies; Decision trees; Diagnosis; Dietary fiber; Disease control; Displays (Marketing); Early Diagnosis; Engineering and Technology; Engineering research; Eye; Eye (anatomy); Female; Fovea; Ganglion cells; Ganglion cysts; Hospitals; Humans; Image Processing, Computer-Assisted; Internet; Learning algorithms; Machine Learning; Macula Lutea - diagnostic imaging; Male; Mechanical engineering; Medical imaging; Medical prognosis; Medical research; Medicine and Health Sciences; Middle Aged; Multilayer perceptrons; Multiple sclerosis; Multiple Sclerosis, Relapsing-Remitting - diagnostic imaging; Nervous system; Neurodegeneration; NMR; Nuclear magnetic resonance; Optical Coherence Tomography; Optics; Physical Sciences; Recurrence (Disease); Research and Analysis Methods; Retina; Retinal ganglion cells; Retinal Ganglion Cells - pathology; Stability; Support vector machines; Systematic review; Teaching methods; Thickness; Tomography; Tomography, Optical Coherence; United States > US; Spain
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0216410

To 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. Patients 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. Machine 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. Measurements 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.