Dataset Information

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

ABSTRACT: OBJECTIVE: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. METHODS: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. RESULTS: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. CONCLUSIONS: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.

SUBMITTER: Perez Del Palomar A

PROVIDER: S-EPMC6502323 | biostudies-other | 2019

REPOSITORIES: biostudies-other

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

Pérez Del Palomar Amaya A Cegoñino José J Montolío Alberto A Orduna Elvira E Vilades Elisa E Sebastián Berta B Pablo Luis E LE Garcia-Martin Elena E

PloS one 20190506 5

<h4>Objective</h4>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 bioma ...[more]

PMID: 31059539

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Project description:PurposeTo describe enhanced vitreous imaging for visualization of anatomic features and microstructures within the posterior vitreous and vitreoretinal interface in healthy eyes using swept-source optical coherence tomography (SS-OCT). The study hypothesis was that long-wavelength, high-speed, volumetric SS-OCT with software registration motion correction and vitreous window display or high-dynamic-range (HDR) display improves detection sensitivity of posterior vitreous and vitreoretinal features compared to standard OCT logarithmic scale display.DesignObservational prospective cross-sectional study.MethodsMultiple wide-field three-dimensional SS-OCT scans (500×500A-scans over 12×12 mm2) were obtained using a prototype instrument in 22 eyes of 22 healthy volunteers. A registration motion-correction algorithm was applied to compensate motion and generate a single volumetric dataset. Each volumetric dataset was displayed in three forms: (1) standard logarithmic scale display, enhanced vitreous imaging using (2) vitreous window display and (3) HDR display. Each dataset was reviewed independently by three readers to identify features of the posterior vitreous and vitreoretinal interface. Detection sensitivities for these features were measured for each display method.ResultsFeatures observed included the bursa premacularis (BPM), area of Martegiani, Cloquet's/BPM septum, Bergmeister papilla, posterior cortical vitreous (hyaloid) detachment, papillomacular hyaloid detachment, hyaloid attachment to retinal vessel(s), and granular opacities within vitreous cortex, Cloquet's canal, and BPM. The detection sensitivity for these features was 75.0% (95%CI: 67.8%-81.1%) using standard logarithmic scale display, 80.6% (95%CI: 73.8%-86.0%) using HDR display, and 91.9% (95%CI: 86.6%-95.2%) using vitreous window display.ConclusionsSS-OCT provides non-invasive, volumetric and measurable in vivo visualization of the anatomic microstructural features of the posterior vitreous and vitreoretinal interface. The vitreous window display provides the highest sensitivity for posterior vitreous and vitreoretinal interface analysis when compared to HDR and standard OCT logarithmic scale display. Enhanced vitreous imaging with SS-OCT may help assess the natural history and treatment response in vitreoretinal interface diseases.

Dataset Information

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

Publications

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

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