The Role of Optical Coherence Tomography Criteria and Machine Learning in Multiple Sclerosis and Optic Neuritis Diagnosis.

Abstract

BACKGROUND AND OBJECTIVES:

Recent studies have suggested that intereye differences (IEDs) in peripapillary retinal nerve fiber layer (pRNFL) or ganglion cell + inner plexiform (GCIPL) thickness by spectral domain optical coherence tomography (SD-OCT) may identify people with a history of unilateral optic neuritis (ON). However, this requires further validation. Machine learning classification may be useful for validating thresholds for OCT IEDs and for examining added utility for visual function tests, such as low-contrast letter acuity (LCLA), in the diagnosis of people with multiple sclerosis (PwMS) and for unilateral ON history.

METHODS:

Participants were from 11 sites within the International Multiple Sclerosis Visual System consortium. pRNFL and GCIPL thicknesses were measured using SD-OCT. A composite score combining OCT and visual measures was compared individual measurements to determine the best model to distinguish PwMS from controls. These methods were also used to distinguish those with a history of ON among PwMS. Receiver operating characteristic (ROC) curve analysis was performed on a training data set (2/3 of cohort) and then applied to a testing data set (1/3 of cohort). Support vector machine (SVM) analysis was used to assess whether machine learning models improved diagnostic capability of OCT.

RESULTS:

Among 1,568 PwMS and 552 controls, variable selection models identified GCIPL IED, average GCIPL thickness (both eyes), and binocular 2.5% LCLA as most important for classifying PwMS vs controls. This composite score performed best, with area under the curve (AUC) = 0.89 (95% CI 0.85-0.93), sensitivity = 81%, and specificity = 80%. The composite score ROC curve performed better than any of the individual measures from the model (p < 0.0001). GCIPL IED remained the best single discriminator of unilateral ON history among PwMS (AUC = 0.77, 95% CI 0.71-0.83, sensitivity = 68%, specificity = 77%). SVM analysis performed comparably with standard logistic regression models.

DISCUSSION:

A composite score combining visual structure and function improved the capacity of SD-OCT to distinguish PwMS from controls. GCIPL IED best distinguished those with a history of unilateral ON. SVM performed as well as standard statistical models for these classifications.

CLASSIFICATION OF EVIDENCE:

This study provides Class III evidence that SD-OCT accurately distinguishes multiple sclerosis from normal controls as compared with clinical criteria.

• Authors: Rachel C Kenney, Mengling Liu, Lisena Hasanaj, Binu Joseph, Abdullah Abu Al-Hassan, Lisanne J Balk, Raed Behbehani, Alexander Brandt, Peter A Calabresi, Elliot Frohman, Teresa C Frohman, Joachim Havla, Bernhard Hemmer, Hong Jiang, Benjamin Knier, Thomas Korn, Letizia Leocani, Elena Hernandez Martinez-Lapiscina, Athina Papadopoulou, Friedemann Paul, Axel Petzold, Marco Pisa, Pablo Villoslada, Hanna Zimmermann, Lorna E Thorpe, Hiroshi Ishikawa, Joel S Schuman, Gadi Wollstein, Yu Chen, Shiv Saidha, Steven Galetta, Laura J Balcer
• Journal: Neurology (Neurology) Vol. 99 Issue 11 Pg. e1100-e1112 (Sep 13 2022) ISSN: 1526-632X [Electronic] United States
• PMID: 35764402 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural)
• Copyright: © 2022 American Academy of Neurology.

Topics

• Humans
• Machine Learning
• Multiple Sclerosis (diagnosis)
• Nerve Fibers
• Optic Neuritis (diagnosis)
• Retinal Ganglion Cells
• Tomography, Optical Coherence (methods)