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Advancing Optical Coherence Tomography Diagnostic Capabilities: Machine Learning Approaches to Detect Autoimmune
Rachel C Kenney1, Thomas A Flagiello, Anitha D' Cunha
1Departments of Neurology (RK, TF, SNG, LJB, SLG), and Population Health (RK, LJB), New York University Grossman School of Medicine, New York, New York; Department of Medicine (RK), Vanderbilt University Medical Center, Nashville, Tennessee; Center for Advanced Neurological Research (ADC, LP), Nitte University, Mangalore, India; Department of Neurology (SA), KS Hegde Medical Academy, Nitte University, Mangalore, India; Experimental and Clinical Research Center (FO, FP), Max Delbrueck Center for Molecular Medicine and Charité-Universitätsmedizin Berlin, Berlin, Germany; Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin (FO, FP), Berlin, Germany; Neuroscience Clinical Research Center (FO, FP), Charité-Universitätsmedizin Berlin, Berlin, Germany; Department of Neurology (FO, FP), Charité-Universitätsmedizin Berlin, Berlin, Germany; Department of Radiology and Radiological Sciences (KS), Vanderbilt University Medical Center, Nashville, Tennessee; and Department of Ophthalmology (LJB, SLG), New York University Grossman School of Medicine, New York, New York.
Background:
In many parts of the world including India, the prevalence of autoimmune inflammatory diseases such as neuromyelitis optica spectrum disorders (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and multiple sclerosis (MS) is rising. A diagnosis is often delayed due to insufficient diagnostic tools. Machine learning (ML) models have accurately differentiated eyes of patients with MS from those of healthy controls (HCs) using optical coherence tomography (OCT)-based retinal images. Examining OCT characteristics may allow for early differentiation of these conditions. The objective of this study was to determine feasibility of ML analyses to distinguish between patients with different autoimmune inflammatory diseases, other ocular diseases, and HCs based on OCT measurements of the peripapillary retinal nerve fiber layer (pRNFL), ganglion cell-inner plexiform layer (GCIPL), and inner nuclear layers (INLs).
Methods:
Eyes of people with MS (n = 99 patients), NMOSD (n = 40), MOGAD (n = 74), other ocular diseases (OTHER, n = 16), and HCs (n = 54) from the Mangalore Demyelinating Disease Registry were included. Support vector machine (SVM) classification models incorporating age, pRNFL, GCIPL, and INL were performed. Data were split into training (70%) and testing (30%) data and accounted for within-patient correlations. Cross-validation was used in training to choose the best parameters for the SVM model. Accuracy and area under receiver operating characteristic curves (AUROCs) were used to assess model performance.
Results:
The SVM models distinguished between eyes of patients with each condition (i.e., MOGAD vs NMOSD, NMOSD vs HC, MS vs OTHER, etc) with strong discriminatory power demonstrated from the AUROCs for these comparisons ranging from 0.81 to 1.00. These models also performed with moderate to high accuracy, ranging from 0.66 to 0.81, with the exception of the MS vs NMOSD comparison, which had an accuracy of 0.53.
Conclusion S:
ML models are useful for distinguishing between autoimmune inflammatory diseases and for distinguishing these from HCs and other ocular diseases based on OCT measures. This study lays the groundwork for future deep learning studies that use analyses of raw OCT images for identifying eyes of patients with such disorders and other etiologies of optic neuropathy.
