Machine learning classification of multiple sclerosis in children using optical coherence tomography

Abstract

Background: In children, multiple sclerosis (MS) is the ultimate diagnosis in only 1/5 to 1/3 of cases after a first episode of central nervous system (CNS) demyelination. As the visual pathway is frequently affected in MS and other CNS demyelinating disorders (DDs), structural retinal imaging such as optical coherence tomography (OCT) can be used to differentiate MS. Objective: This study aimed to investigate the utility of machine learning (ML) based on OCT features to identify distinct structural retinal features in children with DDs. Methods: This study included 512 eyes from 187 ( neyes = 374) children with demyelinating diseases and 69 ( neyes = 138) controls. Input features of the analysis comprised of 24 auto-segmented OCT features. Results: Random Forest classifier with recursive feature elimination yielded the highest predictive values and identified DDs with 75% and MS with 80% accuracy, while multiclass distinction between MS and monophasic DD was performed with 64% accuracy. A set of eight retinal features were identified as the most important features in this classification. Conclusion: This study demonstrates that ML based on OCT features can be used to support a diagnosis of MS in children.

Diagnosis of multiple sclerosis using optical coherence tomography supported by artificial intelligence

“…With the same database but using convolutional neural networks to implement data augmentation using generative adversarial networks and a classifier, a perfect classification was obtained (accuracy = 1.0) (López-Dorado et al, 2021). In a paediatric population (disease duration: 0.6 years) it yielded accuracy = 0.80 when classifying multiple sclerosis patients versus controls using a Random Forest classifier (Ciftci Kavaklioglu et al, 2022). Since the results reported to date depend on factors such as the patient cohort characteristics, the technology used to take the readings (spectral domain OCT versus swept-source OCT), the exploratory protocol employed, the data analysis and classification method, as well as consideration of the subjects' other biological variables, etc., further studies are needed to determine the most appropriate procedure.…”

“…Machine learning (ML) techniques are capable of identifying complex relationships or patterns in empirical data and applying that knowledge to new datasets. Conventional neural networks, support vector machines and other traditional automatic classifiers can be used in MS diagnosis supported by OCT if the available information comprises peripapillary measurements or the average thickness values in the regions defined by the ETDRS grid (Cavaliere et al, 2019;Ciftci Kavaklioglu et al, 2022;Garcia-Martin et al, 2021;Kenney et al, 2022). When analysing a greater number of measurements, such as those produced by a 45 × 60 thicknesses matrix, it is preferable to use a convolutional neural network (CNN) (López-Dorado et al, 2021).…”

Optimal Integration of Machine Learning for Distinct Classification and Activity State Determination in Multiple Sclerosis and Neuromyelitis Optica

“…One of the limitations is that it was satisfied with only one model and did not compare it with modern models, and this is not sufficient to verify the results [29]. Ciftci worked on detecting multiple sclerosis through optical coherence tomography (OCT) features in children through machine learning algorithms, wherein the study was evaluated on a dataset of 512 eyes from 187 children, where the random forest model outperformed other models with a detection accuracy of 80% for MS and 75% for demyelinating diseases [30]. Kenny did so on separating optic neuritis and people with multiple sclerosis.…”