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Comparison of Machine Learning Methods Using Spectralis OCT for Diagnosis and Disability Progression Prognosis in

Alberto Montolío1,2,3, José Cegoñino4,5, Elena Garcia-Martin6,7

  • 1Group of Biomaterials, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain. amontolio@unizar.es.

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Summary

Machine learning accurately diagnoses multiple sclerosis (MS) and predicts its progression using optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) thickness measurements. This approach aids clinicians in managing MS patient care.

Keywords:
Machine learningMultiple sclerosisOptical coherence tomographyRetinal nerve fiber layer

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Area of Science:

  • Ophthalmology
  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Multiple sclerosis (MS) diagnosis and prognosis rely on clinical and imaging data.
  • Optical coherence tomography (OCT) provides high-resolution imaging of the retinal nerve fiber layer (RNFL).
  • Machine learning (ML) offers potential for analyzing complex medical data for diagnostic and prognostic insights.

Purpose of the Study:

  • To evaluate machine learning models for diagnosing MS using RNFL thickness.
  • To assess the predictive capability of ML models for MS disability progression over time.
  • To compare different OCT protocols and ML algorithms for optimal performance.

Main Methods:

  • A cross-sectional study of 72 MS patients and 30 controls for diagnosis.
  • A 10-year longitudinal follow-up of MS patients for prognosis.
  • RNFL thickness measured using Spectralis OCT with various protocols (macular and peripapillary).
  • Binary classifiers including MLR, SVM, DT, k-NN, NB, EC, and LSTM were tested.

Main Results:

  • For MS diagnosis, k-NN with the fast macular thickness protocol achieved 95.8% accuracy.
  • For MS prognosis, a 3-year follow-up model predicted disability 8 years later with high accuracy (up to 91.3%).
  • Decision Tree (DT) and Support Vector Machine (SVM) models showed strong predictive performance for prognosis.

Conclusions:

  • RNFL thickness measurements via Spectralis OCT are valuable for MS diagnosis.
  • ML analysis of RNFL thickness effectively predicts disability progression in MS patients.
  • This ML approach provides clinicians with crucial information for MS management.