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Related Experiment Video

Updated: Jan 17, 2026

Evaluation of Capillary and Other Vessel Contribution to Macular Perfusion Density Measured with Optical Coherence Tomography Angiography
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Evaluation of Capillary and Other Vessel Contribution to Macular Perfusion Density Measured with Optical Coherence Tomography Angiography

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Machine learning-based migraine analysis using retinal vessel diameters from optical coherence tomography: an

Fırat Orhanbulucu1,2, Metin Ünlü3, Duygu Gülmez Sevim3

  • 1Department of Biomedical Engineering, Faculty of Engineering, Inonu University, Malatya, Türkiye.

Neurological Sciences : Official Journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
|September 16, 2025
PubMed
Summary

Optical Coherence Tomography (OCT) retinal imaging can predict migraine. Machine learning models, particularly ensemble-based boosting, achieved high accuracy in distinguishing migraine patients from healthy controls.

Keywords:
Boosting algorithmsMachine learningMigraineOptical coherence tomographyRetinal vessel diameters

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Area of Science:

  • Ophthalmology
  • Neurology
  • Medical Imaging

Background:

  • Migraine is a complex neurological disorder affecting the central nervous system and retinal vasculature.
  • Retinal imaging techniques, like Optical Coherence Tomography (OCT), offer a promising avenue for studying neuro-ophthalmological conditions.
  • This study explores the potential of OCT-derived measurements for migraine prediction.

Purpose of the Study:

  • To predict migraine using measurements from retinal images obtained via OCT.
  • To evaluate the efficacy of machine learning algorithms in classifying migraine patients based on OCT data.

Main Methods:

  • Examined 70 eyes of migraine patients and 38 eyes of healthy controls.
  • Utilized features including retinal artery/vein diameters and choroidal thickness.
  • Applied the SMOTE method for data balancing and Pearson's Correlation Coefficient for feature analysis.

Main Results:

  • The LightGBM algorithm demonstrated superior performance in classifying migraine patients.
  • Achieved high classification metrics: 93.28% AUC, 91.14% Accuracy, 86.67% F1-score, 0.74 Kappa statistic, and 0.76 Matthews Correlation Coefficient.
  • Ensemble-based boosting models outperformed traditional machine learning classifiers.

Conclusions:

  • Machine learning algorithms show effective performance in predicting migraine from OCT data.
  • This preliminary study highlights the potential of OCT imaging in migraine diagnosis.
  • Ensemble-based boosting models are particularly effective for this application.