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Poster Session: Leveraging AI to classify sex based on fovea shape features.

Knectt Lendoye1, Raheleh Kafieh2, David Steel1

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This study uses artificial intelligence (AI) to analyze Optical Coherence Tomography (OCT) scans, identifying specific fovea shape features to differentiate between male and female retinas. This AI-driven approach reveals novel retinal biomarkers for sex classification.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Biomedical Imaging

Background:

  • Deep neural networks can classify sex from retinal images, but identifying influential features remains challenging.
  • Understanding foveal variability between sexes is crucial for interpreting retinal structure.
  • Previous methods lacked detailed feature analysis for sex classification from OCT scans.

Purpose of the Study:

  • To develop and validate an AI-based methodology for classifying sex using fovea shape features from OCT scans.
  • To identify and rank specific retinal features contributing to sex differentiation.
  • To explore foveal morphology variations between sexes.

Main Methods:

  • Segmentation of 4000 healthy UK Biobank OCT scans.
  • Extraction of over 50 foveal features, including layer boundaries and thicknesses.
  • Training and evaluation of machine learning classifiers, ranking feature importance.

Main Results:

  • Classifiers achieved 0.55 ROC with 4 features and improved to 0.65 ROC with 49 features on single B-scan segmentation.
  • The methodology successfully identified key foveal features influencing sex classification.
  • Demonstrated improved performance over random chance with increased feature utilization.

Conclusions:

  • The proposed AI methodology effectively leverages foveal shape features for sex classification from OCT scans.
  • This approach facilitates the discovery of meaningful retinal biomarkers and enhances the analysis of fovea morphology.
  • The study highlights the potential of AI in uncovering subtle biological variations within retinal imaging data.