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Artificial intelligence (AI) offers a new way to screen for eye diseases using optical coherence tomography angiography (OCTA). This technology can differentiate and classify various retinal conditions, improving accessibility in underserved areas.

Keywords:
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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) shows potential as an accessible screening tool for ocular diseases, especially in underserved regions lacking ophthalmologists.
  • Quantitative optical coherence tomography angiography (OCTA) effectively detects vascular changes crucial for diagnosing retinovascular diseases.
  • Current AI applications for classifying multiple ocular diseases are not well-established.

Purpose of the Study:

  • To develop and validate a supervised machine learning (ML) model for multi-task classification of ocular diseases using OCTA.
  • To differentiate normal from diseased eyes, distinguish between different ocular diseases, and stage disease severity.
  • To identify optimal quantitative OCTA features for accurate multi-task classification.

Main Methods:

  • Supervised ML-based multi-task classification of OCTA images.
  • Automatic extraction of quantitative OCTA features: blood vessel tortuosity (BVT), vascular caliber (BVC), vessel perimeter index (VPI), blood vessel density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and FAZ contour irregularity (FAZ-CI).
  • Stepwise backward elimination to select sensitive OCTA features and optimal combinations for classification, validated using diabetic retinopathy (DR) and sickle cell retinopathy (SCR).

Main Results:

  • Demonstrated a supervised ML approach for multi-task OCTA classification.
  • Identified key OCTA features for differentiating and staging ocular diseases.
  • Successfully validated the classifier using diabetic retinopathy and sickle cell retinopathy as proof-of-concept.

Conclusions:

  • The developed AI classification methodology is effective for multi-task OCTA analysis.
  • This approach can be extended to various ocular diseases, paving the way for mass-screening platforms.
  • The technology holds significant promise for clinical deployment and telemedicine, enhancing eye care accessibility.