Method to Enhance Optical Coherence Tomography (OCT) for Eye Disease Detection

This technology is an artificial intelligence-based platform for improving the quality of images taken from portable OCT devices and detecting age-related macular degeneration.

Unmet Need: A low-cost, portable detection of age-related macular degeneration (AMD)

Current methods for diagnosing ophthalmic disease use optical coherence tomography (OCT). However, these devices are bulky, costly and require expertise to operate. Portable OCT devices offer potential, but images acquired from these devices are lower resolution. These shortcomings have led to a gap in routine eye disease diagnosis and care in under-resourced communities. If left undiagnosed, conditions such as AMD may lead to sight loss.

The Technology: Artificial intelligence-based enhancement of portable OCT and detection of AMD

This technology uses generative adversarial networks (GANs) to improve the quality of images taken from portable OCT devices. These enhanced images are then fed through a deep learning-based method for the detection of AMD.

This technology has been validated with a Duke University dataset containing 221 images from 59 individuals.

Applications:

  • Ophthalmic disease detection for low resource or natural disaster settings
  • Ophthalmic disease detection for defense/military settings
  • Early AMD diagnostic tool for use in pharmacies or internal medicine offices
  • Detection tool for individuals with high risk of macular degeneration or other ophthalmic diseases

Advantages:

  • Portable device
  • Cost-effective
  • Automated AMD detection pipeline

Lead Inventor:

Kaveri Thakoor, Ph.D.

Patent Information:

Patent pending

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