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Estimating Retinal Sensitivity Using Optical Coherence Tomography With Deep-Learning Algorithms in Macular

Yuka Kihara1, Tjebo F C Heeren2,3, Cecilia S Lee1

  • 1Department of Ophthalmology, University of Washington, Seattle.

JAMA Network Open
|February 9, 2019
PubMed
Summary

A new deep-learning model estimates retinal sensitivity from OCT scans, creating high-resolution maps for Macular Telangiectasia type 2. This offers a more efficient way to monitor disease progression and potential surrogate outcome measure.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Microperimetry is a crucial but time-consuming clinical test for assessing retinal sensitivity.
  • Current methods require significant time and specialized technicians, posing a burden in clinical practice.

Purpose of the Study:

  • To develop a deep-learning network capable of estimating retinal sensitivity directly from structural data.
  • To generate high-resolution, en face maps of estimated retinal sensitivity for improved visualization and analysis.

Main Methods:

  • A cross-sectional study utilized optical coherence tomography (OCT) scans and microperimetry data from 38 participants with Macular Telangiectasia type 2.
  • Deep-learning models were trained to predict retinal sensitivity based on OCT structural information.
  • En face sensitivity maps were created, and model performance was evaluated against traditional microperimetry results.

Main Results:

  • A deep-learning model achieved a mean absolute error of 3.36 dB in estimating retinal sensitivity, outperforming linear regression and a LeNet model.
  • The model demonstrated a high degree of agreement with observed sensitivity (Pearson correlation r=0.78).
  • Generated high-resolution en face maps accurately delineated functionally healthy and impaired retinal areas.

Conclusions:

  • The developed deep-learning model successfully created high-resolution en face maps of estimated retinal sensitivity in MacTel patients.
  • These maps offer superior resolution compared to traditional microperimetry.
  • The model shows potential as an objective surrogate outcome measure for monitoring disease progression in clinical trials.