June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Transfer Learning from Optical Coherence Tomography Images of MacTel Type II to Predict Retinal Sensitivity for Early-Intermediate Age-Related Macular Degeneration
Author Affiliations & Notes
  • Randy Lu
    Ophthalmology, University of Washington, Seattle, Washington, United States
  • Yuka Kihara
    Ophthalmology, University of Washington, Seattle, Washington, United States
  • Julia Owen
    Ophthalmology, University of Washington, Seattle, Washington, United States
  • Zhichao Wu
    Centre for Eye Research Australia Ltd, East Melbourne, Victoria, Australia
  • Cecilia S Lee
    Ophthalmology, University of Washington, Seattle, Washington, United States
  • Catherine A Egan
    Moorfields Eye Hospital NHS Foundation Trust, London, London, United Kingdom
  • Robyn H Guymer
    Centre for Eye Research Australia Ltd, East Melbourne, Victoria, Australia
  • Aaron Y Lee
    Ophthalmology, University of Washington, Seattle, Washington, United States
  • Footnotes
    Commercial Relationships   Randy Lu None; Yuka Kihara None; Julia Owen None; Zhichao Wu None; Cecilia Lee None; Catherine Egan None; Robyn Guymer None; Aaron Lee Genentech, Verana Health, Johnson and Johnson, Gyroscope, Code C (Consultant/Contractor), US Food and Drug Administration, Code E (Employment), Santen, Carl Zeiss Meditec, Novartis, Microsoft, NVIDIA, Code F (Financial Support), Topcon, Code R (Recipient)
  • Footnotes
    Support  NIH/NEI K23EY029246, NIH/NIA U19AG066567, NIH/NIA R01AG060942, RPB unrestricted funding, Latham Vision Grant, Karalis Johnson Retina Center
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 174 – F0021. doi:
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    • Get Citation

      Randy Lu, Yuka Kihara, Julia Owen, Zhichao Wu, Cecilia S Lee, Catherine A Egan, Robyn H Guymer, Aaron Y Lee; Transfer Learning from Optical Coherence Tomography Images of MacTel Type II to Predict Retinal Sensitivity for Early-Intermediate Age-Related Macular Degeneration. Invest. Ophthalmol. Vis. Sci. 2022;63(7):174 – F0021.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Microperimetry (MP) is a valuable endpoint in clinical trials for various retinal diseases. Deep learning models that can predict MP from optical coherence tomography (OCT) B-scans may be beneficial given the laborious effort of MP. Transfer learning involves using pre-trained weights on a different but related problem where proficiency in one task may generalize to another. We sought to evaluate a deep learning model pre-trained on OCTs of macular telangiectasia type 2 (MacTel) and fine-tuned on patients with non neovascular age-related macular degeneration (AMD).

Methods : The AMD dataset includes 322 eyes of 161 patients enrolled in the Laser Intervention in the Early Stages of AMD (LEAD) trial at the Centre for Eye Research Australia from 2013 to 2018, partitioned into 110,450 samples for training, 52,858 for validation, and 41,719 for testing. Each sample consists of B-scan window slices and MP sensitivities, for which infrared fundus photos were registered by aligning segmented blood vessels. Three VGGNet models were evaluated: 1) trained on MacTel data (VGG-Mac), 2) trained on AMD data (VGG-AMD), 3) pre-trained with MacTel data and fine-tuned with AMD data (VGG-FT). Primary outcome is the mean absolute error (MAE) of predicted vs. observed MP sensitivity.

Results : All models were evaluated against the same AMD validation set. The fine-tuned model VGG-FT achieved lowest MAE of 2.88 dB (95% CI 2.86, 2.91). VGG-AMD achieved MAE of 3.01 dB (2.99, 3.04), and VGG-Mac achieved MAE of 3.44 dB (3.41, 3.48). Continuous predictions by VGG-FT across B-scan levels were then aggregated to create 2D en-face maps (Figure 2).

Conclusions : The model pretrained on MacTel then fine-tuned on AMD data outperformed the model trained only on AMD data, suggesting that transfer learning between different retinal diseases may increase domain specific performance. Deep learning models to estimate retinal sensitivities using OCTs may be a valuable endpoint in following patients with AMD for clinical trials.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

Bar graph showing predicted MP sensitivity vs. ground truth sensitivity for all deep learning models.

Bar graph showing predicted MP sensitivity vs. ground truth sensitivity for all deep learning models.

 

Ground truth MP sensitivity (a) is overlaid with en-face maps generated from model predictions (b) with the red regions indicating lower predicted sensitivity. Continuous prediction along a B-scan is shown (c) for the level of the green line (a).

Ground truth MP sensitivity (a) is overlaid with en-face maps generated from model predictions (b) with the red regions indicating lower predicted sensitivity. Continuous prediction along a B-scan is shown (c) for the level of the green line (a).

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