June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Deep-learning-aided Detection of Referable and Vision Threatening Diabetic Retinopathy based on Structural and Angiographic Optical Coherence Tomography
Author Affiliations & Notes
  • Pengxiao Zang
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
    Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States
  • Tristan T. Hormel
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Yukun Guo
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Xiaogang Wang
    Shanxi Eye Hospital, Taiyuan, Shanxi, China
  • Christina J Flaxel
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Steven Bailey
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Thomas S Hwang
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Yali Jia
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
    Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Pengxiao Zang, None; Tristan Hormel, None; Yukun Guo, None; Xiaogang Wang, None; Christina Flaxel, None; Steven Bailey, None; Thomas Hwang, None; Yali Jia, Optovue (F), Optovue (P)
  • Footnotes
    Support  National Institutes of Health (R01 EY027833, R01 EY024544, P30 EY010572); Unrestricted Departmental Funding Grant and William & Mary Greve Special Scholar Award from Research to Prevent Blindness (New York, NY).
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 2116. doi:
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    • Get Citation

      Pengxiao Zang, Tristan T. Hormel, Yukun Guo, Xiaogang Wang, Christina J Flaxel, Steven Bailey, Thomas S Hwang, Yali Jia; Deep-learning-aided Detection of Referable and Vision Threatening Diabetic Retinopathy based on Structural and Angiographic Optical Coherence Tomography. Invest. Ophthalmol. Vis. Sci. 2021;62(8):2116.

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

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Abstract

Purpose : We propose two fully automated deep-learning-aided frameworks for detecting referable and vision threatening DR (rDR and vtDR) from volumetric and en face structural and angiographic optical coherence tomography (OCT) data.

Methods : 3×3-mm macular OCTA scans were acquired from the eyes of 50 healthy participants and 293 patients with diabetes (with or without DR) using a spectral-domain OCTA system (Avanti RTVue-XR, Optovue Inc). Masked trained retina specialists graded the disease severity based on the Early Treatment of Diabetic Retinopathy Study (ETDRS) scale using 7-field fundus photography. rDR was defined as level 35 or worse, or any level with diabetic macular edema (DME). vtDR was defined as level 53 or worse, or level with DME. A 3D (EfficientNet-3D-B0) and a 2D (DcardNet-36) frameworks were constructed. Each was trained separately for rDR and vtDR. The rDR/vtDR detection performance of 3D and 2D frameworks were respectively optimized using the area under the receiver operating characteristic curve (AUC) and specificity at sensitivity over 95%. Class activation maps (CAMs) were also generated from each framework. Performance was evaluated with 5-fold cross-validation, with 60%, 20%, and 20% of the data split for training, validation, and testing, respectively.

Results : The 3D framework achieved AUC of 0.89±0.04 for rDR and 0.88±0.02 for vtDR. The 2D framework superior AUC of 0.95±0.02 for rDR (p=0.0130) and 0.94±0.02 for vtDR (p=0.0009). 2D framework demonstrated higher overall diagnostic accuracy, sensitivity, and AUC (t-test on 5-fold cross-validation output) (Table 1). By overlaying CAM on OCTA three-dimensionally, we found the regions with higher values in the CAMs were mostly focused on the non-perfusion areas (NPA) and fluid areas (Fig. 1).

Conclusions : We demonstrated that a deep-learning based framework can detect rDR and vtDR based on OCTA volume and 2D framework shows superior diagnostic power than 3D framework as trained by the currently available dataset.

This is a 2021 ARVO Annual Meeting abstract.

 

 

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