June 2023
Volume 64, Issue 9
Open Access
ARVO Imaging in the Eye Conference Abstract  |   June 2023
Etiology of Macular Edema Defined by Deep Learning in Optical Coherence Tomography Scans
Author Affiliations & Notes
  • Fabio Daniel Padilla-Pantoja
    Universidad Nacional de Colombia, Bogota, Bogota, Colombia
  • Bernardo Alfonso Quijano Nieto
    Universidad Nacional de Colombia, Bogota, Bogota, Colombia
  • Oscar Julian Perdomo Charry
    Universidad del Rosario, Bogota, Cundinamarca, Colombia
  • Yeison David Sanchez Legarda
    Universidad Nacional de Colombia, Bogota, Bogota, Colombia
  • Fabio Augusto Gonzalez Osorio
    Universidad Nacional de Colombia, Bogota, Bogota, Colombia
  • Footnotes
    Commercial Relationships   Fabio Daniel Padilla-Pantoja, None; Bernardo Quijano Nieto, None; Oscar Perdomo Charry, None; Yeison Sanchez Legarda, None; Fabio Gonzalez Osorio, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2023, Vol.64, PB0031. doi:
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      Fabio Daniel Padilla-Pantoja, Bernardo Alfonso Quijano Nieto, Oscar Julian Perdomo Charry, Yeison David Sanchez Legarda, Fabio Augusto Gonzalez Osorio; Etiology of Macular Edema Defined by Deep Learning in Optical Coherence Tomography Scans. Invest. Ophthalmol. Vis. Sci. 2023;64(9):PB0031.

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

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Abstract

Purpose : To develop an automated method based on deep learning (DL) to classify macular edema (ME) from the evaluation of optical coherence tomography (OCT) scans.

Methods : A total of 4230 images were obtained from data repositories of patients attended in an ophthalmology clinic in Colombia and two free open-access databases. They were annotated with four biomarkers (BMs) as intraretinal fluid, subretinal fluid, hyperreflective foci/tissue, and drusen. Then the scans were labeled as control or ocular disease among diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), and retinal vein occlusion (RVO) by two expert ophthalmologists. Our method was developed by following four consecutive phases: segmentation of BMs, the combination of BMs, feature extraction with convolutional neural networks to achieve binary classification for each disease, and, finally, multiclass classification of diseases and control images.

Results : The accuracy of our model for nAMD was 97%, and for DME, RVO, and control were 94%, 93%, and 93%, respectively. Area under curve values were 0.99, 0.98, 0.96, and 0.97, respectively. The mean Cohen's kappa coefficient for the multiclass classification task was 0.84.

Conclusions : The proposed DL model may identify OCT scans as normal and ME. In addition, it may classify its cause among three major exudative retinal diseases with high accuracy and reliability.

This abstract was presented at the 2023 ARVO Imaging in the Eye Conference, held in New Orleans, LA, April 21-22, 2023.

 

Heatmap visualization of regions considered by the model to perform disease classification. The original optical coherence tomography scan, manual annotation performed by expert ophthalmologists, and model prediction are shown from left to right to provide a visual comparison. (a) Detection of subretinal fluid, hyperreflective foci/tissue and drusen was highlighted for recognition of nAMD. (b) Detection of intraretinal fluid and diffuse hyperreflective tissue conducts to recognition of DME. (c) Macrocystoid spaces, subretinal fluid and hyperreflective foci are highlighted on RVO. (d) No fluid or biomarker was highlighted by the deep learning model in the control scan.

Heatmap visualization of regions considered by the model to perform disease classification. The original optical coherence tomography scan, manual annotation performed by expert ophthalmologists, and model prediction are shown from left to right to provide a visual comparison. (a) Detection of subretinal fluid, hyperreflective foci/tissue and drusen was highlighted for recognition of nAMD. (b) Detection of intraretinal fluid and diffuse hyperreflective tissue conducts to recognition of DME. (c) Macrocystoid spaces, subretinal fluid and hyperreflective foci are highlighted on RVO. (d) No fluid or biomarker was highlighted by the deep learning model in the control scan.

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