June 2023
Volume 64, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2023
Automated identification of geographic atrophy eyes with and without subfoveal involvement using machine learning and real-world ophthalmic images in the IRIS Registry
Author Affiliations & Notes
  • Zhongdi Chu
    Verana Health, California, United States
  • Theodore Leng
    Byers Eye Institute, Stanford University School of Medicine, Stanford, California, United States
    Verana Health, California, United States
  • Durga S Borkar
    Duke Eye Center, Duke University, Durham, North Carolina, United States
  • Carlos Candano
    Verana Health, California, United States
  • Kim Le
    Verana Health, California, United States
  • Aishwarya Ramakrishnan
    Verana Health, California, United States
  • Ketki Khapare
    Verana Health, California, United States
  • Michael Mbagwu
    Verana Health, California, United States
    Byers Eye Institute, Stanford University School of Medicine, Stanford, California, United States
  • Aracelis Torres
    Verana Health, California, United States
  • Footnotes
    Commercial Relationships   Zhongdi Chu Verana Health, Code E (Employment); Theodore Leng Verana Health, Code C (Consultant/Contractor); Durga Borkar Verana Health, Code C (Consultant/Contractor); Carlos Candano Verana Health, Code E (Employment); Kim Le Verana Health, Code E (Employment); Aishwarya Ramakrishnan Verana Health, Code E (Employment); Ketki Khapare Verana Health, Code E (Employment); Michael Mbagwu Verana Health, Code E (Employment); Aracelis Torres Verana Health, Code E (Employment)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2023, Vol.64, 261. doi:
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      Zhongdi Chu, Theodore Leng, Durga S Borkar, Carlos Candano, Kim Le, Aishwarya Ramakrishnan, Ketki Khapare, Michael Mbagwu, Aracelis Torres; Automated identification of geographic atrophy eyes with and without subfoveal involvement using machine learning and real-world ophthalmic images in the IRIS Registry. Invest. Ophthalmol. Vis. Sci. 2023;64(8):261.

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

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Abstract

Purpose :
To develop a machine learning (ML)-aided pipeline to identify eyes with geographic atrophy (GA) that have subfoveal involvement and eyes without subfoveal involvement using real-world optical coherence tomography (OCT) images linked to the American Academy of Ophthalmology IRIS® Registry (Intelligent Research in Sight).

Methods :
The IRIS Registry is the nation’s first comprehensive eye clinical database, with over 70% of US ophthalmologists contributing. A ML-aided pipeline was developed using a de-identified clinical-imaging dataset of patients with dry age-related macular degeneration (AMD) identified through ICD-9/10 codes from 2006 to 2022. Images were contributed by 3 different practices. Previously developed ML models that identify eyes with GA secondary to AMD were used to identify eyes with GA. From eyes with GA, a training and testing set were selected using patient-level stratified sampling (age, sex, and race) and labeled by two fellowship-trained retinal specialists. A three round consensus workflow was implemented to ensure both graders agreed on all labels, with the center point of the fovea being involved as the definition of subfoveal involvement. A deep learning model was developed to classify GA with subfoveal involvement from GA without subfoveal involvement, trained on the training set with an 80:20 split between training and validation.

Results :
In total, 265 OCT volumetric images from 256 eyes of 232 patients were labeled. A training set of 178 images, a validation set of 45 images and a testing set of 42 images were randomly split (stratified by subfoveal involvement status) where no patients were included in more than one set. A modified VGG19 network was developed.The model achieved an accuracy of 87%, 84% and 79% for training, validation and testing, respectively. In the testing set, the model achieved a precision of 0.79, a recall of 0.79 and a F-1 score of 0.78.

Conclusions :
The proposed pipeline demonstrated satisfactory performance identifying GA eyes with and without subfoveal involvement using images collected in real world practice. It could potentially be useful in screening patients for GA trials and identifying patients for future GA treatments.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

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