Abstract

Purpose: Patients of the Los Angeles County (LAC) Department of Health Services (DHS) teleretinal program currently undergo screening for referable glaucoma based on manual grading of fundus photographs by trained LAC DHS optometrists. In this study, we develop and test a deep learning (DL) algorithm to automate the detection process and assess its performance compared to a panel of 12 ophthalmologists.
Methods: Fundus photographs and labels of referable glaucoma were obtained from the LAC DHS teleretinal screening program. Patient-level labels of referable glaucoma were generalized to images from both eyes. A convolutional neural network (CNN) was developed with VGG-19 architecture using separate training and validation datasets. Area under curve (AUC), accuracy, sensitivity, and specificity were calculated to assess algorithm performance using an independent test set graded by 12 ophthalmologists (1 to 15 years of clinical experience). Reference labels were provided by either LAC DHS optometrists (LAC DHS reference labels) or the majority of three glaucoma specialists (expert reference labels).
Results: 12,098 images from 5,616 patients (3,540 referable glaucoma, 2,076 non-glaucoma) were used to develop the DL algorithm. In this dataset, mean age was 56.9 years with 54.9% females and 67.6% Latinos, 9.0% Blacks, 2.7% Caucasians, 6.5% Asians, and 11.2% Other or Unspecified. 1,000 images from 500 patients (250 referable glaucoma, 250 non-glaucoma) were used to test the DL algorithm. In this dataset, mean age was 57.3 years with 52.4% females and 69.2% Latinos, 8.6% Blacks, 5.2% Asians, and 11 …

Metadata

publication

Investigative Ophthalmology & Visual Science 65 (7), 372-372, 2024

year

2024

publication date

2024/6/17

authors

Haroon Rasheed, Sreenidhi Iyengar, Van Nguyen, Zhiwei Li, Aniket Kumar, Aidan Lee, Andrew Duong, Brandon Wong, Jeffrey Gluckstein, Kendra Hong, Kent Nguyen, Rahul Dhodapkar, Carl Kesselman, Lauren Daskivich, Michael Pazzani, Benjamin Y Xu

link

https://iovs.arvojournals.org/article.aspx?articleid=2796242

resource_link

https://iovs.arvojournals.org/article.aspx?articleid=2796242

journal

Investigative Ophthalmology & Visual Science

volume

65

issue

7

pages

372-372

publisher

The Association for Research in Vision and Ophthalmology