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Lookup NU author(s): Dr Jeffry Hogg, Dr Hani Hasan, James Talks
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025 American Academy of Ophthalmology. Purpose: We present a deep learning algorithm—HCQuery—that detects the presence of hydroxychloroquine retinopathy and predicts its future occurrence from spectral-domain OCT (SD-OCT) images. Design: We trained and validated a deep learning algorithm using retrospective SD-OCT images from patients taking hydroxychloroquine. Subjects: The study involved a retrospective, nonconsecutive collection of 409 patients (171 positive for hydroxychloroquine retinopathy and 238 negative) and 8251 SD-OCT b-scans (1988 volumes) from 5 independent international clinical locations. Methods: Imaging macular volumes from 2 different SD-OCT devices (Heidelberg Spectralis and Zeiss Cirrus) at 2 clinical sites were used to train and validate a convolutional neural network (EfficientNet-b4) to produce a likelihood of retinopathy score for each SD-OCT b-scan. Likelihood of retinopathy score were processed across SD-OCT volumes for an eye-level and patient-level binary decision output for the presence or absence of retinopathy. The adjudicated consensus of ≤3 independent retina specialists using patient clinical data and multimodal testing served as the reference standard for hydroxychloroquine retinopathy. The algorithm was tested on 4 withheld test sets, 1 internal (data set 1), and 3 external (data sets 3–5). The test sets were obtained in 2 countries (United States and United Kingdom) and represented 2 SD-OCT devices each with diverse acquisition parameters. Main Outcome Measures: Sensitivity, specificity, accuracy, negative predictive value, positive predictive value, area under the receiver-operator characteristic curve, and area under the precision-recall curve for the detection of hydroxychloroquine retinopathy either at the time of clinical diagnosis or ≤18 months in advance of clinical diagnosis. Results: The algorithm discriminated hydroxychloroquine retinopathy at the time of clinical diagnosis as well as in advance of clinical diagnosis (mean: 220.8 days before clinical diagnosis; accuracy: 0.987 [95% CI: 0.962–1.00]; sensitivity: 1.00 [95% CI: 0.833–1.00]; specificity: 0.983 [95% CI: 0.952–1.00]; positive predictive value: 0.944 [95% CI: 0.836–1.00]; negative predictive value: 1.00 [95% CI: 0.937–1.00]). For eyes that developed retinopathy, it was identified as positive 2.74 years in advance of the clinical diagnosis on average. Conclusions: Our algorithm can detect retinopathy at all stages of disease, as well as predict retinopathy years in advance of clinical diagnosis. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
Author(s): Woodward-Court P, Hogg J, Lee T, Taribagil P, Zhao CS, Otti V, Tucker WR, Allingham M, Alekseev O, Wagner SK, Myung D, Leung L-S, Lad EM, Hasan H, Talks J, Alexander DC, Keane PA, Dow ER
Publication type: Article
Publication status: Published
Journal: Ophthalmology Retina
Year: 2025
Pages: Epub ahead of print
Online publication date: 11/06/2025
Acceptance date: 05/06/2025
Date deposited: 01/09/2025
ISSN (print): 2468-7219
ISSN (electronic): 2468-6530
Publisher: Elsevier Inc.
URL: https://doi.org/10.1016/j.oret.2025.06.003
DOI: 10.1016/j.oret.2025.06.003
Data Access Statement: Supplementary data is available alongside the article on the publisher's page.
PubMed id: 40513830
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