Deep Learning Algorithm for the Diagnosis and Prediction of Hydroxychloroquine Retinopathy: An International, Multi-institutional Study.

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.