Hydroxychloroquine retinal toxicity affects a significant number of patients using this medication. Detection of toxicity is difficult in the early stages of the disease and depends on the subjectivity of the clinician who reads the tests (optical coherence tomography, autofluorescence and visual fields). Automating the reading of these diagnostic exams could lead to earlier detection of this pathology and reduce the burden associated with interpreting these exams in the ophthalmology service. The images that are usually taken in the screening and monitoring of hydroxychloroquine toxicity by will be collected - photography of the ocular fundus and optical coherence tomography with autofluorescence.
Patients with > 10 years of HCQ intake
Patients with ocular diseases that might mimic HCQ maculopathy or interfer with HCQ maculopathy screening
Patients with HCQ intake
Case-control
Cross-sectional
Retinopathy group
Diagnostic testOCT scans and Retinai algorithm will be performed
Control group
Diagnostic testOCT scans and Retinai algorithm will be performed
To develop an automated screening method in hydroxychloroquine (HCQ) screening based on OCT features
We hypothesize that HCQ toxicity can be detected with a deep learning system with OCT
To explore early changes of toxicity in HCQ-user patients using deep learning
We hypothesize that deep learning in OCT might provide an accurate tool for the early detection of HCQ toxicity
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