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Deep Learning in the Detection and Prediction of Hydroxychloroquine Maculopathy

Deep Learning in the Detection and Prediction of Hydroxychloroquine Maculopathy

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06839443
Acronym
PLAQUINAI
Enrollment
100
Registered
2025-02-21
Start date
2024-08-01
Completion date
2025-06-20
Last updated
2025-02-21

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Hidroxicloroquine Intake

Keywords

Hidroxicloroquine maculopathy, Hidroxicloroquine toxicity

Brief summary

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.

Detailed description

HCQ is one of the most prescribed drugs worldwide. Originally developed for the treatment and prevention of malaria, it was soon proven effective for several other non-related disorders, most commonly non-organ specific autoimmune diseases. The use of HCQ in patients with systemic lupus erythematosus (SLE), for example, is estimated at 50%, increasing to 90% in specialized centers. These numbers are likely to increase following the LUMINA study that found a survival benefit of SLE patients on HCQ. In recent years, HCQ indications have been expanding to other medical areas such as dermatological disorders and oncology. Adding to the growing list of clinical indications, there are other factors that increase HCQ prescription such as a favourable safety profile and the possibility of adjunctive use with primary therapies, leading to a growing confidence amongst physicians to start treatment. But HCQ is not an innocuous drug. There are several HCQ related adverse effects, either from acute or chronic intake, in particular in the nervous and cardiovascular system. It is estimated that around 1.8-7.5% of patients with more than 5 years of HCQ therapy suffer from retinal toxicity, with prevalence increasing to 20% after 20 years. Other known risk factors for toxicity are a higher dose, kidney failure and concomitant tamoxifen use and it has been suggested that previous macular pathology and genetic factors should be taken in account when calculating disease probability. Due to increase in HCQ usage, HCQ maculopathy and subsequent screening has become a public health problem. Although toxicity is directly related to drug use, retinal degeneration might continue despite drug cessation, which adding to the increase of users empathises the need for early disease detection. HCQ maculopathy screening guidelines differ slightly from country to country and have been evolving over time. The American Academy of Ophthalmology recommends baseline examination with OCT, autofluorescence and visual fields with annual review after 5 years unless there are other risk factors. On the other hand, the Royal College of Ophthalmology currently suggests annual monitoring 5 years after drug therapy with OCT and Widefield FAF unless there are other risk factors. Deep learning techniques are paving the way in image-centric specialities and promise to lower healthcare costs and increase accuracy when compared to current methods. In ophthalmology, systems are being developed for the detection of diabetic retinopathy, glaucoma and age-related macular degeneration with high sensitivity and sensibility. We believe that using RETINAI technology and a sequential analysis of HCQ toxicity patients, a higher prediction model could be achieved. Three blinded readers will validate data as controls or toxicity.

Interventions

DIAGNOSTIC_TESTRetinopathy group

OCT scans and Retinai algorithm will be performed

DIAGNOSTIC_TESTControl group

OCT scans and Retinai algorithm will be performed

Sponsors

Centro Hospitalar de Lisboa Central
Lead SponsorOTHER

Study design

Observational model
CASE_CONTROL
Time perspective
CROSS_SECTIONAL

Eligibility

Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
Yes

Inclusion criteria

* Patients with \> 10 years of HCQ intake

Exclusion criteria

* Patients with ocular diseases that might mimic HCQ maculopathy or interfer with HCQ maculopathy screening

Design outcomes

Primary

MeasureTime frameDescription
To develop an automated screening method in hydroxychloroquine (HCQ) screening based on OCT featuresOne yearWe hypothesize that HCQ toxicity can be detected with a deep learning system with OCT

Secondary

MeasureTime frameDescription
To explore early changes of toxicity in HCQ-user patients using deep learningOne yearWe hypothesize that deep learning in OCT might provide an accurate tool for the early detection of HCQ toxicity

Countries

Portugal

Contacts

Primary ContactRita Anjos, MD
RITA.Anjos@gmail.com+351 914535963
Backup ContactAna Luisa Basílio, MD
a.luisabasilio@gmail.com+351 96557040

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026