Skip to content

Detecting Dementia in the Retina Using Optical Coherence Tomography

Detecting Dementia in the Retina: a Big Data Machine Learning Approach

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT03631069
Enrollment
280000
Registered
2018-08-15
Start date
2019-09-01
Completion date
2022-08-01
Last updated
2021-09-17

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

Conditions

Dementia, Dementia Alzheimers, Dementia Senile, Dementia, Vascular

Brief summary

This retrospective case control explores the retinal features of dementia associated with neurodegenerative diseases, particularly Alzheimer's disease. By linking a pseudonymised dataset of three-dimensional retinal scans, called optical coherence tomography, with nationally held data on dementia, corresponding characteristics will be evaluated through descriptive statistics and machine learning techniques.

Detailed description

By 2025, it is estimated that approximately 1 million people in the United Kingdom (UK) will suffer from dementia, a syndrome associated with progressive decline in brain function. While there is currently no cure for most types of dementia, early diagnosis can help patients receive the appropriate treatment and support to help maintain mental function. The focus of this project is to identify changes in retinal structure associated with dementia. In collaboration with bioinformatics experts at University College London (UCL), the investigators propose to analyse our repository of \>1 million retinal scans, termed optical coherence tomography (OCT), performed regularly on patients since 2008. OCT scans will be linked at a patient level to data from the Hospital Episode Statistics (HES) database to identify those who have been diagnosed with dementia or went on the develop dementia. Thus, a pseudonymised classified dataset of retinal scans will be generated for qualitative and quantitative analysis. The primary objective is to characterise changes in the layers of the retina associated with dementia. Machine learning techniques may also be employed to identify novel patterns of retinal change associated with dementia.

Interventions

None listed

Sponsors

Moorfields Eye Hospital NHS Foundation Trust
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
40 Years to No maximum
Healthy volunteers
Yes

Inclusion criteria

All patients over the age of 40 who have undergone an optical coherence tomography scan at Moorfields Eye Hospital NHS Foundation Trust

Exclusion criteria

Poor quality image

Design outcomes

Primary

MeasureTime frame
Retinal nerve fiber layer thickness1 year
Ganglion cell layer thickness1 year
Macular volume1 year

Countries

United Kingdom

Outcome results

None listed

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