Pilot Study on Deep Learning in the Eye

Validation of a Transfer Learning Deep Learning Algorithm for Image Classification in Multiple Pathologies

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04665102

Acronym

IDLE

Enrollment

120

Registered

2020-12-11

Start date

2021-02-01

Completion date

2022-12-01

Last updated

2021-01-07

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

Conditions

Cataract, Central Serous Chorioretinopathy, Diabetic Retinopathy

Brief summary

Deep learning allows you to classify images using a self-learning algorithm. Transfer learning builds on an existing self-learning algorithm to enable image classification with fewer images. In this study, this technique will be applied to different image modalities in different syndromes. Retrospective study design.

Interventions

OTHERImage classification using deep learning algorithm

Image classification using deep learning algorithm

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 100 Years

Healthy volunteers

Yes

Inclusion criteria

* Availability of images, which allow discrimination.

Exclusion criteria

* No availability of clear data on disease differentiation

Design outcomes

Primary

Measure	Time frame
Validation of Image classification by transfer learning algorithm	1 year

Contacts

Primary ContactPieter Nelis

nelispieter@gmail.com+32494354198

Outcome results

None listed

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