IDLE: Pilot Study on Deep Learning in the Eye

Sponsor

CRG UZ Brussel (Other)

Overall Status

Not yet recruiting

CT.gov ID

NCT04665102

Collaborator

(none)

120

Enrollment

21.9

Anticipated Duration (Months)

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
Central Serous Chorioretinopathy Diabetic Retinopathy Cataract	Other: Image classification using deep learning algorithm

Study Design

Study Type:

Observational

Anticipated Enrollment :

120 participants

Observational Model:

Cohort

Time Perspective:

Retrospective

Official Title:

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

Anticipated Study Start Date :

Feb 1, 2021

Anticipated Primary Completion Date :

Dec 1, 2021

Anticipated Study Completion Date :

Dec 1, 2022

Arms and Interventions

Arm	Intervention/Treatment
No pathology
Pathology	Other: Image classification using deep learning algorithm Image classification using deep learning algorithm

Arm

Intervention/Treatment

No pathology

Pathology

Other: Image classification using deep learning algorithm

Image classification using deep learning algorithm

Outcome Measures

Primary Outcome Measures

Validation of Image classification by transfer learning algorithm [1 year]

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years to 100 Years

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Yes

Inclusion Criteria:

Availability of images, which allow discrimination.

Exclusion Criteria:

No availability of clear data on disease differentiation

Contacts and Locations

Locations

No locations specified.

Sponsors and Collaborators

CRG UZ Brussel

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

Responsible Party:

Pieter Nelis, Researcher, CRG UZ Brussel

ClinicalTrials.gov Identifier:

NCT04665102

Other Study ID Numbers:

IDLE1000

First Posted:

Dec 11, 2020

Last Update Posted:

Jan 7, 2021

Last Verified:

Jan 1, 2021

Individual Participant Data (IPD) Sharing Statement:

Plan to Share IPD:

Studies a U.S. FDA-regulated Drug Product:

Studies a U.S. FDA-regulated Device Product:

Product Manufactured in and Exported from the U.S.:

Additional relevant MeSH terms:

Cataract

Diabetic Retinopathy

Central Serous Chorioretinopathy

Study Results

No Results Posted as of Jan 7, 2021