Pilot Study on Deep Learning in the Eye (IDLE)

January 5, 2021 updated by: Pieter Nelis, CRG UZ Brussel

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

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.

Study Overview

Status

Unknown

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Anticipated)

120

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 100 years (Adult, Older Adult)

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Healthy and non-healthy subjects: extract data out of available images

Description

Inclusion Criteria:

  • Availability of images, which allow discrimination.

Exclusion Criteria:

  • No availability of clear data on disease differentiation

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
Validation of Image classification by transfer learning algorithm
Time Frame: 1 year
1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

CRG UZ Brussel

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

February 1, 2021

Primary Completion (Anticipated)

December 1, 2021

Study Completion (Anticipated)

December 1, 2022

Study Registration Dates

First Submitted

December 7, 2020

First Submitted That Met QC Criteria

December 7, 2020

First Posted (Actual)

December 11, 2020

Study Record Updates

Last Update Posted (Actual)

January 7, 2021

Last Update Submitted That Met QC Criteria

January 5, 2021

Last Verified

January 1, 2021

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • IDLE1000

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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