NCT04665102

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.

Trial Health

35
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
120

participants targeted

Target at P50-P75 for all trials

Timeline
Completed

Started Feb 2021

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

December 7, 2020

Completed
4 days until next milestone

First Posted

Study publicly available on registry

December 11, 2020

Completed
2 months until next milestone

Study Start

First participant enrolled

February 1, 2021

Completed
10 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 1, 2021

Completed
1 year until next milestone

Study Completion

Last participant's last visit for all outcomes

December 1, 2022

Completed
Last Updated

January 7, 2021

Status Verified

January 1, 2021

Enrollment Period

10 months

First QC Date

December 7, 2020

Last Update Submit

January 5, 2021

Conditions

Outcome Measures

Primary Outcomes (1)

  • Validation of Image classification by transfer learning algorithm

    1 year

Study Arms (2)

No pathology

Pathology

Other: Image classification using deep learning algorithm

Interventions

Image classification using deep learning algorithm

Pathology

Eligibility Criteria

Age18 Years - 100 Years
Sexall
Healthy VolunteersYes
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

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

You may qualify if:

  • Availability of images, which allow discrimination.

You may not qualify if:

  • No availability of clear data on disease differentiation

Contact the study team to confirm eligibility.

Sponsors & Collaborators

MeSH Terms

Conditions

Central Serous ChorioretinopathyDiabetic RetinopathyCataract

Condition Hierarchy (Ancestors)

Retinal DiseasesEye DiseasesDiabetic AngiopathiesVascular DiseasesCardiovascular DiseasesDiabetes ComplicationsDiabetes MellitusEndocrine System DiseasesLens Diseases

Central Study Contacts

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Researcher

Study Record Dates

First Submitted

December 7, 2020

First Posted

December 11, 2020

Study Start

February 1, 2021

Primary Completion

December 1, 2021

Study Completion

December 1, 2022

Last Updated

January 7, 2021

Record last verified: 2021-01

Data Sharing

IPD Sharing
Will not share