Pilot Study on Deep Learning in the Eye | NCT04665102

Studies
Pilot Study on Deep Learning in the Eye

NCT04665102

Sponsor

CRG UZ Brussel

Status

Unknown status

Last Update Posted

Jan 7, 2021Actual

Enrollment

120Estimated

Phase

Not provided

Conditions

Central Serous Chorioretinopathy
Diabetic Retinopathy
Cataract

Interventions

Image classification using deep learning algorithm

Countries

Not provided

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Jan 2021

Overall Recruitment Status or Expanded Access Status

Unknown status

Last Known Status

Not yet recruiting

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Feb 1, 2021Estimated

Primary Completion Date

Dec 1, 2021Estimated

Completion Date

Dec 1, 2022Estimated

First Submitted Date

Dec 7, 2020

First Submission Date that Met QC Criteria

Dec 7, 2020

First Posted Date

Dec 11, 2020Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Jan 5, 2021

Last Update Posted Date

Jan 7, 2021Actual

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

FDAAA801 Violation

Not provided

Description Module

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.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

120Estimated

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
No pathology
Pathology			Other: Image classification using deep learning algorithm

Interventions

Name	Type	Description	Arm Group Labels	Other Names
Image classification using deep learning algorithm	Other	Image classification using deep learning algorithm	Pathology

Outcomes Module

Primary Outcomes

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

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

Availability of images, which allow discrimination.

Exclusion Criteria:

No availability of clear data on disease differentiation

Accepts Healthy Volunteers

Yes

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

100 Years

Standard Ages

AdultOlder Adult

Study Population

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

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Pieter Nelis	Contact	+32494354198		nelispieter@gmail.com

Overall Officials

Not provided

Locations

Not provided

IPD Sharing Statement Module

Plan to Share IPD

Description

Not provided

Types

Not provided

Time Frame

Not provided

Access Criteria

Not provided

URL

Not provided

Condition Browse Module

MeSH Terms

ID	Term
D056833	Central Serous Chorioretinopathy
D003930	Diabetic Retinopathy
D002386	Cataract

Ancestor Terms

ID	Term
D012164	Retinal Diseases
D005128	Eye Diseases
D003925	Diabetic Angiopathies
D014652	Vascular Diseases

Browse Leaves

Not provided

Browse Branches

Not provided