Pilot Study on Deep Learning in the Eye

NCT ID: NCT04665102

Last Updated: 2021-01-07

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

120 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-02-01

Study Completion Date

2022-12-01

Brief Summary

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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

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Conditions

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Central Serous Chorioretinopathy Diabetic Retinopathy Cataract

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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No pathology

No interventions assigned to this group

Pathology

Image classification using deep learning algorithm

Intervention Type OTHER

Image classification using deep learning algorithm

Interventions

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Image classification using deep learning algorithm

Image classification using deep learning algorithm

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Availability of images, which allow discrimination.

Exclusion Criteria

* No availability of clear data on disease differentiation
Minimum Eligible Age

18 Years

Maximum Eligible Age

100 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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CRG UZ Brussel

OTHER

Sponsor Role lead

Responsible Party

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Pieter Nelis

Researcher

Responsibility Role PRINCIPAL_INVESTIGATOR

Central Contacts

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Pieter Nelis

Role: CONTACT

+32494354198

Other Identifiers

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IDLE1000

Identifier Type: -

Identifier Source: org_study_id

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