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NCT05058599 Clinical Trial

Reconstruction Technology to Auxiliary Diagnosis and Guarantee Patient Privacy

Deep Learning Clinical Trial

Official title:
Using a Reconstruction Technology With Facial Pathological Features to Auxiliary Diagnosis and Guarantee Patient Privacy

Clinical Trial Details — Status: Recruiting

Administrative data
NCT number NCT05058599
Other study ID # 2021KYPJ77
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date May 10, 2020
Est. completion date January 30, 2022

Study information
Verified date September 2021
Source Sun Yat-sen University
Contact Haotian Lin
Phone 13802793086
Email gddlht@aliyun.com
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Medical data that contain facial images are particularly sensitive as they retain important personal biometric identity, privacy protection. We developed a novel technology called "Digital Mask" (DM), based on real-time three-dimensional (3D) reconstruction and deep learning algorithm, to extract disease-relevant features but remove patient identifiable features from facial images of patients.

Recruitment information / eligibility
Status Recruiting
Enrollment 400
Est. completion date January 30, 2022
Est. primary completion date September 20, 2021
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group N/A and older
Eligibility Inclusion Criteria: - The quality of facial images should be clinically acceptable.

Study Design

Related Conditions & MeSH terms

Intervention

Diagnostic Test:
• DM
A new technology based on 3D reconstruction and deep learning algorithm to irreversibly erase the biometric attributes whilst retaining the clinical attributes needed for diagnosis and management

Locations
Country Name City State
China Zhongshan Ophthalmic Center Guangzhou Guangdong

Sponsors (1)
Lead Sponsor Collaborator
Sun Yat-sen University

Country where clinical trial is conducted

China, 

Outcome
Type Measure Description Time frame Safety issue
Primary Diagnostic consistency For each eye, both the diagnosis from the original videos and the diagnosis from the DM-reconstructed videos were recorded and compared. If the two diagnoses were consistent, it suggests that the reconstruction would be precise enough in clinical practice. baseline
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