Reconstruction Technology to Auxiliary Diagnosis and Guarantee Patient Privacy

• Sponsor: Sun Yat-sen University (Other)
• Overall Status: Recruiting
• CT.gov ID: NCT05058599
• Collaborator: (none)
• Enrollment: 400
• Location: 1
• Anticipated Duration (Months): 20.7
• Patients Per Site Per Month: 19.3

Study Details

Study Description

Brief 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.

Condition or Disease	Intervention/Treatment	Phase
3D-reconstruction; Patient Privacy; Deep Learning; Digital Medicine	Diagnostic Test: DM

Study Design

• Study Type: Observational
• Anticipated Enrollment: 400 participants
• Observational Model: Cohort
• Time Perspective: Prospective
• Official Title: Using a Reconstruction Technology With Facial Pathological Features to Auxiliary Diagnosis and Guarantee Patient Privacy
• Actual Study Start Date: May 10, 2020
• Anticipated Primary Completion Date: Sep 20, 2021
• Anticipated Study Completion Date: Jan 30, 2022

Arms and Interventions

Arm	Intervention/Treatment
facial videos dataset facial videos collected from Zhongshan Ophthalmic Center of Sun Yat-sen University.	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

Outcome Measures

Primary Outcome Measures

1. Diagnostic consistency [baseline]

   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.

Eligibility Criteria

Criteria

• Ages Eligible for Study: N/A and Older
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: Yes

Inclusion Criteria:

• The quality of facial images should be clinically acceptable.

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Zhongshan Ophthalmic Center	Guangzhou	Guangdong	China	510000

Sponsors and Collaborators

• Sun Yat-sen University

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: Haotian Lin, Clinical Professor, Sun Yat-sen University
• ClinicalTrials.gov Identifier: NCT05058599
• Other Study ID Numbers: 2021KYPJ77
• First Posted: Sep 28, 2021
• Last Update Posted: Sep 28, 2021
• Last Verified: Sep 1, 2021

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No

Study Results

No Results Posted as of Sep 28, 2021