Facial Prediction Technology for Edentulous Patients

Sponsor

KU Leuven (Other)

Overall Status

Recruiting

CT.gov ID

NCT06080633

Collaborator

(none)

Enrollment

Location

Anticipated Duration (Months)

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

According to data from the World Health Organization, approximately 160 million people worldwide are edentulous. The incidence increases with age, and the proportion of edentulous patients is higher in the population aged 60 and above. Loss of teeth or edentulism can affect facial appearance, causing people to feel self-conscious and loss confidence in social situations, and even lead to psychological illnesses. Therefore, edentulous patients not only pay close attention to the recovery of oral function but also attach great importance to facial contour improvement. For a long time, due to technological limitations, clinicians have been unable to depict the changes in facial contour after implant placement for patients before surgery. However, with the development of artificial intelligence technology, deep learning-based methods for predicting soft tissue facial deformation have made this mission a possibility. This study established a multi-modal dataset for edentulous patients before and after implant restoration to lay the foundation for predicting facial contour changes after implant treatment. A graph generative adversarial network based on multi-modal data was proposed to achieve fast and high-precision facial contour prediction. To address the common challenges of slow computation and excessive computational resource consumption in current triangular mesh deformation simulation methods, this project innovatively proposed a graph generative adversarial network that uses multi-modal data and incorporates self-attention mechanisms to achieve fast and high-precision facial contour prediction for edentulous patients after implant restoration.

Condition or Disease	Intervention/Treatment	Phase
Edentulous Jaw

Study Design

Study Type:

Observational

Anticipated Enrollment :

24 participants

Observational Model:

Case-Only

Time Perspective:

Retrospective

Official Title:

Research on Facial Prediction Technology for Edentulous Implant-Supported Fixed Prostheses Based on Multimodal Data Fusion

Actual Study Start Date :

Jan 1, 2023

Anticipated Primary Completion Date :

Dec 1, 2023

Anticipated Study Completion Date :

Dec 1, 2024

Outcome Measures

Primary Outcome Measures

Changes in Soft Tissue Volume in the Lip Region after Implant Dentistry [Between pre-operation and after Implant-Supported Fixed Prostheses up to 3 months]
Quantitative analysis of lip volume changes in patients after oral implant surgery using facial scanning equipment

Eligibility Criteria

Criteria

Ages Eligible for Study:

50 Years to 100 Years

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Yes

Inclusion Criteria:

Patients with complete edentulism,
aged 50 years or above,
in good physical health,

Exclusion Criteria:

patients who refuse to participate in the study，
patients who cannot undergo facial scanning.

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Hongyang Ma	Leuven	Heverlee	Belgium	3000

Sponsors and Collaborators

KU Leuven

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

Responsible Party:

Hongyang Ma, Research Associate, KU Leuven

ClinicalTrials.gov Identifier:

NCT06080633

Other Study ID Numbers:

S20230825

First Posted:

Oct 12, 2023

Last Update Posted:

Oct 12, 2023

Last Verified:

Oct 1, 2023

Studies a U.S. FDA-regulated Drug Product:

Studies a U.S. FDA-regulated Device Product:

Keywords provided by Hongyang Ma, Research Associate, KU Leuven

Additional relevant MeSH terms:

Jaw, Edentulous

Mouth, Edentulous

Study Results

No Results Posted as of Oct 12, 2023