Facial Prediction Technology for Edentulous Patients

Edentulous Jaw Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06080633
• Other study ID #: S20230825
• Status: Recruiting
• Start date: June 1, 2023
• Est. completion date: December 1, 2026

Study information

• Verified date: June 2024
• Source: KU Leuven
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 24
• Est. completion date: December 1, 2026
• Est. primary completion date: December 1, 2025
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 50 Years to 100 Years

Eligibility

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.

Related Conditions & MeSH terms

• Edentulous Jaw
• Jaw, Edentulous
• Mouth, Edentulous

Locations

Country	Name	City	State
Belgium	Hongyang Ma	Leuven	Heverlee

Sponsors (1)

Lead Sponsor	Collaborator
KU Leuven

Country where clinical trial is conducted

Belgium, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Changes in Soft Tissue Volume in the Lip Region after Implant Dentistry	Quantitative analysis of lip volume changes in patients after oral implant surgery using facial scanning equipment	Between pre-operation and after Implant-Supported Fixed Prostheses up to 3 months