NCT06080633|Recruiting
Facial Prediction Technology for Edentulous Patients
Research on Facial Prediction Technology for Edentulous Implant-Supported Fixed Prostheses Based on Multimodal Data Fusion
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Research on Facial Prediction Technology for Edentulous Implant-Supported Fixed Prostheses Based on Multimodal Data Fusion
Sponsor study identifier
observational
24
1 country
1
Study Start
June 1, 2023
Actual (reported)Primary Completion
December 1, 2025
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
Study Completion
December 1, 2026
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
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.
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
December 1, 2026
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
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Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
June 1, 2023
Actual (reported)August 29, 2023
Actual (reported)October 12, 2023
Actual (reported)December 1, 2025
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
December 1, 2026
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
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
Patient with Edentulous Maxilla after Implant Prosthetic Restoration
Hongyang Ma
Leuven, Heverlee, 3000, Belgium
First Submitted
August 29, 2023
First Posted
October 12, 2023
Study Start
June 1, 2023
Actual (reported)Primary Completion
December 1, 2025
Estimated (date passed)Monolithic Zirconia Full-Arch Rehabilitation Using Digital Technology
Contact the study team to confirm eligibility.
This estimate has passed. The registry has not confirmed an actual date for this milestone.
Study Completion
December 1, 2026
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Last Updated
June 13, 2024
Record last verified: 2024-06
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Source: ClinicalTrials.gov.
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