Assessment of Accuracy and Aesthetics Following Automated Mandibular Defect Reconstruction Using AI

Not Applicable

Not yet recruiting

• Conditions: Mandibular Tumor

• Registration Number: NCT06945692
• Lead Sponsor: Cairo University

Brief Summary

The Aim of the study is to evaluate Accuracy of automated mandibular defect reconstruction using Artificial intelligence and assessing impact on aesthetic and occlusion outcomes using patient-specific reconstruction plates.

Detailed Description

The digital surgical process often requires an expected mandibular reference model. Currently, the common digital surgery process, is to mirror repair or manually look for other similar mandibles for local data fusion and smoothing processing. A more accurate expected reference model is difficult to achieve, time consuming and difficult to promote in clinical practice. Moreover, rapid routing processing often has poor accuracy. For cumulative bilateral lesions, massive lesions, obvious displacement or lesions cross the middle line, there is still no effective method to predict the expected reference model in clinical practice.

The main objective for conducting this study is to propose an improved algorithm to overcome the drawbacks of recent studies using 3D Unet and to test the predictability and clinical value of virtually generated 3d models of defected mandible in real patients.

Study Timeline

• Status Verified: 2025-04
• Study First Submitted: 2025-04-10
• Study First Submitted QC: 2025-04-20
• Last Update Submitted: 2025-04-20
• Study First Posted: 2025-04-25
• Last Update Posted: 2025-04-25
• Study Start: 2025-05-01
• Primary Completion: 2026-04-01
• Study Completion: 2026-05-01

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 4

Inclusion Criteria

• Patients with mandibular tumors, cysts or any benign disease resulting in mandibular continuity defect.
• Age group: from 18 - 55 years old.
• No sex predilection.
• CTs or CBCTs of only healthy mandibles from an online database and real data.

Exclusion Criteria

• Patients with mandibular malignant lesions.
• Children age group from 2-17.
• CTs Of maxilla.
• Elderly patients to be excluded due to the normal physiologic bony change.

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Primary Outcome Measures

Name	Time	Method
Accuracy Of the virtually Generated 3D model using AI	baseline	The measuring device is the AI model using the Percentage as a unit
Accuracy of AI generated model clinically	baseline	The measuring device is by Superimposition of both virtual 3-d generated model and real patient CT post operative using software ( blender ) .; ( Structural Similarity Index) (SSIM)

Secondary Outcome Measures

Name	Time	Method
Aethetic outcome	baseline	The measuring device is Facial appearance using a 4-point score
Occlusion	baseline	The measuring device is Digital occlusion analysis using T-scan and the unit is percentage