Clinical trial NCT06945692: Assessment of Accuracy and Aesthetics Following Automated Mandibular Defect Reconstruction Using AI
Official title: Assessment of Accuracy and Aesthetics Following Automated Mandibular Defect Reconstruction Using Artificial Intelligence: A Case Series Study
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Mandibular Tumor
Keywords: Artificial intelligence
Patient specific reconstruction plates
Lead sponsor: Cairo University
Sponsor class: OTHER
First posted: 2025-04-25
Enrollment: 4
Sex: ALL
Minimum age (years): 18
Maximum age (years): 55
Standard ages: ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: TREATMENT

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Accuracy Of the virtually Generated 3D model using AI
timeFrame: baseline
measure: Accuracy of AI generated model clinically
timeFrame: baseline
Secondary outcomes:
measure: Aethetic outcome
timeFrame: baseline
measure: Occlusion
timeFrame: baseline
Other outcomes:
Locations:
Study officials:
affiliation: Cairo University
name: Sarah Moustafa, MSc.
role: PRINCIPAL_INVESTIGATOR
References:
citation: Liang Y, Huan J, Li JD, Jiang C, Fang C, Liu Y. Use of artificial intelligence to recover mandibular morphology after disease. Sci Rep. 2020 Oct 2;10(1):16431. doi: 10.1038/s41598-020-73394-5.
citation: van Baar GJC, Forouzanfar T, Liberton NPTJ, Winters HAH, Leusink FKJ. Accuracy of computer-assisted surgery in mandibular reconstruction: A systematic review. Oral Oncol. 2018 Sep;84:52-60. doi: 10.1016/j.oraloncology.2018.07.004. Epub 2018 Jul 20.