Assessment of Accuracy and Aesthetics Following Automated Mandibular Defect Reconstruction Using AI
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: patient specific reconstruction plates.
- Who it may be relevant to
- Registry conditions: Mandibular Tumor. Basic parameters: 18 years — 55 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Assessment of Accuracy and Aesthetics Following Automated Mandibular Defect Reconstruction Using Artificial Intelligence: A Case Series Study
Overview
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.
Interventions
- Procedure patient specific reconstruction plates
Use of patient specific reconstruction plates on the 3-D virtually-generated defect using Artificial Intelligence.
Primary outcome measures
- Accuracy Of the virtually Generated 3D model using AI [Time frame: baseline]
- Accuracy of AI generated model clinically [Time frame: baseline]
Secondary outcome measures (2)
- Aethetic outcome [Time frame: baseline]
- Occlusion [Time frame: baseline]
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- N/A
- Model
- Single group
- Masking
- Open label
- Primary purpose
- Treatment
Study locations
Center list to be confirmed — check the primary protocol.
Publications
- 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. PMID 33009429
- 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. PMID 30115476
Identifiers
NCT: NCT06945692 · AI in Mandibular Defects