Research on Intelligent Diagnosis and Treatment Technologies for Craniomaxillofacial Multi-modal Imaging Based on Deep Learning
Trial ID: NCT07551622
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-05-01
Completion date: 2029-12-31
Sponsor: Xi'an Jiaotong University
Conditions: Malocclusion; Craniofacial Morphology; Dentofacial Deformities
Enrollment: 2000
Age range: 6 Years - 70 Years
Sex: ALL

Summary:
This study aims to develop and evaluate deep learning-based artificial intelligence models for craniomaxillofacial multi-modal imaging analysis and clinical decision support. Approximately 2,000 participants with craniomaxillofacial imaging data and related clinical information will be included. The imaging data may include two-dimensional facial photographs, cone-beam computed tomography images, and three-dimensional facial surface scans.

The study will use artificial intelligence methods to analyze craniofacial images and identify clinically meaningful features related to facial morphology, skeletal or dental classification, anatomical landmarks, regional structures, and craniomaxillofacial abnormalities. The models will be developed for tasks such as image classification, anatomical landmark detection, image segmentation, abnormality recognition, and treatment-related decision support.

The purpose of this study is to improve the accuracy, efficiency, and consistency of image-based assessment in dentistry, orthodontics, and oral and maxillofacial clinical practice. The artificial intelligence models developed in this study are intended to provide objective imaging analysis and decision-support information for health care providers. These models are designed to assist clinicians and will not replace professional diagnosis or individualized treatment planning by qualified clinicians.

This research may benefit patients and families by supporting earlier and more accurate recognition of craniomaxillofacial conditions, improving communication about diagnosis and treatment options, and promoting more personalized oral health care. All clinical images and related information will be handled according to approved research procedures and privacy protection requirements.

Eligibility criteria:
Inclusion Criteria:

* Participants with available craniomaxillofacial imaging data obtained during routine dental, orthodontic, oral and maxillofacial, or related clinical care.
* Participants with at least one eligible imaging modality, including two-dimensional facial photographs, cone-beam computed tomography images, or three-dimensional facial surface scans.
* Participants with related clinical information available for model development or validation, such as demographic information, clinical diagnosis, skeletal or dental classification, cephalometric measurements, treatment-related records, or expert assessment results.
* Imaging data of sufficient quality for artificial intelligence-based image analysis, annotation, segmentation, landmark detection, classification, or decision-support model development.

Exclusion Criteria:

* Participants with incomplete or unavailable key imaging data or clinical information required for the planned analysis.
* Images with severe artifacts, poor resolution, incorrect orientation, incomplete anatomical coverage, or other quality problems that prevent reliable analysis.
* Duplicate records or repeated imaging records that cannot be reliably linked to a unique participant.
* Participants whose data cannot be used according to institutional review board approval, consent requirements, or applicable privacy protection regulations.

Outcomes:
Diagnostic performance of artificial intelligence models for craniomaxillofacial imaging analysis