Research on Deep Learning-Based Intelligent Diagnosis and Treatment
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age6-70
SponsorXi'an Jiaotong University
About this trial
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
This trial accepts healthy volunteersQualifiers
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.
Disqualifiers
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.
Trial population
The study population will include approximately 2,000 participants with craniomaxillofacial imaging data and related clinical information obtained during routine dental, orthodontic, oral and maxillofacial, or related clinical care. Eligible participants may have two-dimensional facial photographs, cone-beam computed tomography images, or three-dimensional facial surface scans available for artificial intelligence-based imaging analysis. Related clinical information may include demographic characteristics, clinical diagnosis, skeletal or dental classification, cephalometric measurements, treatment-related records, and expert assessment results. The study will use available clinical imaging data to develop and validate deep learning models for craniomaxillofacial image classification, segmentation, landmark detection, abnormality recognition, and treatment-related decision support.
Trial design
Design model
Cohort
Time perspective
Retrospective
Treatments tested in this trial
Not listed
Trial groups
2,000 Participants
are grouped into 1 trial groupGroup A: Craniomaxillofacial Imaging Cohort
Trial outcomes
Primary outcomes
1
Diagnostic performance of artificial intelligence models for craniomaxillofacial imaging analysis
The primary outcome is the diagnostic performance of the developed artificial intelligence models on the independent testing dataset. Performance will be evaluated using accuracy, precision, recall, F1-score, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic curve analysis, and area under the receiver operating characteristic curve, as appropriate for the specific classification or diagnostic recognition task.
Time frame
At completion of model validation on the independent testing dataset, expected within 12 months after study initiationLocations
Sponsors and contacts
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Source ClinicalTrials.gov
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