Research on Intelligent Diagnosis and Treatment Technologies for Craniomaxillofacial Multi-modal Imaging Based on Deep Learning

This study is an imaging-based clinical artificial intelligence study that aims to develop, train, and validate deep learning models for craniomaxillofacial multi-modal imaging analysis and intelligent clinical decision support. Approximately 2,000 participants are planned to be enrolled. All participants will have craniomaxillofacial imaging data and related clinical information obtained during routine dental, orthodontic, oral and maxillofacial, or related clinical care.

The imaging data used in this study may include two-dimensional facial photographs, cone-beam computed tomography images, and three-dimensional facial surface scans. Related clinical data may include demographic information, clinical diagnosis, skeletal or dental classification, cephalometric measurements, treatment-related records, and available expert assessments. This study will focus on developing artificial intelligence models for craniomaxillofacial image classification, anatomical landmark detection, regional segmentation, abnormality recognition, and treatment-related decision support.

Before model development, all imaging data will undergo standardized preprocessing. Preprocessing procedures may include image de-identification, quality assessment, format conversion, image orientation correction, cropping, resolution standardization, grayscale or intensity normalization, spatial registration, and region-of-interest extraction. For two-dimensional images, standardized facial or radiographic regions will be defined according to the specific imaging task. For cone-beam computed tomography and three-dimensional facial surface data, preprocessing may include volumetric reconstruction, surface reconstruction, mesh processing, point-cloud processing, or anatomical structure extraction.

Reference labels will be derived from clinical medical records, clinician diagnosis, and expert manual annotation. For segmentation tasks, anatomical regions or structures will be manually annotated by trained clinicians or calibrated researchers. For landmark detection tasks, predefined anatomical landmarks will be annotated according to standardized craniomaxillofacial measurement protocols. For classification tasks, skeletal pattern, dental relationship, craniomaxillofacial morphology category, or other clinically meaningful classification labels will be determined based on expert assessment. Inter-observer and intra-observer consistency will also be evaluated to ensure the reliability of manual annotations and clinical labels.

The dataset of approximately 2,000 participants will be divided into training, validation, and testing datasets. Seventy percent of the data will be used for model training, 15% for model validation, and 15% for independent testing.