Deep learning in the diagnosis for cystic lesions of the jaws: a review of recent progress.

Cystic lesions of the gnathic bones present challenges in differential diagnosis. In recent years, artificial intelligence (AI) represented by deep learning (DL) has rapidly developed and emerged in the field of dental and maxillofacial radiology (DMFR). Dental radiography provides a rich resource for the study of diagnostic analysis methods for cystic lesions of the jaws and has attracted many researchers.

CBCT image based segmentation method for tooth pulp cavity region extraction.

Objectives:: A method was proposed to segment the tooth pulp cavity region in cone beam CT) images, which aimed to make the extraction process more efficient and generate more reliable results for further research.

Methods:: Cone beam CT images of 50 teeth from 10 patients were randomly collected with the help of Peking University Hospital of Stomatology. All slice images have a ground truth tooth pulp cavity region delineated by two doctors manually.

Three-dimensional CBCT images registration method for TMJ based on reconstructed condyle and skull base.

Objectives: A method was introduced for three-dimensional (3D) cone-beamCT (CBCT) images registration of temporomandibular joint (TMJ). This study aimed to provide quantitative and qualitative analysis of TMJ bone changes in two-dimensional (2D) and 3D and to provide the technique for computer-aided diagnosis of temporomandibular joint disorders in the future.

Methods: 10 TMJ samples of six patients were obtained from Peking University Hospital of Stomatology.

[A study of trabecular bone structure in the mandibular condyle of healthy young people by cone beam computed tomography].

Objective: To evaluate the feasibility of cone beam computed tomography (CBCT) for the evaluation of trabecular bone structure in mandibular condyle and to investigate the distribution of the trabecular bone structure within mandibular condyle.

Methods: Eighty condyles from 40 healthy young volunteers (aged 20-32) were scanned by CBCT. A coronoid image was acquired of each condyle and divided into 8 regions where regions of interest were specified.