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. The aim of the current study was to investigate the diagnostic performance of DL for cystic lesions of the jaws. Online searches were done on Google Scholar, PubMed, and IEEE Xplore databases, up to September 2023, with subsequent manual screening for confirmation. The initial search yielded 1862 titles, and 44 studies were ultimately included. All studies used DL methods or tools for the identification of a variable number of maxillofacial cysts. The performance of algorithms with different models varies. Although most of the reviewed studies demonstrated that DL methods have better discriminative performance than clinicians, further development is still needed before routine clinical implementation due to several challenges and limitations such as lack of model interpretability, multicentre data validation, etc. Considering the current limitations and challenges, future studies for the differential diagnosis of cystic lesions of the jaws should follow actual clinical diagnostic scenarios to coordinate study design and enhance the impact of AI in the diagnosis of oral and maxillofacial diseases.

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. After necessary gamma transform in pre-processing stage, three kinds of information in an image such as greyscale, neighbour average greyscale and gradient were fused to search an optimal segmentation threshold by using plane intercept histogram of reciprocal cross entropy algorithm. With the optimal threshold, binarization was conducted and the tooth pulp cavity regions in slice images can be extracted. Qualitative and quantitative analyses compared to ground truth are involved with the evaluation criterion of average non-coincidence rate ( RANOA ). Independent repeated experiments were carried out to test the stability of this segmentation method. RESULTS:: Accurate and complete segmentation results are obtained. The proposed method reaches the lowest RANOA values in most cases and owns more competitive robustness under various interferences compared with the other popular segmentation methods like reciprocal cross entropy method, active contour-based method, region growing method and level set method. Quantitative analysis verified the effectiveness of this method. CONCLUSIONS:: The proposed method can extract tooth pulp cavity regions from teeth efficiently. The segmentation results of this method are more accurate compared to other popular methods under different circumstances and can be used for subsequent applications.

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. Four of the six patients imaged bilateral TMJs and the other two patients only imaged unilateral TMJ. Each sample consisted of two images from the same TMJ taken at different times. First, condyle and skull base were segmented semi-automatically for 3D model reconstruction. Then the segmented condyle and skull base were registered separately. Registration process can be divided into two processes of rough registration and fine registration. Rough registration step was achieved by selecting corresponding points manually and initialized fine registration. Condyle and skull base were fine registered by minimizing mean square error of condyle (MSEcondyle) and skull base (MSEskull) respectively. Qualitative assessment of osseous component changes utilized 2D color-fused model and 3D surface-fused model and quantitative analyses the convergence of this method used the mean square error of the model (MSEmodel). Independent repeated experiments were carried out to test the stability of our 3D registration method. RESULTS: Sufficiently alignment was achieved. Osseous abnormality and morphology changes were displayed using fusion model. MSEmodel of condylar registration and skull base registration declined 51.80% and 64.58% compared with that before registration. Quantitative analysis verified the stability of the method. CONCLUSIONS: The proposed method completed 3D TMJ registration for different physiological structure. The result of this method was accurate, reproducible and not relied on the experience of operators.

[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. After CBCT images were binarized, four morphological parameters including bone volume fraction, trabecular thickness, trabecular number and trabecular separation were computed. RESULTS: All parameters were significantly different between the superior zone and middle/inferior zone of the condyle (P < 0.05). Superior zone showed the largest bone volume fraction (52.2%), the highest trabecular number (1.33 mm(-1)), the thinnest trabecular thickness (393.48 microm), and the smallest trabecular separation (361.59 microm). Inferior zone showed the smallest bone volume fraction (49.64%). These results were not significantly different between bilateral sides of the condyles (P > 0.05). CONCLUSIONS: Trabecular bone structure was inhomogeneous within the condyle, but symmetrical between bilateral sides of the condyles. CBCT combined with image processing is a feasible tool in evaluating trabecular bone structure of human mandibular condyle.