Gang Li

Objectives: The differential diagnosis of ameloblastoma and odontogenic keratocyst is essential for surgical planning and patient counseling. While deep learning (DL)-based methods show promising potential in this domain, their clinical translation remains challenging due to insufficient interpretability. This study aims to introduce segmentation-guided preprocessing approaches to provide support for the clinical implementation of computer-aided diagnosis systems. Methods: This study evaluated the performance of an InceptionV3 model on 128 pathologically confirmed CBCT scans (AME: 64; OKC: 64) by 5-fold cross-validation. Four experimental inputs were compared: (1) Original slice; (2) Bounding-box ROI; (3) Precise segmentation ROI; and (4) Moderately expanded ROI. All models were trained under the same settings. Assessment was conducted on both the slice and patient levels, incorporating accuracy, recall, precision, F1-score, and the area under the receiver operating characteristic curve (AUC). Grad-CAM visualization and confidence curve analysis were employed to verify models’ attention patterns and diagnostic confidence. Results: All models based on segmentation-guided ROI significantly outperformed models based on original slice. The moderately expanded ROI achieved optimal performance. The bounding-box ROI provided competitive performance with higher recall. Grad-CAM confirmed improved attention localization, while confidence curve analysis showed more consistent and reliable prediction patterns across slices. Conclusions: Segmentation-guided preprocessing represents an effective and clinically relevant approach for jaw lesion diagnosis and enhances interpretability.

Objectives: This study aimed to analyze the correlation between disc position and condylar position and morphology through fused cone-beam computed tomography (CBCT) and magnetic resonance (MR) images. Methods: Patients exhibiting temporomandibular disorder symptoms were included, and joints with poor osseous consistency were excluded. Angle of disc was measured in the fused image using the method proposed in this study. Joint spaces were measured, and condylar morphology was assessed in cone-beam computed tomography images. Statistical analysis was performed to examine the reliability of the measurement method and the correlation between disc position and condylar position/morphology. A logistic regression model was used for identifying factors associated with anterior disc displacement. Results: Our results showed that inter- and intra-observer agreement for measurements of disc angle and joint space were excellent (intraclass correlation coefficient > 0.9). Superior joint space, posterior joint space, and natural logarithm of the posterior-to-anterior joint space ratio showed significant correlations with the angle (P < .01) and significant differences between groups (P < .01). The posterior-to-anterior joint space ratio was significantly smaller in the mild displacement group. The logistic regression model demonstrated that a beak-like shape in oblique sagittal view (OR = 5.235, P < .05) and reduced posterior-to-anterior ratio (OR = 0.301, P < .05) significantly increased the risk of anterior disc displacement. Conclusions: Condylar position and morphology demonstrated statistically significant association with disc position. Multivariate logistic regression analysis revealed that condylar position and morphology in sagittal views in cone-beam computed tomography images can serve as indicators for disc displacement.

Objectives: This review aims to systematically summarize the current studies on the correlation between different degrees of anterior disc displacement (ADD) and temporomandibular joint (TMJ) space. Materials and Methods: Study protocol was carried out based on the Preferred Reporting Items for Systematic Review and Meta-Analysis protocols (PRISMA). The PubMed, Scopus, Web of Science, and China National Knowledge Infrastructure (CNKI) databases were searched to find the relevant articles from database up to February 13, 2025. Studies that evaluated the correlation between ADD and TMJ spaces were included. We excluded intervention studies, case reports, case series, reviews, systematic reviews, opinion articles, book chapters, and studies involving populations with comorbidities potentially affecting the joints. The risk of bias was assessed using the QUADAS-2 tool. Results: Of the 1109 articles retrieved, a total of 10 articles on the correlation between different degrees of ADD and TMJ spaces were included after evaluation of the title, abstract, and full text. Throughout different degree of ADD, variations in anterior joint space (AJS) and posterior joint space (PJS) exist due to condyle displacement. When the progresses further to ADD without reduction (ADDWoR), the superior joint space (SJS) becomes narrow. Conclusions: There was a stepwise correlation between the TMJ spaces and the degree of ADD. The AJS and PJS tend to be various in different ADD groups, and the SJS tends to be narrowed in ADDWoR patients. Future studies should standardize methodologies to validate its clinical applicability.

Objectives: Reliable cancellous bone segmentation in cone beam CT (CBCT) images is essential for post-orthognathic assessment of condylar resorption. However, challenges such as edge blurring and low contrast in CBCT images make effective segmentation difficult. This study aims to overcome these issues, providing a foundation for accurate bone quantification to enhance surgical planning and patient outcomes. Methods: We propose a novel approach to enhance edge-based segmentation for cancellous bone in CBCT images. By incorporating edge features from the cancellous bone region and utilizing cancellous edge localization as an auxiliary task via dual-branch fusion network (DBF-Net), our model leverages shared feature parameters across functions to improve segmentation accuracy and robustness. Results: Our DBF-Net outperformed other models, achieving Dice coefficient of 91.48%. And the 95% Hausdorff distance decreased to 3.88 mm, demonstrating significant improvement in cancellous bone boundary detection, which is crucial for the post-orthognathic assessment of condylar resorption. Conclusions: This method provides a robust solution for reliable cancellous bone segmentation in CBCT images to support the quantitative assessment of condylar resorption.

Objectives: Due to the difference between the natural head position during scan and the orientation of CBCT display required for diagnosis, radiologists need to manually adjust the image orientation during clinical diagnosis. To eliminate this difference, this study explored orientation normalization algorithm for mandibular condyle in the small field-of-view (FoV) cone beam CT (CBCT) images. Methods: Based on the morphology analysis, we designed principal component analysis (PCA) based orientation normalization algorithm for condyle in the small FoV CBCT images. The algorithm involves first locating the reference centre, defined as the centre coordinates of the condylar head in the maximum axial plane, through segmentation and centroid calculation. Subsequently, the maximum principal orientations in the axial, coronal, and sagittal planes are extracted using PCA algorithm. Finally, the condyle orientation is normalized by using rotation transformation matrices derived from condylar head centre localization and principal orientation extraction. Results: Our algorithm was evaluated on 2 CBCT image datasets with 692 scans, and multiple experiments were designed from aspects of algorithm accuracy and stability. Experimental results demonstrate that images with orientation normalization are consistent with the radiologists expected perspective from both qualitative and quantitative aspects. The normalized results of CBCT images taken at multiple time-points also further confirm that our method has good stability. Conclusion: Based on the morphological characteristics, medical image processing algorithm can achieve accurate and stable orientation normalization for condyle in the small FoV CBCT images.

Objectives To select and discover features which are highly correlated with condylar resorption (CR) after orthognathic surgery (OS) by a new method. Materials and methods The cone beam computed tomography (CBCT) images were collected from orthognathic patients taken at T1(baseline time point) and T2(within 3–36 months postoperatively). The subjects were divided into the CR group and the control group by using a 2-level scale after registering T1 and T2 images. The condyles were segmented by MVEL-Net network model and were analyzed by Pyradiomics. The D-value equaled to the absolute difference-value (D-value) between the feature values of T1 and T2. The correlation between features with statistically significant differences in T1 and D-values would be analyzed to find the specific features related to CR after OS. Results A total of 145 subjects were included (27 males and 118 females), which had 44 subjects in the CR group, 101 subjects in the control group. For all samples, a total of 82 features were extracted (F₁), which were with statistical differences at T1 time point between CR and control groups. By using the D-value, the number of features was reduced to 32 features (F_d). Among F_d, only 3 were not included in F₁. Conclusions The D-value was proposed for selecting specific features of CR after OS and it can be observed that the D-value serves the purpose of feature specification compared to T1 values. By using the D-values, several features were found to change significantly during the process of CR after OS. Clinical relevance. The features selected by D-value can be used for the establishment of a prediction model for CR after OS.

Objective: To compare the accuracy, duration, and certainty of diagnosing approximal caries in bitewing radiographs displayed in 3 monitors under 2 luminance conditions. Methods: A total of 39 teeth without evident caries were selected from 11 patients undergoing partial jaw resection. Before the operation, 13 bitewing radiographs were captured by a digital imaging system. Eight observers evaluated the images under dark (9 lux) and bright (200 lux) conditions, using 2 medical-grade monitors and a commercial monitor. Using histological results as the gold standard, the areas under the receiver operating characteristic curves under different conditions were compared using the Z-test. Multivariate analysis of variance was conducted to assess the impact of various factors on diagnostic duration, while ordinal logistic regression was used to examine factors influencing diagnostic certainty level. It was considered significant when P < .05. Results: No significant difference was found in the diagnostic accuracy or duration for diagnosis of approximal caries under 2 luminance conditions with the 3 distinct monitors (P > .05). Ambient light, clinical experience, and the pathological grade of approximal caries have influence on the degree of diagnostic confidence (P < .05). Conclusions: Different monitors and ambient luminance didn’t influence the diagnostic accuracy or evaluation duration. Ambient luminance, clinical experience, and the depth of caries affect the degree of diagnostic confidence. Advances in knowledge: The study employing bitewing radiographs from real patients indicates that ambient luminance, clinical experience, and the depth of caries affect the degree of diagnostic confidence.

Objectives: During deep learning model training, it is essential to consider the balance among the effects of sample size, actual resources, and time constraints. Single-arm objective performance criteria (OPC) was proposed to determine the optimal positive sample size for training deep learning models in caries recognition. Methods: An expected sensitivity (P_T) of 0.6 and a clinically acceptable sensitivity (P₀) of 0.5 were applied to the single-arm OPC calculation formula, yielding an optimal training set comprising 263 carious teeth. U-Net, YOLOv5n, and CariesDetectNet were trained and validated using clinically self-collected cone-beam computed tomography (CBCT) images that included varying quantities of carious teeth. To assess performance, an additional dataset was utilized to evaluate the accuracy of caries detection by both the models and two dental radiologists. Results: When the number of carious teeth reached approximately 250, the models reached the optimal performance levels. U-Net demonstrated superior performance, achieving accuracy, sensitivity, specificity, F1-Score, and Dice similarity coefficients of 0.9929, 0.9307, 0.9989, 0.9590, and 0.9435, respectively. The three models exhibited greater accuracy in caries recognition compared to dental radiologists. Conclusions: This study demonstrated that the positive sample size of CBCT images containing caries was predictable and could be calculated using single-arm OPC.

Original Article Relationship between development of the condylar cortex and the changes in condyle morphology: a cone-beam computed tomography (CBCT) observational study Ruo-Han Ma, Ji-Ling Feng, Michael M. Bornstein, Gang Li