ABSTRACT
BACKGROUND: Morphological differences in the temporomandibular joint (TMJ) are crucial for the treatment of patients with cleft lip and palate (CLP). This study aims to evaluate and compare the TMJ parameters in patients with unilateral and bilateral CLP across growing and non-growing age groups using cone-beam computed tomography (CBCT). METHODS: CBCT records from 57 patients (23 males and 34 females) aged 6-50 years with a diagnosed unilateral or bilateral CLP were analyzed. Patients were categorized into four groups: growing unilateral (UGCLP), growing bilateral (BGCLP), non-growing unilateral (UNGCLP), and non-growing bilateral (BNGCLP). Measurements of TMJ parameters, including the mandibular fossa, articular eminence inclination, joint spaces, and roof thickness of the glenoid fossa, were conducted using CBCT images. RESULTS: Significant differences were observed in the anterior joint space (AJS) and the roof of the glenoid fossa (RGF) between growing and non-growing unilateral cleft patients. Additionally, significant discrepancies were found in the articular eminence angle when comparing the cleft and non-cleft sides within the unilateral growing group. No significant differences were observed in TMJ parameters between the right and left sides among bilateral cleft patients. CONCLUSIONS: The study highlights distinct TMJ morphological differences between growing and non-growing patients with CLP, emphasizing the importance of age-specific considerations in the treatment planning and growth monitoring of these patients.
ABSTRACT
BACKGROUND: Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future. METHODS: A total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions. The labeled images were resized to 640 × 320 while maintaining their aspect ratio before model training. The labeled images were randomly divided into three groups (Training:349 images (1589 labels), Validation:43 images (181 labels), Test:43 images (215 labels)). YOLOv5x algorithm was used to perform deep learning. The segmentation performance of the tested model was visualized and analyzed using ROC analysis and a confusion matrix. True Positive (TP), False Positive (FP), and False Negative (FN) values were determined. RESULTS: Among the test group images, there were 133 TPs, 36 FPs, and 82 FNs. The model's performance metrics include precision, recall, and F1 score values of detecting white spot lesions were 0.786, 0.618, and 0.692. The AUC value obtained from the ROC analysis was 0.712. The mAP value obtained from the Precision-Recall curve graph was 0.425. CONCLUSIONS: The model's accuracy and sensitivity in detecting white spot lesions remained lower than expected for practical application, but is a promising and acceptable detection rate compared to previous study. The current study provides a preliminary insight to further improved by increasing the dataset for training, and applying modifications to the deep learning algorithm. CLINICAL REVELANCE: Deep learning systems can help clinicians to distinguish white spot lesions that may be missed during visual inspection.
Subject(s)
Algorithms , Deep Learning , Photography, Dental , Humans , Image Processing, Computer-Assisted/methods , Photography, Dental/methods , Pilot ProjectsABSTRACT
OBJECTIVES: This study aimed to determine mastoid emissary canal's (MEC) and mastoid foramen (MF) prevalence and morphometric characteristics on cone-beam computed tomography (CBCT) images to underline its clinical significance and discuss its surgical consequences. METHODS: In the retrospective analysis, two oral and maxillofacial radiologists analyzed the CBCT images of 135 patients (270 sides). The biggest MF and MEC were measured in the images evaluated in MultiPlanar Reconstruction (MPR) views. The MF and MEC mean diameters were calculated. The mastoid foramina number was recorded. The prevalence of MF was studied according to gender and side of the patient. RESULTS: The overall prevalence of MEC and MF was 119 (88.1%). The prevalence of MEC and MF is 55.5% in females and 44.5% in males. MEC and MF were identified as bilateral in 80 patients (67.20%) and unilateral in 39 patients (32.80%). The mean diameter of MF was 2.4 ± 0.9 mm. The mean height of MF was 2.3 ± 0.9. The mean diameter of the MEC was 2.1 ± 0.8, and the mean height of the MEC was 2.1 ± 0.8. There is a statistical difference between the genders (p = 0.043) in foramen diameter. Males had a significantly larger mean diameter of MF in comparison to females. CONCLUSION: MEC and MF must be evaluated thoroughly if the surgery is contemplated. Radiologists and surgeons should be aware of mastoid emissary canal morphology, variations, clinical relevance, and surgical consequences while operating in the suboccipital and mastoid areas to avoid unexpected and catastrophic complications. CBCT may be a reliable imaging diagnostic technique.