Background: Migraine affects one in ten individuals worldwide and is the second leading cause of disability. Studies have shown an association between migraine and the musculoskeletal system, and myofascial trigger points (MTrPs) play an essential role. Additionally, those with myofascial pain have been proven to experience higher levels of depression and anxiety. Understanding the association between MTrPs and migraine is crucial for developing targeted treatment strategies. Additionally, recognizing the link between MTrPs and migraine-related depression and anxiety underscores the importance of a holistic approach to migraine management. By addressing both musculoskeletal and neurological factors, healthcare providers can provide more effective and personalized care for migraine patients. This study aims to determine the association between MTrPs with migraine-related disability, anxiety, depression, and migraine characteristics.
Methods: This cross-sectional study included 68 migraine patients from an outpatient neurology clinic. The number of MTrPs was determined through examination by an experienced neurologist during a migraine-free period using the recommended international criteria. We evaluated anxiety and depression with the Hospital Anxiety and Depression Scale (HADS) and disability with the Migraine Disability Assessment Scale (MIDAS).
Results: We enrolled 68 patients (22 males) with a mean age of 36.23 ± 9.63 years. The mean number of MTrPs was 2.75 ± 2.934. MTrPs were positively correlated with severity (CC: 0.576, P-value < 0.001). There was no association between MTrPs and HADS-D or MIDAS, but migraine patients with abnormal HADS-A scores had more MTrPs than patients with normal HADS-A scores (0.6 ± 0.84 vs 3.56 ± 3.11, P-value:0.013).
Conclusions: The number of MTrPs is associated with higher anxiety levels and headache intensity. Further research could investigate the impact of MTrP-based therapies on anxiety among individuals suffering from migraines.
Purpose: Nasoseptal perforations (NSP) are a clinically heterogeneous group of disorders with a wide range of available treatments. Patient-reported outcome measures (PROMs) can provide valuable insights for assessing clinical and surgical outcomes. This study aims to develop and validate a novel-specific questionnaire for patients with NSP.
Methods: A multi-centre prospective observational study was conducted at two tertiary referral hospitals. "Septal Perforation Quality of Life" (SEPEQOL) was developed by a committee of experts. The psychometric properties, including reproducibility, reliability, validity, and responsiveness, were assessed.
Results: The study included 96 symptomatic NSP patients and 30 healthy controls. SEPEQOL internal consistency was satisfactory [Cronbach´s α = 0.7843; 95% confidence interval (CI), 0.702-0.856]. Test-retest reliability was excellent, demonstrated by the absolute intraclass correlation (ICC = 0.974; 95% CI, 0.935-0.989, P-value < 0.001) and Bland-Altman plot (line bias = 1.6 ± 4.57; 95% CI -0.54-3.74, P-value < 0.001). The mean total SEPEQOL score was higher before surgery (25.16 ± 1.65) compared to 6-months after the procedure (13.72 ± 11.39), with a mean difference of 12.19 [standard deviation (SD) 10.76], P-value < 0.001.
Conclusions: SEPEQOL is reliable, consistent, valid, and sensitive to change over time. SEPEQOL assesses the impact of health-related quality of life on NSP and its management in clinical practice. Moreover, it is easy to apply in clinical settings with minimal burden.
Background: To support dentists with limited experience, this study trained and compared six convolutional neural networks to detect crossbites and classify non-crossbite, frontal, and lateral crossbites using 2D intraoral photographs.
Methods: Based on 676 photographs from 311 orthodontic patients, six convolutional neural network models were trained and compared to classify (1) non-crossbite vs. crossbite and (2) non-crossbite vs. lateral crossbite vs. frontal crossbite. The trained models comprised DenseNet, EfficientNet, MobileNet, ResNet18, ResNet50, and Xception.
Findings: Among the models, Xception showed the highest accuracy (98.57%) in the test dataset for classifying non-crossbite vs. crossbite images. When additionally distinguishing between lateral and frontal crossbites, average accuracy decreased with the DenseNet architecture achieving the highest accuracy among the models with 91.43% in the test dataset.
Conclusions: Convolutional neural networks show high potential in processing clinical photographs and detecting crossbites. This study provides initial insights into how deep learning models can be used for orthodontic diagnosis of malocclusions based on intraoral 2D photographs.
Background: Cranial, facial, nasal, and maxillary widths have been shown to be significantly affected by the individual's sex. The present study aims to use measurements of dental arch and maxillary skeletal base to determine sex, employing supervised machine learning.
Materials and methods: Maxillary and mandibular tomographic examinations from 100 patients were analyzed to investigate the inter-premolar width, inter-molar width, maxillary width, inter-pterygoid width, nasal cavity width, nostril width, and maxillary length, obtained through Cone Beam Computed Tomography scans. The following machine learning algorithms were used to build the predictive models: Logistic Regression, Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron Classifier (MLP), Decision Tree, and Random Forest Classifier. A 10-fold cross-validation approach was adopted to validate each model. Metrics such as area under the curve (AUC), accuracy, recall, precision, and F1 Score were calculated for each model, and Receiver Operating Characteristic (ROC) curves were constructed.
Results: Univariate analysis showed statistical significance (p < 0.10) for all skeletal and dental variables. Nostril width showed greater importance in two models, while Inter-molar width stood out among dental measurements. The models achieved accuracy values ranging from 0.75 to 0.85 on the test data. Logistic Regression, Random Forest, Decision Tree, and SVM models had the highest AUC values, with SVM showing the smallest disparity between cross-validation and test data for accuracy metrics.
Conclusion: Transverse dental arch and maxillary skeletal base measurements exhibited strong predictive capability, achieving high accuracy with machine learning methods. Among the evaluated models, the SVM algorithm exhibited the best performance. This indicates potential usefulness in forensic sex determination.
Background: This study aimed to investigate the range of angles and depths necessary for effective entry into the TMJ using CBCT images, focusing on classical Holmlund Hellsing points and a two-needle approach.
Methods: A retrospective cohort of CBCT images from January 2020 to November 2023 was analysed using 3D analysis to determine the variance in the required angles and depths.
Results: The average age of the 68 participants included in the study was 29.5 ± 11.1, 58.8% of the participants were female and 41.2% were male. The anterior needle measurements showed a relatively low standard deviation(SD) in depth(SD:3.6) with a low variance coefficient(12.5%), whereas the axial and coronal angles exhibited greater variability(SD:9.1 and 7.6, respectively). For the posterior needles, moderate SDs in depth(SD:3.5) and greater variabilities in axial and coronal angles(SD:9.6 and 5.3, respectively) were observed. A weak negative correlation was observed between the axial angle of the posterior needle and age(p: 0.028, Pearson r: -0.29) Anterior needle depth (p:0.037) and posterior needle axial angle(p:0.014) were greater in males than females. The anterior needle depth in patients with temporamandibular disease was greater than in those without(p:0,03).
Conclusion: There were significant differences in the angle measurements for both anterior and posterior needles, but lower variance in depth. The depths and angles of the needles did not correlate with age.
Background: Tumorous diseases of the jaw demand effective treatments, often involving continuity resection of the jaw. Reconstruction via microvascular bone flaps, like deep circumflex iliac artery flaps (DCIA), is standard. Computer aided planning (CAD) enhances accuracy in reconstruction using patient-specific CT images to create three-dimensional (3D) models. Data on the accuracy of CAD-planned DCIA flaps is scarce. Moreover, the data on accuracy should be combined with data on the exact positioning of the implants for well-fitting dental prosthetics. This study focuses on CAD-planned DCIA flaps accuracy and proper positioning for prosthetic rehabilitation.
Methods: Patients post-mandible resection with CAD-planned DCIA flap reconstruction were evaluated. Postoperative radiograph-derived 3D models were aligned with 3D models from the CAD plans for osteotomy position, angle, and flap volume comparison. To evaluate the DCIA flap's suitability for prosthetic dental rehabilitation, a plane was created in the support zone and crestal in the middle of the DCIA flap. The lower jaw was rotated to close the mouth and the distance between the two planes was measured.
Results: 20 patients (12 males, 8 females) were included. Mean defect size was 73.28 ± 4.87 mm; 11 L defects, 9 LC defects. Planned vs. actual DCIA transplant volume difference was 3.814 ± 3.856 cm³ (p = 0.2223). The deviation from the planned angle was significantly larger at the dorsal osteotomy than at the ventral (p = 0.035). Linear differences between the planned DCIA transplant and the actual DCIA transplant were 1.294 ± 1.197 mm for the ventral osteotomy and 2.680 ± 3.449 mm for the dorsal (p = 0.1078). The difference between the dental axis and the middle of the DCIA transplant ranged from 0.2 mm to 14.8 mm. The mean lateral difference was 2.695 ± 3.667 mm in the region of the first premolar.
Conclusion: The CAD-planned DCIA flap is a solution for reconstructing the mandible. CAD planning results in an accurate reconstruction enabling dental implant placement and dental prosthetics.
Aim: This study aimed to assess the effectiveness of advanced platelet-rich fibrin (A-PRF) combined with the pinhole surgical technique (PST) for enhancing root coverage (RC) in individuals with Miller class I or II gingival recessions (GR). Additionally, it compared the clinical effect of A-PRF and resorbable collagen membrane (RCM).
Materials and methods: A total of 18 patients, encompassing 36 treatment sides of 18 Miller class I or II, were randomly assigned to the PST + A-PRF side (18 sides) and the PST + RCM side (18 sides). Clinical assessments of various parameters, including plaque index (PI), clinical attachment level (CAL), keratinized tissue width (KTW), recession depth (RD), recession width (RW), and gingival thickness (GT) were conducted at baseline and three months after the surgical procedure. A numeric rating scale (NRS) was also evaluated during the 1st, 2nd, 3rd and 4th days. This study was formally recorded under the TCTR identification number TCTR20230613005 in the Thai Clinical Trials Register-Medical Research Foundation of Thailand (MRF) on 13/06/2023. Furthermore, it was ethically approved by Sana'a University's Ethical Committee for Medical Research.
Results: When comparing the values of 3 months follow-up with the baseline values, intra-side comparison of the PST + A-PRF group showed significant improvements in PI (P = 0.02), CAL (P = 0.01), and RD (P = 0.04), and GT values (P < 0.01). The improvements in the PST + A-PRF group were through the reduction of baseline values of PI, CAL, and RD; the mean reductions in PI, CAL, and RD were 0.44 ± 0.71, 0.33 ± 0.45, and 0.22 ± 0.43 respectively, and a significant increase in GT value (0.44 ± 0.24). While there was an insignificant increase in KTW value with no change in RW values (4.50 ± 0.71, P = 1). In contrast, intra- side comparison of PST + RCM side showed only a significant reduction in PI value (0.44 ± 0.71, P = 0.02) and a significant increase in GT value (0.42 ± 0.26, P = < 0.01). Meanwhile, there were insignificant improvements in CAL (2.89 ± 0.95), KTW (3.97 ± 0.74), and RD (1.94 ± 0.87) values. Regarding inter-side comparison, there were no statistically significant among all variables (p > 0.05). The pain scores of the numeric rating scale were significantly lower on the PST + A-PRF sides compared with the PST + RCM sides, especially on the 1st, 2nd, and 3rd days (P < 0.001).
Conclusion: Both A-PRF and RCM showed not wholly satisfactory outcomes in gingival recession treatment. Interestingly, the combination of PST with A-PRF has proven more effective than combining PST with RCM. Additionally, the localized application of A-PRF has been shown to reduce post-operative pain following the pinhole surgical technique.
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