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In order to diagnose TMJ pathologies, we developed and tested a novel algorithm, MandSeg, that combines image processing and machine learning approaches for automatically segmenting the mandibular condyles and ramus. A deep neural network based on the U-Net architecture was trained for this task, using 109 cone-beam computed tomography (CBCT) scans. The ground truth label maps were manually segmented by clinicians. The U-Net takes 2D slices extracted from the 3D volumetric images. All the 3D scans were cropped depending on their size in order to keep only the mandibular region of interest. The same anatomic cropping region was used for every scan in the dataset. The scans were acquired at different centers with different resolutions. Therefore, we resized all scans to 512×512 in the pre-processing step where we also performed contrast adjustment as the original scans had low contrast. After the pre-processing, around 350 slices were extracted from each scan, and used to train the U-Net model. For the cross-validation, the dataset was divided into 10 folds. The training was performed with 60 epochs, a batch size of 8 and a learning rate of 2×10 -5 . The average performance of the models on the test set presented 0.95 ± 0.05 AUC, 0.93 ± 0.06 sensitivity, 0.9998 ± 0.0001 specificity, 0.9996 ± 0.0003 accuracy, and 0.91 ± 0.03 F1 score. This study findings suggest that fast and efficient CBCT image segmentation of the mandibular condyles and ramus from different clinical data sets and centers can be analyzed effectively. Future studies can now extract radiomic and imaging features as potentially relevant objective diagnostic criteria for TMJ pathologies, such as osteoarthritis (OA). The proposed segmentation will allow large datasets to be analyzed more efficiently for disease classification.

The biggest challenge to improve the diagnosis and therapies of Craniomaxillofacial conditions is to translate algorithms and software developments towards the creation of holistic patient models. A complete picture of the individual patient for treatment planning and personalized healthcare requires a compilation of clinician-friendly algorithms to provide minimally invasive diagnostic techniques with multimodal image integration and analysis. We describe here the implementation of the open-source Craniomaxillofacial module of the 3D Slicer software, as well as its clinical applications. This paper proposes data management approaches for multisource data extraction, registration, visualization, and quantification. These applications integrate medical images with clinical and biological data analytics, user studies, and other heterogeneous data.

Introduction: Considering the large number of fixed functional appliances, choosing the best device for your patient is not an easy task., Objective: To describe the development of fixed functional appliances as well as our 20-year experience working with them., Methods: Fixed functional appliances are grouped into flexible, rigid and hybrid. They are different appliances, whose action is described here. Four clinical cases will be reported with a view to illustrating the different appliances., Conclusions: Rigid fixed functional appliances provide better skeletal results than flexible and hybrid ones. Flexible and hybrid appliances have similar effects to those produced by Class II elastics. They ultimately correct Class II with dentoalveolar changes. From a biomechanical standpoint, fixed functional appliances are more recommended to treat Class II in dolichofacial patients, in comparison to Class II elastics.

The evaluation of the upper airway (UA) includes the physical examination of pharyngeal structures and a number of imaging techniques that vary from the mostly used lateral cephalometry and computed tomography to more sophisticated methods such as tri-dimensional magnetic resonance image (MRI). Other complex techniques addressing UA collapsibility assessed by measurement of pharyngeal critical pressure and negative expiratory pressure however are not routinely performed. These methods provide information about anatomic abnormalities and the level of pharyngeal narrowing or collapse while the patient is awake or asleep. Data suggest that individual patients have different patterns of UA narrowing. So, the best method for evaluating obstruction during obstructive events remains controversial. In general, in clinical practice physical examination including a systematic evaluation of facial morphology, mouth, nasal cavity and the pharynx as well as simple imaging techniques such as nasopharyngoscopy and cephalometry have been more routinely utilized. Findings associated with obstructive sleep apnoea (OSA) are UA narrowing by the lateral pharyngeal walls and enlargements of tonsils, uvula and tongue. Additionally cephalometry identifies the most significant craniofacial characteristics associated with this disease. MRI studies demonstrated that lateral narrowing of UA in OSA is due to parapharyngeal muscle hypertrophy and/or enlargement of non adipose soft tissues. The upper airway evaluation has indubitably contributed to understand the pathophysiology and the diagnosis of OSA and snoring. Additionally, it also helps to identify the subjects with increased OSA risk as well as to select the more appropriate modality of treatment, especially for surgical procedures.