A comparison of virtual reality and three-dimensional multiplanar educational methods for student learning of cone beam computed tomography interpretations.

OBJECTIVES: The purpose of this study was to compare student learning of cone beam computed tomography (CBCT) interpretation using immersive virtual reality (VR) and three-dimensional multiplanar (MP) reconstructions. METHODS: Sixty first-year dental students were randomly allocated to two groups, VR and MP, and underwent a one-on-one educational intervention to identify anatomic structures using CBCT data. All participants completed three multiple-choice questionnaires (MCQs) before (T1), immediately after (T2), and 2 weeks following (T3) the intervention. Additionally, pre-survey, post-survey, NASA Task Load Index (NASA-TLX), and presence questionnaires were completed. Analysis of objective measures of performance on MCQs and subjective data from the questionnaires was completed (α = 0.05). RESULTS: There was a significant increase in test performance and informational recall between T1-T2 and T1-T3 for VR and MP groups (p < 0.001). However, there were no significant differences in performance on MCQs between T2 and T3. Analysis of the Presence questionnaire indicated that the VR group felt decreased distraction (p = 0.013), increased realism (p = 0.035), and increased involvement (p = 0.047) during the educational intervention when compared with the MP group. Analysis of the NASA-TLX indicated that the VR group experienced more physical demand (p < 0.01) but similar cognitive demand when compared with the MP group. Qualitative responses indicated that the VR group had a more dynamic sense of visualization and manipulation compared to the MP group. CONCLUSION: Results from this study show that VR is as effective as traditional MP methods of CBCT interpretation learning. Further benefits of VR educational intervention include increased involvement, realism and less distraction.

A skeleton in a huff: insights in etiologies of osteosclerosis.

A 30-yr-old man developed right lower leg pain and a palpable solid mass. Radiographic imaging revealed a periosteal reaction with an exostotic mass arising from the right distal fibula. Generalized skeletal osteosclerosis with periosteal reaction was discovered on a radiographic skeletal survey. A biopsy of the right fibular mass revealed reactive woven bone. The patient was referred to a metabolic bone disease clinic, where laboratory values were consistent with secondary hyperparathyroidism and increased bone turnover. A DXA bone density scan revealed high bone density, with an L1-4 spine Z-score of +9.3, a left femoral neck Z-score of +8.5, and a total hip Z-score of +6.5. A dental exam revealed generalized gingival inflammation, teeth mobility, generalized horizontal alveolar bone loss and widening of the periodontal ligament space, increased bone density around the teeth, and thickening of the radicular lamina dura. An extensive evaluation was performed, with the result of a single test revealing the diagnosis. The differential diagnoses of osteosclerosis affecting the skeleton, teeth, and oral cavity are discussed.

A 30-yr-old man developed, over a short period, pain in his lower right leg accompanied by a hard mass. He also reported weight loss and night sweats for the past 6 months. After evaluation by his primary physician, an X-ray was ordered that reported a bony mass arising from the right fibula bone. A biopsy was performed of the mass, but no evidence of cancer or any other specific abnormality was found. The patient was then referred to a bone disease specialty clinic. Laboratory tests revealed a large increase in how quickly the patient's skeleton was remodeling, affecting the balance of bone formation and removal involved in maintaining a healthy skeleton. A bone density scan reported that the patient had very dense bones. Other unusual changes were also discovered in a dental exam, suggesting bone thickening. After an extensive evaluation, a single blood test revealed the cause of the fibular bone mass and dense bones.

A comprehensive patient-specific prediction model for temporomandibular joint osteoarthritis progression.

Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative disease characterized by chronic pain and impaired jaw function. The complexity of TMJ OA has hindered the development of prognostic tools, posing a significant challenge in timely, patient-specific management. Addressing this gap, our research employs a comprehensive, multidimensional approach to advance TMJ OA prognostication. We conducted a prospective study with 106 subjects, 74 of whom were followed up after 2 to 3 y of conservative treatment. Central to our methodology is the development of an innovative, open-source predictive modeling framework, the Ensemble via Hierarchical Predictions through Nested cross-validation tool (EHPN). This framework synergistically integrates 18 feature selection, statistical, and machine learning methods to yield an accuracy of 0.87, with an area under the ROC curve of 0.72 and an F1 score of 0.82. Our study, beyond technical advancements, emphasizes the global impact of TMJ OA, recognizing its unique demographic occurrence. We highlight key factors influencing TMJ OA progression. Using SHAP analysis, we identified personalized prognostic predictors: lower values of headache, lower back pain, restless sleep, condyle high gray level-GL-run emphasis, articular fossa GL nonuniformity, and long-run low GL emphasis; and higher values of superior joint space, mouth opening, saliva Vascular-endothelium-growth-factor, Matrix-metalloproteinase-7, serum Epithelial-neutrophil-activating-peptide, and age indicate recovery likelihood. Our multidimensional and multimodal EHPN tool enhances clinicians' decision-making, offering a transformative translational infrastructure. The EHPN model stands as a significant contribution to precision medicine, offering a paradigm shift in the management of temporomandibular disorders and potentially influencing broader applications in personalized healthcare.

Three-dimensional decision support system for treatment of canine impaction.

INTRODUCTION: This study aimed to develop a 3-dimensional (3D) characterization of the severity of maxillary impacted canines and to test the clinical performance of this characterization as a treatment decision support tool. METHODS: Cone-beam computed tomography images obtained from 83 patients with 120 impacted maxillary canines were included. Quantitative information on the canine 3D position and qualitative assessment of root damage of adjacent teeth were evaluated. A severity index was constructed on the basis of the quantitative findings. Clinical applicability was tested by comparing clinical diagnosis and treatment planning for conventional records vs the 3D characterization via a 2-part survey. RESULTS: The average quantitative assessments of impacted maxillary canine position were 6.4 ± 3.6 mm from the midsagittal plane, 11.6 ± 3.1 mm in height relative to the occlusal plane, 31.5° ± 18° of roll, and 48.8° ± 14.3° of pitch. The severity index ranged from 0-13 with a mean score of 4.5 ± 2.2. Overlap with adjacent teeth was the greatest contributor (33%) to the index. Bicortically impacted canines caused the most severe root damage. Cone-beam computed tomography was preferred for assessing root damage and overall severity, whereas conventional imaging was sufficient for height and angulation assessment. The 3D report was very important or important for evaluating root damage, canine position, overall severity, and overlap. The 3D report changed most of the decisions relating to biomechanics, patient education, and treatment time estimate. The decision of exposure and traction vs extraction was changed 22% of the time after the presentation of the 3D report. CONCLUSIONS: The overlap with adjacent teeth frequently contributes the most to the severity index. The 3D report provided relevant clinical information regarding the canine position, damage to adjacent teeth, and the severity index, with a profound impact on the decisions of the clinicians regarding biomechanics, patient education, and treatment time estimate.

Student learning of head and neck anatomy using cone beam computed tomography and immersive virtual reality.

OBJECTIVES: The purpose of this study was to evaluate the efficacy of student learning of anatomy and 3D imaging concepts using cone beam computed tomography (CBCT) and immersive virtual reality (VR) technology. METHODOLOGY: Ninety (n = 90) first year dental students with no previous experience in 3D imaging were recruited. All participants completed a 10-item, multiple-choice questionnaire (MCQ) and a pre-survey prior to the educational intervention. Following a brief video orientation on CBCT and anatomy, each participant underwent a one-on-one educational intervention using immersive VR with calibrated instructors to identify head and neck anatomic structures using a VR/CBCT educational tool. Immediately following the intervention, all participants completed a postsurvey, a second MCQ, NASA task load index and presence questionnaires. Participants completed a third MCQ 2 weeks following the intervention. Analysis of objective measures of performance on MCQ's (p < 0.05) and subjective data from the questionnaires was completed. RESULTS: The students doubled their mean test scores 2.45 ± 1.274 to 5.99 ± 1.576 on MCQ's immediately following the educational intervention (p < 0.05). The significant increase in the MCQ test scores was maintained after 2 weeks, 5.73 ± 1.721 (p < 0.05). There were no gender differences in student test performance. Students rated the immersive VR/CBCT educational intervention experience highly for control, sensory, and realism factors with minimal distraction and frustration factors. CONCLUSION: Results from this study show that immersive VR/CBCT educational intervention improved test performance and contributed to information recall in students. Further benefits reported by participants include the sense of presence and increased engagement using immersive VR.

Quantitative bone imaging biomarkers and joint space analysis of the articular Fossa in temporomandibular joint osteoarthritis using artificial intelligence models.

Temporomandibular joint osteoarthritis (TMJ OA) is a disease with a multifactorial etiology, involving many pathophysiological processes, and requiring comprehensive assessments to characterize progressive cartilage degradation, subchondral bone remodeling, and chronic pain. This study aimed to integrate quantitative biomarkers of bone texture and morphometry of the articular fossa and joint space to advance the role of imaging phenotypes for diagnosis of Temporomandibular Joint Osteoarthritis (TMJ OA) in early to moderate stages by improving the performance of machine-learning algorithms to detect TMJ OA status. Ninety-two patients were prospectively enrolled (184 h-CBCT scans of the right and left mandibular condyles), divided into two groups: 46 control and 46 TMJ OA subjects. No significant difference in the articular fossa radiomic biomarkers was found between TMJ OA and control patients. The superior condyle-to-fossa distance (p < 0.05) was significantly smaller in diseased patients. The interaction effects of the articular fossa radiomic biomarkers enhanced the performance of machine-learning algorithms to detect TMJ OA status. The LightGBM model achieved an AUC 0.842 to diagnose the TMJ OA status with Headaches and Range of Mouth Opening Without Pain ranked as top features, and top interactions of VE-cadherin in Serum and Angiogenin in Saliva, TGF-ß1 in Saliva and Headaches, Gender and Muscle Soreness, PA1 in Saliva and Range of Mouth Opening Without Pain, Lateral Condyle Grey Level Non-Uniformity and Lateral Fossa Short Run Emphasis, TGF-ß1 in Serum and Lateral Fossa Trabeculae number, MMP3 in Serum and VEGF in Serum, Headaches and Lateral Fossa Trabecular spacing, Headaches and PA1 in Saliva, and Headaches and BDNF in Saliva. Our preliminary results indicate that condyle imaging features may be more important in regards to main effects, but the fossa imaging features may have a larger contribution in terms of interaction effects. More studies are needed to optimize and further enhance machine-learning algorithms to detect early markers of disease, improve prediction of disease progression and severity to ultimately better serve clinical decision support systems in the treatment of patients with TMJ OA.

Ultrasound insonation angle and scanning imaging modes for imaging dental implant structures: A benchtop study.

INTRODUCTION: High frequency ultrasound has shown as a promising imaging modality to evaluate peri-implant tissues. It is not known if the ultrasound imaging settings might influence ultrasound's ability to differentiate implant structures. The aim of this benchtop study was to evaluate the dependence of ultrasound on imaging angles and modes to measure implant geometry-related parameters. METHODS: A clinical ultrasound scanner (ZS3, Mindray) with an intraoral probe (L30-8) offering combinations of harmonic and compound imaging modes was employed for imaging 16 abutments and 4 implants. The samples were mounted to a micro-positioning system in a water tank, which allowed a range of -30 to 30-degree imaging angles in 5-degree increment between the probe and samples. The abutment angle, implant thread pitch and depth were measured on ultrasound, compared to the reference readings. The errors were computed as a function of the image angles and modes. All samples were replicated 3 times for 3 image modes and 11 image angles, thus resulting in 2,340 images. RESULTS: The mean errors of ultrasound to estimate 16 abutment angles, compared to the reference values, were between -1.8 to 2.7 degrees. The root mean squared error (RMSE) ranged from 1.5 to 4.6 degrees. Ultrasound significantly overestimated the thread pitch by 26.1 µm to 36.2 µm. The error in thread depth measurements were in a range of -50.5 µm to 39.6 µm, respectively. The RMSE of thread pitch and depth of the tested 4 implants was in a range of 34.7 to 56.9 µm and 51.0 to 101.8 µm, respectively. In most samples, these errors were independent of the image angle and modes. CONCLUSIONS: Within the limitations of this study, high-frequency ultrasound was feasible in imaging abutments and implant fixtures independent of scanning angle within ±30° of normal incidence and for compounding and non-compounding-based imaging modes.

Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR.

The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise require an average of 7h of manual work by experienced clinicians. This work focuses on the integration of the state-of-the-art UNEt TRansformers (UNETR) of the Medical Open Network for Artificial Intelligence (MONAI) framework. We trained and tested our models using 618 de-identified Cone-Beam Computed Tomography (CBCT) volumetric images of the head acquired with several parameters from different centers for a generalized clinical application. Our results on a 5-fold cross-validation showed high accuracy and robustness with a Dice score up to 0.962±0.02. Our code is available on our public GitHub repository.

Temporomandibular Joint Osteoarthritis Diagnosis Using Privileged Learning of Protein Markers.

Diagnosis of temporomandibular joint (TMJ) Osteoarthritis (OA) before serious degradation of cartilage and subchondral bone occurs can help prevent chronic pain and disability. Clinical, radiomic, and protein markers collected from TMJ OA patients have been shown to be predictive of OA onset. Since protein data can often be unavailable for clinical diagnosis, we harnessed the learning using privileged information (LUPI) paradigm to make use of protein markers only during classifier training. Three different LUPI algorithms are compared with traditional machine learning models on a dataset extracted from 92 unique OA patients and controls. The best classifier performance of 0.80 AUC and 75.6 accuracy was obtained from the KRVFL+ model using privileged protein features. Results show that LUPI-based algorithms using privileged protein data can improve final diagnostic performance of TMJ OA classifiers without needing protein microarray data during classifier diagnosis.

Automatic Segmentation of Dental Root Canal and Merging with Crown Shape.

In this paper, machine learning approaches are proposed to support dental researchers and clinicians to study the shape and position of dental crowns and roots, by implementing a Patient Specific Classification and Prediction tool that includes RootCanalSeg and DentalModelSeg algorithms and then merges the output of these tools for intraoral scanning and volumetric dental imaging. RootCanalSeg combines image processing and machine learning approaches to automatically segment the root canals of the lower and upper jaws from large datasets, providing clinical information on tooth long axis for orthodontics, endodontics, prosthodontic and restorative dentistry procedures. DentalModelSeg includes segmenting the teeth from the crown shape to provide clinical information on each individual tooth. The merging algorithm then allows users to integrate dental models for quantitative assessments. Precision in dentistry has been mainly driven by dental crown surface characteristics, but information on tooth root morphology and position is important for successful root canal preparation, pulp regeneration, planning of orthodontic movement, restorative and implant dentistry. In this paper we propose a patient specific classification and prediction of dental root canal and crown shape analysis workflow that employs image processing and machine learning methods to analyze crown surfaces, obtained by intraoral scanners, and three-dimensional volumetric images of the jaws and teeth root canals, obtained by cone beam computed tomography (CBCT).

Automatic Segmentation of Mandibular Ramus and Condyles.

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.

Silver Diamine Fluoride and Progression of Incipient Approximal Caries in Permanent Teeth: A Retrospective Study.

Purpose: The purpose of this study was to evaluate the progression of incipient approximal caries lesions in permanent teeth of children and adolescents with and without silver diamine fluoride (SDF) application. Methods: A retrospective analysis of dental records and radiographs was performed. Baseline and follow-up bitewing radiographs were evaluated and scored using International Caries Classification and Management System (ICCMS) criteria to assess lesion progression. Results: A total of 131 lesions from 68 subjects (mean age equals 9.6 years) were evaluated radiographically and followed for up to 41 months (mean time equals 13.61±6.8 months); 23.6 percent of lesions in the SDF group progressed compared to 38.1 percent in the control group (P<0.001). On average, lesions in the control group increased more per month compared to the study group (P<0.001). The odds of lesion progression in the control group were 2.76 times the odds of progression in the study group. There was a statistically significant difference in lesion progression based on application method; lesions where SDF was applied with Superfloss progressed more per month, on average, versus microbrush application. Conclusions: Silver diamine fluoride may be an effective therapy to slow caries progression of incipient approximal lesions in permanent teeth in high caries-risk populations. Future studies are needed to detect differences in application methods.

Mandibular condylar remodeling characteristics after simultaneous condylectomy and orthognathic surgery.

INTRODUCTION: The objectives of this study were to evaluate postsurgical condylar remodeling using a radiographical interpretation, quantify condylar volumetric asymmetry, and assess soft tissue symmetry after simultaneous unilateral high condylectomy and bimaxillary osteotomies. METHODS: Sixteen patients diagnosed with unilateral condylar hyperplasia underwent unilateral high condylectomy and orthognathic surgery to correct skeletal and facial asymmetries. Cone-beam computed tomography scans were acquired before and 1-year after surgery. A radiographic consensus was evaluated for signs of reparative or degenerative changes. The condyles were mirrored and registered for assessment of volumetric and morphologic asymmetry. Soft tissue symmetry was evaluated by measurement of the distance of soft tissue pogonion from the skeletal midsagittal plane. RESULTS: Patients who undergo unilateral high condylectomy and orthognathic surgery present radiographic signs suggestive of degenerative changes, including sclerosis, osteophytes, flattening, and erosion in both the surgical and nonsurgical condyles (P ≤0.01). There was an average volumetric improvement of 531.9 ± 662.3 mm3 1-year postsurgery (P = 0.006). Soft tissue symmetry improved in all patients, with an average improvement of 65.8% (4.0 mm ± 2.6 mm, P ≤ 0.01). There was no correlation between the change in condylar volumetric asymmetry and the stability of the soft tissue correction. CONCLUSIONS: High condylectomy for the correction of a skeletal asymmetry in patients with condylar hyperplasia successfully reduces the volumetric asymmetry between the condyles. Postsurgical dysmorphic remodeling and degenerative changes were noted in both the surgical and nonsurgical condyles. Despite remarkable changes and remaining joint asymmetry, the soft tissue correction is stable 1-year postsurgery.

Radiographic interpretation using high-resolution Cbct to diagnose degenerative temporomandibular joint disease.

The objective of this study was to use high-resolution cone-beam computed images (hr- CBCT) to diagnose degenerative joint disease in asymptomatic and symptomatic subjects using the Diagnostic Criteria for Temporomandibular Disorders DC/TMD imaging criteria. This observational study comprised of 92 subjects age-sex matched and divided into two groups: clinical degenerative joint disease (c-DJD, n = 46) and asymptomatic control group (n = 46). Clinical assessment of the DJD and high-resolution CBCT images (isotropic voxel size of 0.08mm) of the temporomandibular joints were performed for each participant. An American Board of Oral and Maxillofacial Radiology certified radiologist and a maxillofacial radiologist used the DC/TMD imaging criteria to evaluate the radiographic findings, followed by a consensus of the radiographic evaluation. The two radiologists presented a high agreement (Cohen's Kappa ranging from 0.80 to 0.87) for all radiographic findings (osteophyte, erosion, cysts, flattening, and sclerosis). Five patients from the c- DJD group did not present radiographic findings, being then classified as arthralgia. In the asymptomatic control group, 82.6% of the patients presented radiographic findings determinant of DJD and were then classified as osteoarthrosis or overdiagnosis. In conclusion, our results showed a high number of radiographic findings in the asymptomatic control group, and for this reason, we suggest that there is a need for additional imaging criteria to classify DJD properly in hr-CBCT images.

Clinical decision support systems in orthodontics: A narrative review of data science approaches.

Advancements in technology and data collection generated immense amounts of information from various sources such as health records, clinical examination, imaging, medical devices, as well as experimental and biological data. Proper management and analysis of these data via high-end computing solutions, artificial intelligence and machine learning approaches can assist in extracting meaningful information that enhances population health and well-being. Furthermore, the extracted knowledge can provide new avenues for modern healthcare delivery via clinical decision support systems. This manuscript presents a narrative review of data science approaches for clinical decision support systems in orthodontics. We describe the fundamental components of data science approaches including (a) Data collection, storage and management; (b) Data processing; (c) In-depth data analysis; and (d) Data communication. Then, we introduce a web-based data management platform, the Data Storage for Computation and Integration, for temporomandibular joint and dental clinical decision support systems.

Comprehensive peri-implant tissue evaluation with ultrasonography and cone-beam computed tomography: A pilot study.

OBJECTIVES: The aim of the present study was to explore the feasibility of ultrasonography (US) for clinical imaging of peri-implant tissues. MATERIAL AND METHODS: Patients with ≥1 implant, a cone-beam computed tomography (CBCT) scan, an US scan, and clinical photographs taken during the surgery were included. The crestal bone thickness (CBT) and facial bone level (FBL) were measured on both US and CBCT modalities, and direct FBL measurements were also made on clinical images. US measurements were compared with CBCT and direct readings. RESULTS: A total of eight implants from four patients were included. For FBL measurements, US and direct (r2 = 0.95) as well as US and CBCT (r2 = 0.85) were highly correlated, whereas CBCT correlated satisfactorily with the direct reading (r2 = 0.75). In one implant without facial bone, CBCT was not able to measure CBT and FBL accurately. The estimated bias for CBT readings was 0.17 ± 0.23 mm (p = .10) between US and CBCT. US blood flow imaging was successfully recorded and showed a wide dynamic range among patients with different degrees of clinical inflammation. CONCLUSION: US is a feasible method to evaluate peri-implant facial crestal bone dimensions. Additional US features, for example, functional blood flow imaging, may be useful to estimate the extent and severity of inflammation.

FlyBy CNN: A 3D surface segmentation framework.

In this paper, we present FlyBy CNN, a novel deep learning based approach for 3D shape segmentation. FlyByCNN consists of sampling the surface of the 3D object from different view points and extracting surface features such as the normal vectors. The generated 2D images are then analyzed via 2D convolutional neural networks such as RUNETs. We test our framework in a dental application for segmentation of intra-oral surfaces. The RUNET is trained for the segmentation task using image pairs of surface features and image labels as ground truth. The resulting labels from each segmented image are put back into the surface thanks to our sampling approach that generates 1-1 correspondence of image pixels and triangles in the surface model. The segmentation task achieved an accuracy of 0.9.

3D Auto-Segmentation of Mandibular Condyles.

Temporomandibular joints (TMJ) like a hinge connect the jawbone to the skull. TMJ disorders could cause pain in the jaw joint and the muscles controlling jaw movement. However, the disease cannot be diagnosed until it becomes symptomatic. It has been shown that bone resorption at the condyle articular surface is already evident at initial diagnosis of TMJ Osteoarthritis (OA). Therefore, analyzing the bone structure will facilitate the disease diagnosis. The important step towards this analysis is the condyle segmentation. This article deals with a method to automatically segment the temporomandibular joint condyle out of cone beam CT (CBCT) scans. In the proposed method we denoise images and apply 3D active contour and morphological operations to segment the condyle. The experimental results show that the proposed method yields the Dice score of 0.9461 with the standards deviation of 0.0888 when it is applied on CBCT images of 95 patients. This segmentation will allow large datasets to be analyzed more efficiently towards data sciences and machine learning approaches for disease classification.

Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning.

After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-ß1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints.

Patient Specific Classification of Dental Root Canal and Crown Shape.

This paper proposes machine learning approaches to support dentistry researchers in the context of integrating imaging modalities to analyze the morphology of tooth crowns and roots. One of the challenges to jointly analyze crowns and roots with precision is that two different image modalities are needed. Precision in dentistry is mainly driven by dental crown surfaces characteristics, but information on tooth root shape and position is of great value for successful root canal preparation, pulp regeneration, planning of orthodontic movement, restorative and implant dentistry. An innovative approach is to use image processing and machine learning to combine crown surfaces, obtained by intraoral scanners, with three dimensional volumetric images of the jaws and teeth root canals, obtained by cone beam computed tomography. In this paper, we propose a patient specific classification of dental root canal and crown shape analysis workflow that is widely applicable.