Segmentation of periapical lesions with automatic deep learning on panoramic radiographs: an artificial intelligence study.

Periapical periodontitis may manifest as a radiographic lesion radiographically. Periapical lesions are amongst the most common dental pathologies that present as periapical radiolucencies on panoramic radiographs. The objective of this research is to assess the diagnostic accuracy of an artificial intelligence (AI) model based on U²-Net architecture in the detection of periapical lesions on dental panoramic radiographs and to determine whether they can be useful in aiding clinicians with diagnosis of periapical lesions and improving their clinical workflow. 400 panoramic radiographs that included at least one periapical radiolucency were selected retrospectively. 780 periapical radiolucencies in these anonymized radiographs were manually labeled by two independent examiners. These radiographs were later used to train the AI model based on U²-Net architecture trained using a deep supervision algorithm. An AI model based on the U²-Net architecture was implemented. The model achieved a dice score of 0.8 on the validation set and precision, recall, and F1-score of 0.82, 0.77, and 0.8 respectively on the test set. This study has shown that an AI model based on U²-Net architecture can accurately diagnose periapical lesions on panoramic radiographs. The research provides evidence that AI-based models have promising applications as adjunct tools for dentists in diagnosing periapical radiolucencies and procedure planning. Further studies with larger data sets would be required to improve the diagnostic accuracy of AI-based detection models.

Investigation of Phase Transformation and Fracture Pattern as a Result of Long-Term Chewing Simulation and Static Loading of Reduced-Diameter Zirconia Implants.

While zirconia implants exhibit osseointegration comparable to that of titanium, concerns arise regarding low-temperature degradation and its potential impact on fracture strength. This study investigated the phase transformation and fracture characteristics of zirconia dental implants after aging through chewing simulation and subsequent static loading. The experimental setup involved 48 one-piece monobloc zirconia implants with diameters of 3.0 mm and 3.7 mm that had straight or angled abutments, with crown restorations, which were divided into six groups based on intraoral regions. The specimens underwent chewing simulation equal to five years of oral service, which was followed by static loading. Statistical analyses were performed for the data obtained from the tests. After dynamic and static loadings, the fractured samples were investigated by Raman spectroscopy to analyze the phase composition and micro-CT to evaluate fracture surfaces and volume changes. According to the results, narrow-diameter zirconia implants have low mechanical durability. The fracture levels, fracture patterns, total porosity, and implant fracture volume values varied according to the implant diameter and phase transformation grade. It was concluded that phase transformation initially guides the propagation of microcracks in zirconia implants, enhancing fracture toughness up to a specific threshold; however, beyond that point, it leads to destructive consequences.

Pierre Fauchard (1678-1761): Pioneering Dental Surgeon of the Enlightenment Age.

Pierre Fauchard, widely referred to as the "Father of Modern Dentistry," fundamentally transformed the field with his seminal 1728 publication, Le Chirurgien Dentiste, ou Traité des Dents. Born circa 1677 in Brittany, France, Fauchard's early exposure to severe dental conditions during his naval service catalyzed his pursuit of advancements in dental science. Upon transitioning from naval service to establish a practice in Angers, and subsequently gaining acclaim in Paris, Fauchard systematically documented and organized dental practices, encompassing oral surgery, orthodontics, periodontics, and prosthodontics, thereby laying the foundational framework for contemporary dental practices. Fauchard's innovations included the use of materials such as lead, tin, and gold for dental fillings and the introduction of early orthodontic techniques, notably the Bandeau. His treatise also emphasized the importance of preventive care and oral hygiene, which provided a basis for modern dental hygiene protocols. Additionally, Fauchard's critical evaluation of fraudulent practices and his inclusion of numerous clinical case studies in his treatise bridged theoretical knowledge with practical application, significantly impacting dental education and professional standards. Fauchard's influence extends beyond national boundaries, profoundly shaping global dental practices and educational frameworks. The Pierre Fauchard Academy, established in 1936, continues to uphold his principles, underscoring the enduring relevance of his contributions. Fauchard's work remains a cornerstone of modern dentistry, reflecting his profound and lasting impact on the discipline.

Dextrose neuroprolotherapy and occlusal splint treatment outcomes in occlusal trauma: Evaluation through ultrasound imaging.

BACKGROUND: Occlusal trauma has become a common phenomenon among individuals today. Its primary source is bruxism, which involves unusual activities such as clenching and grinding during the day or sleep. The hypothesis is that with 5% dextrose neuroprolotherapy, both the trigger points and affected nerves will be healed, and the muscle will be relieved by eliminating the pain. METHODS: This study aimed to compare the short-term ultrasonographic results of patients treated with occlusal splint and 5% dextrose neuroprolotherapy for bruxism. Patients were divided into two groups: the dextrose neuroprolotherapy group and the occlusal splint group. In the first group, patients were administered 5% dextrose three times at one-week intervals using the dextrose neuroprolotherapy method. Impressions for both jaws were made using a high-viscosity irreversible hydrocolloid impression material in the second group. An occlusal splint was tailored to fit the upper jaw. Patients were assessed for masseter muscle thickness and strain ratio using ultrasonography before and 3 months after the treatment. RESULTS: No statistically significant differences were found between the two groups for all measures. Statistically significant differences were observed in the strain ratio of the left musculus massetericus in the resting position and the thickness of the left musculus massetericus in the contracted position exclusively in the neuroprolotherapy group (p=0.001, p=0.011, respectively). Differences in the strain ratio of both sides of the contracted musculus massetericus were demonstrated in both groups (neuroprolotherapy group: right side p<0.001, left side p=0.007, splint group: right side p=0.005, left side p=0.012). CONCLUSION: This study demonstrates that 5% dextrose neuroprolotherapy is an effective treatment comparable to an occlusal splint. Objectively visualizing changes in the masseter muscle through ultrasound provides clear results in the context of occlusal trauma and bruxism.

Evaluation of tooth development stages with deep learning-based artificial intelligence algorithm.

BACKGROUND: This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients. METHODS: The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model. RESULTS: Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively. CONCLUSIONS: In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options.

Gender-Specific Variations in Greater Palatine Foramen Anatomy: Insights from CBCT Scans in the North Cyprus Population.

BACKGROUND The greater palatine foramen (GPF) is anatomically located distal to the third maxillary molar tooth, midway between the midline of the palate and the dental arch. The GPF contains the major palatine artery, vein, and nerve, traversing the palatine sulcus. This study aimed to evaluate the anatomical position of the GPF in 93 women and 67 men at a single center in Cyprus using cone beam computed tomography (CBCT). MATERIAL AND METHODS A retrospective analysis was conducted on 160 CBCT scans. Measurements of the GPF's horizontal and vertical diameters, distances from GPF to the incisive foramen, posterior nasal spine, anterior nasal spine, and midaxillary suture, and positional relationships to molars were recorded. Statistical analyses compared these measurements between males and females. RESULTS The study included 93 females and 67 males with an average age of 46.6 (±11.6) years. Significant sex differences were observed in most GPF measurements, with males showing larger dimensions such as the anterior nasal spine, posterior nasal spine, mid-maxillary suture, and incisive foramen to the GPF. The GPF was predominantly located in the third molar region (96.25% on the right, 96.9% on the left). The left GPF showed a significantly larger horizontal diameter than the right (P<0.05). CONCLUSIONS There was a significant difference in the average distances from the anterior nasal spine, posterior nasal spine, mid-maxillary suture, and incisive foramen to the GPF, as well as in the size of the GPF, between males and females. Recognizing these variations enhances clinical planning and reduces the risk of complications.

A deep learning approach to detection of oral cancer lesions from intra oral patient images: A preliminary retrospective study.

INTRODUCTION: Oral squamous cell carcinomas (OSCC) seen in the oral cavity are a category of diseases for which dentists may diagnose and even cure. This study evaluated the performance of diagnostic computer software developed to detect oral cancer lesions in intra-oral retrospective patient images. MATERIALS AND METHODS: Oral cancer lesions were labeled with CranioCatch labeling program (CranioCatch, Eskisehir, Turkey) and polygonal type labeling method on a total of 65 anonymous retrospective intraoral patient images of oral mucosa that were diagnosed with oral cancer histopathologically by incisional biopsy from individuals in our clinic. All images have been rechecked and verified by experienced experts. This data set was divided into training (n = 53), validation (n = 6) and test (n = 6) sets. Artificial intelligence model was developed using YOLOv5 architecture, which is a deep learning approach. Model success was evaluated with confusion matrix. RESULTS: When the success rate in estimating the images reserved for the test not used in education was evaluated, the F1, sensitivity and precision results of the artificial intelligence model obtained using the YOLOv5 architecture were found to be 0.667, 0.667 and 0.667, respectively. CONCLUSIONS: Our study reveals that OCSCC lesions carry discriminative visual appearances, which can be identified by deep learning algorithm. Artificial intelligence shows promise in the prediagnosis of oral cancer lesions. The success rates will increase in the training models of the data set that will be formed with more images.

Automatic deep learning detection of overhanging restorations in bitewing radiographs.

OBJECTIVES: This study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms for the detecting and segmentation of overhanging dental restorations in bitewing radiographs. METHODS: A total of 1160 anonymized bitewing radiographs were used to progress the artificial intelligence (AI) system for the detection and segmentation of overhanging restorations. The data were then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels). A CNN model known as You Only Look Once (YOLOv5) was trained to detect overhanging restorations in bitewing radiographs. After utilizing the remaining 115 radiographs to evaluate the efficacy of the proposed CNN model, the accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC) were computed. RESULTS: The model demonstrated a precision of 90.9%, a sensitivity of 85.3%, and an F1 score of 88.0%. Furthermore, the model achieved an AUC of 0.859 on the receiver operating characteristic (ROC) curve. The mean average precision (mAP) at an intersection over a union (IoU) threshold of 0.5 was notably high at 0.87. CONCLUSIONS: The findings suggest that deep CNN algorithms are highly effective in the detection and diagnosis of overhanging dental restorations in bitewing radiographs. The high levels of precision, sensitivity, and F1 score, along with the significant AUC and mAP values, underscore the potential of these advanced deep learning techniques in revolutionizing dental diagnostic procedures.

The diagnostic accuracy of cone beam computed tomography in detecting temporomandibular joint bony disorders: a systematic review.

Computed tomography (CT) has been recognized as a robust and dependable technique for delineating osseous alterations and anomalies within hard tissues. The necessity for accurate diagnosis and management of patients with temporomandibular disorders in dental practices has increasingly come to the forefront. There is ongoing scholarly debate regarding the equivalence of diagnostic outcomes yielded by cone beam computed tomography (CBCT), which offers greater accessibility in dental settings than traditional CT, in identifying bony changes within the temporomandibular joint (TMJ). Our principal aim was to conduct a systematic review of studies that compare the efficacy of CT and CBCT in the detailed assessment of bone conditions affecting the TMJ. An electronic search was conducted across databases: PubMed, Medline, Web of Science, Cochrane and Scopus. Two independent reviewers screened titles and abstracts against predefined inclusion criteria. The included articles underwent rigorous critical appraisal, during which relevant data were extracted and systematically presented in a tabular format. This systematic review incorporates 5 studies published between 2006 and 2015. In 3 studies, CBCT demonstrated comparable outcomes to CT, while 2 investigations revealed significantly enhanced accuracy for CBCT compared to CT, with reported accuracies of 0.95 ± 0.04, 0.77 ± 0.17, and 89-91% for CBCT. The aggregated evidence from the included studies indicates that CBCT offers comparable or superior accuracy in detecting osseous changes within TMJ structures. Owing to its lower radiation exposure and increased accessibility, CBCT emerges as the preferred choice over conventional CT for evaluating bony structures of the TMJ.

Micro-Computed Tomography Analysis of Resin and Calcium Silicate Based Sealers Removal in Mandibular Molars Curved Canals.

Objective: The removal of the root canal sealer is an important factor in nonsurgical retreatment. The aim of this study was to compare the removal of AH Plus, Well Root ST, and AH Plus Bioceramic Sealer using Protaper Universal retreatment files. Methods: The curved mesio-buccal canals of extracted mandibular molars were prepared with the Protaper Gold file system (up to F2). Specimens were randomly divided into 3 groups and filled with the single cone technique using AH Plus, Well-Root ST, and AH Plus Bioceramic Sealer, respectively. After two weeks, the root canal filling of all specimens was removed using Protaper Universal retreatment files. All specimens were scanned using micro-CT. The remaining volume of the root canal filling was recorded in total and the coronal, middle, and apical third of each specimen. Results: Well-Root ST and AH Plus Bioceramic Sealer groups had a higher percentage of total remaining filling material than the AH Plus group (P<0.05). Conclusion: This study has shown that the volume of remaining root canal filling was significantly higher in the samples filled with calcium silicate-based sealers.

Comparison of Three Commercially Available, AI-Driven Cephalometric Analysis Tools in Orthodontics.

Background: Cephalometric analysis (CA) is an indispensable diagnostic tool in orthodontics for treatment planning and outcome assessment. Manual CA is time-consuming and prone to variability. Methods: This study aims to compare the accuracy and repeatability of CA results among three commercial AI-driven programs: CephX, WebCeph, and AudaxCeph. This study involved a retrospective analysis of lateral cephalograms from a single orthodontic center. Automated CA was performed using the AI programs, focusing on common parameters defined by Downs, Ricketts, and Steiner. Repeatability was tested through 50 randomly reanalyzed cases by each software. Statistical analyses included intraclass correlation coefficients (ICC3) for agreement and the Friedman test for concordance. Results: One hundred twenty-four cephalograms were analyzed. High agreement between the AI systems was noted for most parameters (ICC3 > 0.9). Notable differences were found in the measurements of angle convexity and the occlusal plane, where discrepancies suggested different methodologies among the programs. Some analyses presented high variability in the results, indicating errors. Repeatability analysis revealed perfect agreement within each program. Conclusions: AI-driven cephalometric analysis tools demonstrate a high potential for reliable and efficient orthodontic assessments, with substantial agreement in repeated analyses. Despite this, the observed discrepancies and high variability in part of analyses underscore the need for standardization across AI platforms and the critical evaluation of automated results by clinicians, particularly in parameters with significant treatment implications.

Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model.

OBJECTIVES: Radiomics, a recently developed image-processing technology, holds potential in medical diagnostics. This study aimed to propose a machine-learning (ML) model and evaluate its effectiveness in detecting oral squamous cell carcinoma (OSCC) and predicting bone metastasis using magnetic resonance imaging (MRI). MATERIALS-METHODS: MRI radiomic features were extracted and analyzed to identify malignant lesions. A total of 86 patients (44 with benign lesions without bone invasion and 42 with malignant lesions with bone invasion) were included. Data and clinical information were managed using the RadCloud Platform (Huiying Medical Technology Co., Ltd., Beijing, China). The study employed a hand-crafted radiomics model, with the dataset randomly split into training and validation sets in an 8:2 ratio using 815 random seeds. RESULTS: The results revealed that the ML method support vector machine (SVM) performed best for detecting bone invasion (AUC = 0.999) in the test set. Radiomics tumor features derived from MRI are useful to predicting bone invasion from oral squamous cell carcinoma with high accuracy. CONCLUSIONS: This study introduces an ML model utilizing SVM and radiomics to predict bone invasion in OSCC. Despite the promising results, the small sample size necessitates larger multicenter studies to validate and expand these findings.

Accuracy of Artificial Intelligence for Cervical Vertebral Maturation Assessment-A Systematic Review.

Background/Objectives: To systematically review and summarize the existing scientific evidence on the diagnostic performance of artificial intelligence (AI) in assessing cervical vertebral maturation (CVM). This review aimed to evaluate the accuracy and reliability of AI algorithms in comparison to those of experienced clinicians. Methods: Comprehensive searches were conducted across multiple databases, including PubMed, Scopus, Web of Science, and Embase, using a combination of Boolean operators and MeSH terms. The inclusion criteria were cross-sectional studies with neural network research, reporting diagnostic accuracy, and involving human subjects. Data extraction and quality assessment were performed independently by two reviewers, with a third reviewer resolving any disagreements. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool was used for bias assessment. Results: Eighteen studies met the inclusion criteria, predominantly employing supervised learning techniques, especially convolutional neural networks (CNNs). The diagnostic accuracy of AI models for CVM assessment varied widely, ranging from 57% to 95%. The factors influencing accuracy included the type of AI model, training data, and study methods. Geographic concentration and variability in the experience of radiograph readers also impacted the results. Conclusions: AI has considerable potential for enhancing the accuracy and reliability of CVM assessments in orthodontics. However, the variability in AI performance and the limited number of high-quality studies suggest the need for further research.

Gender-Based Variation in Alveolar Bone Thickness of Maxillary Incisor Teeth: A CBCT Retrospective Study.

BACKGROUND This retrospective study from a single center in Cyprus aimed to assess labial (buccal) and palatal bone thickness in 6 anterior maxillary teeth of 120 adults using cone-beam computed tomography (CBCT). MATERIAL AND METHODS The CBCT scans of 120 patients (720 teeth) were examined, with scanning parameters of 90 kvP, 24 s, 4 mA, voxel size 0.3 mm, and field of view of 10×6 cm. All maxillary incisors were categorized into 3 distinct points in terms of buccal (B) and palatal (P) points, with points B1 (buccal) and P1 (palatal) 4 mm below the cementoenamel junction; points B2 and P2 at the midpoint between the labial and palatal alveolar crest plane extending to the root apex; and points B3 and P3 at the root apex. Evaluation was done by measuring the distance from these points to the labial and palatal alveolar bone. RESULTS When the thicknesses were measured between all 6 points and labial and palatal bone, the thickness of point B3 of tooth 13 in men was significantly higher than that in women. At points P1, P2, and P3 for teeth 11 and 13, the palatal bone thickness of men was significantly higher than that of women. At points P2 and P3 of tooth 12, the palatal bone thickness of men was significantly higher than that of women. CONCLUSIONS The study found a correlation between alveolar bone thickness and patient sex in the North Cyprus population. Alveolar bone thickness in the anterior maxillary should be considered in implant treatment and orthodontic techniques.

Classification of Periapical and Bitewing Radiographs as Periodontally Healthy or Diseased by Deep Learning Algorithms.

Objectives The aim of this artificial intelligence (AI) study was to develop a deep learning algorithm capable of automatically classifying periapical and bitewing radiography images as either periodontally healthy or unhealthy and to assess the algorithm's diagnostic success. Materials and methods The sample of the study consisted of 1120 periapical radiographs (560 periodontally healthy, 560 periodontally unhealthy) and 1498 bitewing radiographs (749 periodontally healthy, 749 periodontally ill). From the main datasets of both radiography types, three sub-datasets were randomly created: a training set (80%), a validation set (10%), and a test set (10%). Using these sub-datasets, a deep learning algorithm was developed with the YOLOv8-cls model (Ultralytics, Los Angeles, California, United States) and trained over 300 epochs. The success of the developed algorithm was evaluated using the confusion matrix method. Results The AI algorithm achieved classification accuracies of 75% or higher for both radiograph types. For bitewing radiographs, the sensitivity, specificity, precision, accuracy, and F1 score values were 0.8243, 0.7162, 0.7439, 0.7703, and 0.7821, respectively. For periapical radiographs, the sensitivity, specificity, precision, accuracy, and F1 score were 0.7500, 0.7500, 0.7500, 0.7500, and 0.7500, respectively. Conclusion The AI models developed in this study demonstrated considerable success in classifying periodontal disease. Future applications may involve employing AI algorithms for assessing periodontal status across various types of radiography images and for automated disease detection.

Retrospective evaluation of ponticulus posticus prevalence, sella turcica types, and stylohyoid complex calcifications in a group of Turkish population.

The aim of this retrospective analysis was to assess the incidence of ponticulus posticus and stylohyoid ligament calcification and to evaluate the morphological variations of the sella turcica within the Turkish demographic using CBCT scans. Involving a retrospective review of 460 CBCT scans and utilizing the NewTom 3G system, the study analyzed high-quality CBCT images to investigate ponticulus posticus, stylohyoid ligament calcifications, and sella turcica morphology. The ponticulus posticus was examined for complete or partial formations, while the stylohyoid ligament was classified according to its elongation and calcification patterns. The sella turcica was categorized into six distinct morphological types, enhancing the understanding of structural variations in the context of the Turkish population. The calcification patterns of the styloid processes were examined on both sides of 380 individuals, revealing the highest prevalence in the 'd' and 'e' categories on the right, and similar findings on the left among 373 individuals. Symmetric calcification patterns were more common, with 68.4% symmetry observed. For the sella turcica, category 'a' was the most frequent among 363 individuals. Analysis of ponticulus posticus absence and presence showed a majority lacking this feature on both sides, with complete and partial forms less common. The study highlights the anatomical variability and bilateral symmetry of the styloid processes, sella turcica, and ponticulus posticus, illustrating that these structures do not significantly vary with gender or age. These results hold clinical significance for the diagnosis and treatment of related conditions, prompting further investigation into their impact on patient care.

A Deep Learning Approach to Automatic Tooth Caries Segmentation in Panoramic Radiographs of Children in Primary Dentition, Mixed Dentition, and Permanent Dentition.

OBJECTIVES: The purpose of this study was to evaluate the effectiveness of dental caries segmentation on the panoramic radiographs taken from children in primary dentition, mixed dentition, and permanent dentition with Artificial Intelligence (AI) models developed using the deep learning method. METHODS: This study used 6075 panoramic radiographs taken from children aged between 4 and 14 to develop the AI model. The radiographs included in the study were divided into three groups: primary dentition (n: 1857), mixed dentition (n: 1406), and permanent dentition (n: 2812). The U-Net model implemented with PyTorch library was used for the segmentation of caries lesions. A confusion matrix was used to evaluate model performance. RESULTS: In the primary dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8525, 0.9128, and 0.8816, respectively. In the mixed dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.7377, 0.9192, and 0.8185, respectively. In the permanent dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8271, 0.9125, and 0.8677, respectively. In the total group including primary, mixed, and permanent dentition, the sensitivity, precision, and F1 scores calculated using the confusion matrix were 0.8269, 0.9123, and 0.8675, respectively. CONCLUSIONS: Deep learning-based AI models are promising tools for the detection and diagnosis of caries in panoramic radiographs taken from children with different dentition.

MgCa-Based Alloys Modified with Zn- and Ga-Doped CaP Coatings Lead to Controlled Degradation and Enhanced Bone Formation in a Sheep Cranium Defect Model.

Mg-based biodegradable metallic implants are gaining increased attraction for applications in orthopedics and dentistry. However, their current applications are hampered by their high rate of corrosion, degradation, and rapid release of ions and gas bubbles into the physiological medium. The aim of the present study is to investigate the osteogenic and angiogenic potential of coated Mg-based implants in a sheep cranial defect model. Although their osteogenic potential was studied to some extent, their potential to regenerate vascularized bone formation was not studied in detail. We have studied the potential of magnesium-calcium (MgCa)-based alloys modified with zinc (Zn)- or gallium (Ga)-doped calcium phosphate (CaP) coatings as a strategy to control their degradation rate while enhancing bone regeneration capacity. MgCa and its implants with CaP coatings (MgCa/CaP) as undoped or as doped with Zn or Ga (MgCa/CaP + Zn and MgCa/CaP + Ga, respectively) were implanted in bone defects created in the sheep cranium. MgCa implants degraded faster than the others at 4 weeks postop and the weight loss was ca. 50%, while it was ca. 15% for MgCa/CaP and <10% in the presence of Zn and Ga with CaP coating. Scanning electron microscopy (SEM) analysis of the implant surfaces also revealed that the MgCa implants had the largest degree of structural breakdown of all the groups. Radiological evaluation revealed that surface modification with CaP to the MgCa implants induced better bone regeneration within the defects as well as the enhancement of bone-implant surface integration. Bone volume (%) within the defect was ca. 25% in the case of MgCa/CaP + Ga, while it was around 15% for undoped MgCa group upon micro-CT evaluation. This >1.5-fold increase in bone regeneration for MgCa/CaP + Ga implant was also observed in the histopathological examination of the H&E- and Masson's trichrome-stained sections. Immunohistochemical analysis of the bone regeneration (antiosteopontin) and neovascularization (anti-CD31) at the defect sites revealed >2-fold increase in the expression of the markers in both Ga- and Zn-doped, CaP-coated implants. Zn-doped implants further presented low inflammatory reaction, notable bone regeneration, and neovascularization among all the implant groups. These findings indicated that Ga- and Zn-doped CaP coating is an important strategy to control the degradation rate as well as to achieve enhanced bone regeneration capacity of the implants made of Mg-based alloys.

Development and Validation of an Ultrasonography-Based Machine Learning Model for Predicting Outcomes of Bruxism Treatments.

BACKGROUND AND OBJECTIVES: We aimed to develop a predictive model for the outcome of bruxism treatments using ultrasonography (USG)-based machine learning (ML) techniques. This study is a quantitative research study (predictive modeling study) in which different treatment methods applied to bruxism patients are evaluated through artificial intelligence. MATERIALS AND METHODS: The study population comprised 102 participants with bruxism in three treatment groups: Manual therapy, Manual therapy and Kinesio Tape or Botulinum Toxin-A injection. USG imaging was performed on the masseter muscle to calculate muscle thickness, and pain thresholds were evaluated using an algometer. A radiomics platform was utilized to handle imaging and clinical data, as well as to perform a subsequent radiomics statistical analysis. RESULTS: The area under the curve (AUC) values of all machine learning methods ranged from 0.772 to 0.986 for the training data and from 0.394 to 0.848 for the test data. The Support Vector Machine (SVM) led to excellent discrimination between bruxism and normal patients from USG images. Radiomics characteristics in pre-treatment ultrasound scans of patients, showing coarse and nonuniform muscles, were associated with a greater chance of less effective pain reduction outcomes. CONCLUSIONS: This study has introduced a machine learning model using SVM analysis on ultrasound (USG) images for bruxism patients, which can detect masseter muscle changes on USG. Support Vector Machine regression analysis showed the combined ML models can also predict the outcome of the pain reduction.