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.

Automated artificial intelligence-based three-dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery.

OBJECTIVE(S): This study aims to evaluate the influence of the piezocision surgery in the orthodontic biomechanics, as well as in the magnitude and direction of tooth movement in the mandibular arch using novel artificial intelligence (AI)-automated tools. MATERIALS AND METHODS: Nineteen patients, who had piezocision performed in the lower arch at the beginning of treatment with the goal of accelerating tooth movement, were compared to 19 patients who did not receive piezocision. Cone beam computed tomography (CBCT) and intraoral scans (IOS) were acquired before and after orthodontic treatment. AI-automated dental tools were used to segment and locate landmarks in dental crowns from IOS and root canals from CBCT scans to quantify 3D tooth movement. Differences in mesial-distal, buccolingual, intrusion and extrusion linear movements, as well as tooth long axis angulation and rotation were compared. RESULTS: The treatment time for the control and experimental groups were 13.2 ± 5.06 and 13 ± 5.52 months respectively (P = .176). Overall, anterior and posterior tooth movement presented similar 3D linear and angular changes in the groups. The piezocision group demonstrated greater (P = .01) mesial long axis angulation of lower right first premolar (4.4 ± 6°) compared with control group (0.02 ± 4.9°), while the mesial rotation was significantly smaller (P = .008) in the experimental group (0.5 ± 7.8°) than in the control (8.5 ± 9.8°) considering the same tooth. CONCLUSION: The open source-automated dental tools facilitated the clinicians' assessment of piezocision treatment outcomes. The piezocision surgery prior to the orthodontic treatment did not decrease the treatment time and did not influence in the orthodontic biomechanics, leading to similar tooth movements compared to conventional treatment.

Artificial intelligence as a prediction tool for orthognathic surgery assessment.

INTRODUCTION: An ideal orthodontic treatment involves qualitative and quantitative measurements of dental and skeletal components to evaluate patients' discrepancies, such as facial, occlusal, and functional characteristics. Deciding between orthodontics and orthognathic surgery remains challenging, especially in borderline patients. Advances in technology are aiding clinical decisions in orthodontics. The increasing availability of data and the era of big data enable the use of artificial intelligence to guide clinicians' diagnoses. This study aims to test the capacity of different machine learning (ML) models to predict whether orthognathic surgery or orthodontics treatment is required, using soft and hard tissue cephalometric values. METHODS: A total of 920 lateral radiographs from patients previously treated with either conventional orthodontics or in combination with orthognathic surgery were used, comprising n = 558 Class II and n = 362 Class III patients, respectively. Thirty-two measures were obtained from each cephalogram at the initial appointment. The subjects were randomly divided into training (n = 552), validation (n = 183), and test (n = 185) datasets, both as an entire sample and divided into Class II and Class III sub-groups. The extracted data were evaluated using 10 machine learning models and by a four-expert panel consisting of orthodontists (n = 2) and surgeons (n = 2). RESULTS: The combined prediction of 10 models showed top-ranked performance in the testing dataset for accuracy, F1-score, and AUC (entire sample: 0.707, 0.706, 0.791; Class II: 0.759, 0.758, 0.824; Class III: 0.822, 0.807, 0.89). CONCLUSIONS: The proposed combined 10 ML approach model accurately predicted the need for orthognathic surgery, showing better performance in Class III patients.

Enhancing skeletal stability and Class III correction through active orthodontist engagement in virtual surgical planning: A voxel-based 3-dimensional analysis.

INTRODUCTION: Skeletal stability after bimaxillary surgical correction of Class III malocclusion was investigated through a qualitative and quantitative analysis of the maxilla and the distal and proximal mandibular segments using a 3-dimensional voxel-based superimposition among virtual surgical predictions performed by the orthodontist in close communication with the maxillofacial surgeon and 12-18 months postoperative outcomes. METHODS: A comprehensive secondary data analysis was conducted on deidentified preoperative (1 month before surgery [T1]) and 12-18 months postoperative (midterm [T2]) cone-beam computed tomography scans, along with virtual surgical planning (VSP) data obtained by Dolphin Imaging software. The sample for the study consisted of 17 patients (mean age, 24.8 ± 3.5 years). Using 3D Slicer software, automated tools based on deep-learning approaches were used for cone-beam computed tomography orientation, registration, bone segmentation, and landmark identification. Colormaps were generated for qualitative analysis, whereas linear and angular differences between the planned (T1-VSP) and observed (T1-T2) outcomes were calculated for quantitative assessments. Statistical analysis was conducted with a significance level of α = 0.05. RESULTS: The midterm surgical outcomes revealed a slight but significantly less maxillary advancement compared with the planned position (mean difference, 1.84 ± 1.50 mm; P = 0.004). The repositioning of the mandibular distal segment was stable, with insignificant differences in linear (T1-VSP, 1.01 ± 3.66 mm; T1-T2, 0.32 ± 4.17 mm) and angular (T1-VSP, 1.53° ± 1.60°; T1-T2, 1.54° ± 1.50°) displacements (P >0.05). The proximal segments exhibited lateral displacement within 1.5° for both the mandibular right and left ramus at T1-VSP and T1-T2 (P >0.05). CONCLUSIONS: The analysis of fully digital planned and surgically repositioned maxilla and mandible revealed excellent precision. In the midterm surgical outcomes of maxillary advancement, a minor deviation from the planned anterior movement was observed.

Automatic landmark identification in cone-beam computed tomography.

OBJECTIVE: To present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans. MATERIALS AND METHODS: One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position. RESULTS: Our method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU. CONCLUSION: The ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.

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.

Three-dimensional comparison between the effects of mandibular advancement device and maxillomandibular advancement surgery on upper airway.

BACKGROUND: The efficacy of mandibular advancement devices (MAD) and maxillomandibular advancement (MMA) in improving upper airway (UA) patency has been described as being comparable to continuous positive airway pressure (CPAP) outcomes. However, no previous study has compared MAD and MMA treatment outcomes for the upper airway enlargement. This study aimed to evaluate three-dimensionally the UA changes and mandibular rotation in patients after MAD compared to MMA. METHODS: The sample consisted of 17 patients with treated with MAD and 17 patients treated with MMA matched by weight, height, body mass index. Cone-beam computed tomography from before and after both treatments were used to measure total UA, superior/inferior oropharynx volume and surface area; and mandibular rotation. RESULTS: Both groups showed a significant increase in the superior oropharynx volume after the treatments (p = 0.003) and the MMA group showed greater increase (p = 0.010). No statistical difference was identified in the MAD group considering the inferior volume, while the MMA group showed a significantly gain (p = 0.010) and greater volume (p = 0.024). Both groups showed anterior mandibular displacement. However, the mandibular rotation were statistically different between the groups (p < 0.001). While the MAD group showed a clockwise rotation pattern (-3.97 ± 1.07 and - 4.08 ± 1.30), the MMA group demonstrated a counterclockwise (2.40 ± 3.43 and 3.41 ± 2.79). In the MAD group, the mandibular linear anterior displacement was correlated with superior [p = 0.002 (r=-0.697)] and inferior [p = 0.004 (r = 0.658)] oropharynx volume, suggesting that greater amounts of mandibular advancement are correlated to a decrease in the superior oropharynx and an increase in the inferior oropharynx. In the MMA group, the superior oropharynx volume was correlated to mandibular anteroposterior [p = 0.029 (r=-0.530)] and vertical displacement [p = 0.047 (r = 0.488)], indicating greater amounts of mandibular advancement may lead to a lowest gain in the superior oropharynx volume, while a great mandibular superior displacement is correlated with improvements in this region. CONCLUSIONS: The MAD therapy led to a clockwise mandibular rotation, increasing the dimensions of the superior oropharynx; while a counterclockwise rotation with greater increases in all UA regions were showed in the MMA treatment.

Dental long axes using digital dental models compared to cone-beam computed tomography.

OBJECTIVE: Standard methods of evaluating tooth long axes are not comparable (digital dental models [DDMs], panoramic and cephalometric radiographs) or expose patients to more radiation (cone-beam computed tomography [CBCT]). This study aimed to compare angular changes in tooth long axes using DDMs vs using CBCTs. SETTINGS AND SAMPLE POPULATION: Secondary data analysis of DDMs and CBCTs, taken before and after orthodontic treatment with piezocision of 24 patients. METHODS: Angular changes in tooth long axes were evaluated using landmarks on first molars (centre of the occlusal surface and centre of the furcation), canines and incisors (cusp tip and centre of the root at the cementoenamel junction). Wilcoxon test, intraclass correlation coefficient (ICC) and Bland-Altman plots were used to test intra- and inter-rater agreement and compare DDM and CBCT measurements. RESULTS: The mesiodistal angulation and buccolingual inclination DDM measurements were reproducible. Overall mean differences between DDM and CBCT measurements of mesiodistal angulation, 1.9°±1.5°, and buccolingual inclination, 2.2 ± 2.2°, were not significant for all teeth. ICC between DDM and CBCT measurements ranged from good (0.85 molars) to excellent (0.94 canines; 0.96 incisors). The percentages of measurements outside the range of ±5 were 17.4% for molars, 13.8% for canines and 4.5% for incisors. CONCLUSIONS: DDM assessment of changes in tooth long axes has good reproducibility and yields comparable measurements to those obtained from CBCT within a 5° range. These findings lay the groundwork for machine learning approaches that synthesize crown and root canal information towards planning tooth movement without the need for ionizing radiation scans.

Three-dimensional craniofacial characteristics associated with obstructive sleep apnea severity and treatment outcomes.

OBJECTIVES: This study aims to assess craniofacial dimensions in obstructive sleep apnea (OSA) patients treated with a mandibular advancement device (MAD) and to identify anatomic influences on OSA severity and MAD therapy outcomes. MATERIALS AND METHODS: Twenty patients with OSA were prospectively treated with MAD. Clinical, cone-beam computed tomography, and polysomnography exams were performed before treatment and 4-6 months after achieving the MAD therapeutic position. Polysomnographic exams and three-dimensional maxillary, mandibular, and upper airway (UA) measurements were evaluated. Pearson's correlation and t-tests were applied. RESULTS: Before MAD treatment, the transverse width measured at the frontomaxillary suture and the angle between the mandibular ramus and Frankfurt horizontal were statistically correlated with apnea and the hypopnea index (AHI), while the gonial angle was correlated with therapeutic protrusion. After MAD treatment, all patients showed a significant AHI reduction and an improvement in minimum oxyhemoglobin saturation. The UA total volume, superior and inferior oropharynx volume, and area were statistically correlated with MAD therapeutic protrusion. The UA total area showed a statistical correlation with the improvement in AHI, and the superior oropharynx volume and area increased significantly. CONCLUSIONS: The transversal frontomaxillary suture width and the mandibular ramus facial angle may influence OSA severity. The gonial angle, volume, and area of all UA regions may indicate the amount of protrusion needed for successful MAD treatment. CLINICAL RELEVANCE: The craniofacial characteristics reported as important factors for OSA severity and MAD treatment outcomes impact therapy planning for OSA patients, considering individual anatomic characteristics, prognosis, and cost benefits.

Presurgical orthodontic decompensation with clear aligners.

INTRODUCTION: Orthodontists, surgeons, and patients have taken an interest in using clear aligners in combination with orthognathic surgery. This study aimed to evaluate the accuracy of tooth movements with clear aligners during presurgical orthodontics using novel 3-dimensional superimposition techniques. METHODS: The study sample consisted of 20 patients who have completed presurgical orthodontics using Invisalign clear aligners. Initial (pretreatment) digital dental models, presurgical digital dental models, and ClinCheck prediction models were obtained. Presurgical models were superimposed onto initial ones using stable anatomic landmarks; ClinCheck models were superimposed onto presurgical models using surface best-fit superimposition. Five hundred forty-five teeth were measured for 3 angular movements (buccolingual torque, mesiodistal tip, and rotation) and 4 linear movements (buccolingual, mesiodistal, vertical, and total scalar displacement). The predicted tooth movement was compared with the achieved amount for each movement and tooth, using both percentage accuracy and numerical difference. RESULTS: Average percentage accuracy (63.4% ± 11.5%) was higher than in previously reported literature. The most accurate tooth movements were buccal torque and mesial displacement compared with lingual torque and distal displacement, particularly for mandibular posterior teeth. Clinically significant inaccuracies were found for the buccal displacement of maxillary second molars, lingual displacement of all molars, intrusion of mandibular second molars, the distal tip of molars, second premolars, and mandibular first premolars, buccal torque of maxillary central and lateral incisors, and lingual torque of premolars and molars. CONCLUSIONS: Superimposition techniques used in this study lay the groundwork for future studies to analyze advanced clear aligner patients. Invisalign is a treatment modality that can be considered for presurgical orthodontics-tooth movements involved in arch leveling and decompensation are highly accurate when comparing the simulated and the clinically achieved movements.

Craniofacial and airway morphology of individuals with oculoauriculovertebral spectrum.

OBJECTIVES: The objectives of this study were to characterize the craniofacial and airway morphology of oculo-auriculo-vertebral spectrum (OAVS) individuals using computed tomography (CT) examination. SETTING AND SAMPLE POPULATION: This sample included individuals in the age range from 5 to 14 years, consisted of a group of 18 OAVS individuals (12 females and 6 males), Pruzansky-Kaban1 IIB and III and by a paired control group matched by age and sex for comparison of morphometric and airway variables. MATERIALS AND METHODS: Through the CT examination, airway analysis was performed using Dolphin Imaging® Software, and seven morphometric measurements were performed to evaluate craniofacial morphology by Materialize Mimics® Software. To compare airway and morphometric variables, the control group was used. Student's t test and Mann-Whitney U test were performed to compare differences between the groups. RESULTS: Statistically significant differences were showed between the control and OAVS groups for the variables: total airway (TA) area, volume and MAA, RP area, RP volume, RP MAA, RG volume, RG MAA, total posterior height diff, Md incl and y-axis asymmetry. Pearson and Spearman's correlation showed mostly moderate correlations between Mand Occlusal canting AS with TA area and RP volume, Ax-Gn with TA area and Hy-C3 with TA volume. CONCLUSIONS: The OAVS's airway was altered and worse than the control group. Our results suggest that the contralateral side of OAVS individuals is unaffected; however, longitudinal assessments are needed to confirm it. Hyoid bone and postural measures play an important role in interpreting airway features of individuals with and without OAVS.

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.

Decision Support Systems in Temporomandibular Joint Osteoarthritis: A review of Data Science and Artificial Intelligence Applications.

With the exponential growth of computational systems and increased patient data acquisition, dental research faces new challenges to manage a large quantity of information. For this reason, data science approaches are needed for the integrative diagnosis of multifactorial diseases, such as Temporomandibular joint (TMJ) Osteoarthritis (OA). The Data science spectrum includes data capture/acquisition, data processing with optimized web-based storage and management, data analytics involving in-depth statistical analysis, machine learning (ML) approaches, and data communication. Artificial intelligence (AI) plays a crucial role in this process. It consists of developing computational systems that can perform human intelligence tasks, such as disease diagnosis, using many features to help in the decision-making support. Patient's clinical parameters, imaging exams, and molecular data are used as the input in cross-validation tasks, and human annotation/diagnosis is also used as the gold standard to train computational learning models and automatic disease classifiers. This paper aims to review and describe AI and ML techniques to diagnose TMJ OA and data science approaches for imaging processing. We used a web-based system for multi-center data communication, algorithms integration, statistics deployment, and process the computational machine learning models. We successfully show AI and data-science applications using patients' data to improve the TMJ OA diagnosis decision-making towards personalized medicine.

Can palatal splint improve stability of segmental Le Fort I osteotomies?

OBJECTIVES: The purpose of this study was to evaluate the influence of a palatal splint on stability in multi-segment maxillary osteotomies. SETTING AND SAMPLE POPULATION: Retrospective series of fifty-one adult patients, consecutively operated with bilateral sagittal split osteotomy (BSSO) and three-piece maxillary osteotomies, divided according to the use of a palatal splint (Group 1, n = 30) or no palatal splint (Group 2, n = 21). MATERIALS AND METHODS: Maxillary surgical casts (T1) and post-retention casts (T2), taken at least six months after discontinuation of orthodontic retention, were digitized (MicroScribe-3DX), measured and compared. Fifty-one landmarks were identified on the maxillary, transverse dimension changes and arch length were calculated. Longitudinal changes in all measurements were assessed by t test. RESULTS: Post-surgical transverse instability in group 1 ranged from 0.3 ± 0.4 to -1.3 ± 0.2 mm and was statistically significantly smaller than in group 2 that ranged from -1.0 ± 0.3 to -2.5 ± 0.5 mm. CONCLUSIONS: The use of a palatal splint after segmental Le Fort I maxillary osteotomy improved transverse stability in the posterior region. The post-surgical transverse instability occurred only between canine gingival points and thus suggesting no clinical relevance.

Cone-beam computed tomography airway measurements: Can we trust them?

INTRODUCTION: Pharyngeal airway space (PAS) assessment has been used in the past for a better understanding of orthodontic and surgical outcomes; however, this analysis could be unreliable. Our objective was to evaluate possible changes in the PAS reading in the same patient from their consecutive cone-beam computed tomography (CBCT) scans. METHODS: We evaluated a total of 27 patients' CBCT scans obtained at 2 time points with the use of a standardized acquisition protocol. The mean age at T0 was 31 years (range 17-62 years) and the follow-up records (T1) were taken after 4-6 months. Dolphin Imaging software was used to measure the volumes of the nasopharynx, oropharynx, and hypopharynx. We also evaluated the craniocervical position with the use of a lateral cephalogram. RESULTS: The variables exhibited high intraclass correlation coefficients (ICCs) when measuring the same CBCT scan twice (T0 and T0). However, The ICC between the measurements performed on the first and second CBCT scans (T0 and T1) showed that the only variable with high reproducibility between the 2 scans was cranial base, with an ICC >0.97. Average differences of 682.1 mm3, 2255.3 mm3, and 517.4 mm3 were found for the nasopharynx, oropharynx, and hypopharynx, respectively. Regarding the cephalometric angles, average differences between T0 and T1 scans were 0.6°, 2.7°, and 0.4° for OPT.CVT, OPT.SN, and cranial base, respectively. CONCLUSIONS: Different CBCT exams with equal scanning and patient positioning protocols can result in different 3D PAS readings. A more careful interpretation of CBCT volumetric data to achieve adequate conclusions of the clinical outcomes is necessary.

Effect of temporomandibular joint articular disc repositioning on anterior open-bite malocclusion: An orthodontic-surgical approach.

An anterior open bite is a challenge to orthodontic treatment; it has a multifactorial etiology and a wide range of intervention options. Temporomandibular joint (TMJ) disorders are a risk factor for the development of malocclusions such as the anterior open bite, especially in patients who have TMJ osteoarthritis with disc displacement. Articular disc repositioning surgery is an available option for treating this pathology, and it contributes to maintaining the condyles in a more stable position. The aim of this article was to report the case of a 20-year-old woman diagnosed with anterior open bite and TMJ osteoarthritis with bilateral disc displacement. The patient received both orthodontic treatment and TMJ disc repositioning surgery. Cone-beam computed tomography was used to create 3-dimensional models of the condyles with regional superposition, and assessment of bone remodeling was performed at different time intervals. Complete orthodontic and surgical treatment time was approximately 12 months. The results provided a stable correction of the patient's anterior open bite with a 2-year follow-up and favorable bone remodeling of the condyles due to functional improvement of the TMJ.

Osteoblast differentiation is enhanced by a nano-to-micro hybrid titanium surface created by Yb:YAG laser irradiation.

OBJECTIVES: The aim of this study was to analyze the capacity of a new modified laser surface to stimulate calvarial osteoblasts isolated from neonatal mouse bones to differentiate and form mineralized nodules. METHODS: Titanium discs were subjectezd or not to laser irradiation according to specific parameters and characterized. Osteoblasts isolated from neonatal mouse calvaria were cultured over the discs, and the capacity of these cells to proliferate (MTT assay), form mineralized nodules (Alizarin red assay), and enhance alkaline phosphatase activity (ALPase activity) was analyzed. Real-time PCR was used for quantification of gene expression. RESULTS: Laser-irradiated titanium discs (L) presented a rough nano-to-micrometric oxidized surface contrasting with the smooth pattern on polished discs (P). The Ra on the micrometric level increased from 0.32 ± 0.01 µm on P surfaces to 10.57 ± 0.39 µm on L surfaces. When compared with P, L promoted changes in osteoblast morphology, increased mineralized nodule formation in osteoblasts cultured on the surfaces for 14 days, and enhanced ALPase activity at days 7 and 14. Transcription factors triggering osteoblast differentiation (Runx2 and Sp7) and genes encoding the bone extracellular matrix proteins collagen type-1 (Col1a1), osteopontin (Spp1), and osteocalcin (Bglap) were upregulated in cells on L surfaces compared with those on P surfaces at days 1-14. CONCLUSION: Laser treatment of titanium surfaces created a rough surface that stimulated osteoblast differentiation. CLINICAL RELEVANCE: Laser treatment of titanium generates a reproducible and efficient surface triggering osteoblast differentiation that can be of importance for osteointegration.

Is three-piece maxillary segmentation surgery a stable procedure?

Objective: : The number of three-piece maxillary osteotomies has increased over the years; however, the literature remains controversial. The objective of this study was to evaluate the skeletal stability of this surgical modality compared with that of one-piece maxillary osteotomy. Methods: : This retrospective cohort study included 39 individuals who underwent Le Fort I maxillary osteotomies and were divided into two groups: group 1 (three pieces, n = 22) and group 2 (one piece, n = 17). Three cone-beam computed tomography scans from each patient (T1, pre-surgical; T2, post-surgical; and T3, follow-up) were used to evaluate the three-dimensional skeletal changes. Results: : The differences within groups were statistically significant only for group 1 in terms of surgical changes (T2-T1) with a mean difference in the canine region of 3.09 mm and the posterior region of 3.08 mm. No significant differences in surgical stability were identified between or within the groups. The mean values of the differences between groups were 0.05 mm (posterior region) and -0.39 mm (canine region). Conclusions: : Our findings suggest that one- and three-piece maxillary osteotomies result in similar post-surgical skeletal stability.

ShapeAXI: Shape Analysis Explainability and Interpretability.

ShapeAXI represents a cutting-edge framework for shape analysis that leverages a multi-view approach, capturing 3D objects from diverse viewpoints and subsequently analyzing them via 2D Convolutional Neural Networks (CNNs). We implement an automatic N-fold cross-validation process and aggregate the results across all folds. This ensures insightful explainability heat-maps for each class across every shape, enhancing interpretability and contributing to a more nuanced understanding of the underlying phenomena. We demonstrate the versatility of ShapeAXI through two targeted classification experiments. The first experiment categorizes condyles into healthy and degenerative states. The second, more intricate experiment, engages with shapes extracted from CBCT scans of cleft patients, efficiently classifying them into four severity classes. This innovative application not only aligns with existing medical research but also opens new avenues for specialized cleft patient analysis, holding considerable promise for both scientific exploration and clinical practice. The rich insights derived from ShapeAXI's explainability images reinforce existing knowledge and provide a platform for fresh discovery in the fields of condyle assessment and cleft patient severity classification. As a versatile and interpretative tool, ShapeAXI sets a new benchmark in 3D object interpretation and classification, and its groundbreaking approach hopes to make significant contributions to research and practical applications across various domains. ShapeAXI is available in our GitHub repository https://github.com/DCBIA-OrthoLab/ShapeAXI.