Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 16 appraisals
Acta odontologica Scandinavica
Predicting gingival embrasure risk after invisible orthodontics using multimodal data and machine learning
OBJECTIVE: To develop and validate a risk prediction model for gingival embrasures after clear aligner therapy using multimodal oral data. METHODS: A retrospective study of 340 patients (December 2022-June 2025) was randomly divided into training (n = 238) and validation (n = 102) sets (7:3). Univariate analysis, multivariate logistic regression, and least absolute shrinkage and selection operator regression were applied to identify independent risk factors. Three machine learning models - random forest (RF), logistic regression, and support vector machine - were constructed based on seven core variables. Model performance was assessed using area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis, and Shapley Additive Explanations (SHAP) values for interpretability. RESULTS: No significant baseline differences existed between sets (p > 0.05). Seven indicators were identified (p < 0.05). Multivariate analysis confirmed percentage of bleeding on probing-positive sites, interproximal alveolar bone height, and relative movement of adjacent teeth at target site as independent risk factors, while gingival thickness, proximal contact area, interdental papilla height, and buccal/lingual bone plate thickness were protective factors (p < 0.05). The RF model performed best: training AUC = 0.849 (95% CI: 0.786-0.912), validation AUC = 0.815 (95% CI: 0.720-0.910), with good calibration and net benefit. SHAP analysis highlighted gingival thickness and interproximal alveolar bone height as key predictors. CONCLUSION: A risk prediction model for post-clear aligner gingival embrasures was successfully developed and validated using multimodal oral data, with RF as the optimal algorithm. The model exhibits good discrimination, calibration, and clinical utility, which can be used as an objective auxiliary tool for individualized risk prediction following clear aligner therapy and supplement traditional clinical empirical judgment.
23 July 2026
Read appraisal →Clinical oral investigations
Predicting occlusal force distribution from intraoral scans using deep learning: a pilot study
OBJECTIVES: This pilot study investigated whether occlusal force distribution measured by T-Scan can be predicted from intraoral scan images using deep learning. METHODS: Paired intraoral scan and T-Scan images from 26 subjects were analyzed. Following quality control, data were randomly partitioned at the subject level into training (n = 18), validation (n = 4), and independent test (n = 4; 8 image pairs) sets. Five deep learning architectures (U-Net, ResNet-UNet, Vision Transformer, CNN-Attention, and Pix2Pix) were trained to predict T-Scan force maps from occlusal scan images. Structural Similarity Index (SSIM) was predefined as the primary endpoint. Nonparametric statistical comparisons (Friedman test with Holm-corrected Wilcoxon post hoc analysis) and bootstrap confidence intervals were used. RESULTS: A significant difference in SSIM was observed among models (χ² = 19.3, p = 0.00069). CNN-Attention achieved the highest structural similarity (mean SSIM 0.501 ± 0.030), significantly outperforming U-Net, Vision Transformer, and ResNet-UNet (adjusted p < 0.05), while not differing significantly from Pix2Pix. Pix2Pix demonstrated the lowest mean absolute error (0.237 ± 0.018), indicating superior pixel-level intensity approximation. Validation curves revealed distinct optimization behaviors, with adversarial training showing late-stage improvements in SSIM. CONCLUSIONS: Deep learning models approximated T-Scan occlusal force maps from intraoral scan images in this pilot dataset. These findings suggest that artificial intelligence may help estimate functional occlusal force distribution from morphological intraoral scan data. Further studies with larger datasets are required to validate clinical applicability. CLINICAL SIGNIFICANCE: Computational prediction of functional occlusal force maps from intraoral scans may represent a future step toward integrating morphological and functional data within digital dentistry workflows; however, larger validation studies are required before clinical application.
7 July 2026
Read appraisal →Clinical oral investigations
Temporal dynamics of human biomarkers of orthodontically-induced root resorption: A systematic review
OBJECTIVE: To summarize time-related biomarker changes in orthodontically-induced root resorption (OIRR), classify biomarkers by function, and examine trajectories across OIRR stages. MATERIALS AND METHODS: PubMed/MEDLINE, Embase, Scopus, Web of Science, CENTRAL, CINAHL and grey literature, were searched from inception to October 1, 2025. Reference lists were manually searched. The risk of bias was assessed using the Cochrane RoB 2 for RCTs and ROBINS-I for non-RCTs. The certainty of evidence was evaluated via GRADE. Because of heterogeneity, findings were synthesized narratively. RESULTS: From 11,434 records, 14 studies (225 participants) were included. Gingival crevicular fluid, saliva and serum were analyzed, mainly using enzyme-linked immunosorbent assay. RoB was 'moderate'- 'serious' (non-RCTs) or 'some concerns' (RCTs). Studies reported a rise of pro-inflammatory cytokines right after force application. Subsequent force application phases showed signals of osteoclastic activity and regulation, followed by detection of root hard tissue degradation products that correlated with OIRR volume. Higher baseline salivary secretory IgA and anti-dentin humoral responses and lower baseline mineralized-tissue regulators were associated with greater OIRR severity. CONCLUSION: Evidence suggests time-related changes involving early inflammatory activation and later matrix degradation, which may be shaped by interaction between orthodontic force and patients' biological responses. Standardized multicenter prospective cohorts are needed for validation. CLINICAL RELEVANCE: Early cytokines changes and later signals of osteoclast regulation and root hard tissue degradation may help define candidate monitoring method for OIRR during treatment. Baseline immune profiles could inform future risk stratification, while bone turnover biomarkers may reflect tooth movement rather than resorption.
26 June 2026
Read appraisal →Physics in medicine and biology
Interlayer-aware postoperative facial appearance prediction in orthognathic surgery with bio-geometric guidance
Objective.Accurate prediction of postoperative facial appearance is essential for orthognathic surgical planning, yet remains challenging due to the nonlinear biomechanical coupling between bone and soft tissue. While deep learning methods offer a faster alternative to traditional biomechanical simulation, they typically map bony displacements directly to facial surface deformation, overlooking the intervening soft-tissue layers with distinct biomechanical properties through which displacement is progressively transmitted. Moreover, the many-to-one effect of regional bony movements on each soft-tissue point remains insufficiently captured.Approach.We propose a novel network that introduces an interlayer mixture-of-experts mechanism to decouple bone-to-surface deformation propagation into three biomechanically inspired proxy representations at different tissue depth levels. Since the contribution of each depth level varies across individuals and facial regions, a gated routing network adaptively weights each layer's contribution, providing a data-driven approximation of spatially heterogeneous, patient-specific deformation transmission. Additionally, a bio-geometric convolution module captures regional bony influences through elastic-weighted neighborhood aggregation.Main results.Evaluations on 88 orthognathic surgery patients demonstrate that the proposed method achieves a mean whole-face error of 1.39 mm, below the 2 mm clinical threshold, with statistically significant improvements over all baselines (p<0.05) and the highest clinician acceptance rate (94%).Significance.By incorporating bio-geometric priors into a deep learning framework, our approach enables a more physically grounded and interpretable prediction paradigm, supporting efficient and clinically reliable surgical planning.
21 June 2026
Read appraisal →Clinical oral investigations
Changes in the sagittal pharyngeal airway dimension following levelling and alignment in class II division 2 patients: a prospective clinical trial
OBJECTIVES: This study aimed to evaluate changes in the sagittal pharyngeal airway dimensions (SPAD) and the related mandibular position following maxillary levelling and alignment in late adolescent Class II division 2 (Class II/2) patients. MATERIALS AND METHODS: Thirty Class II/2 patients (mean age 14.07 ± 1.21 years) were included. Pre-treatment orthodontic records were acquired, including panoramic and lateral cephalometric x-rays (T0), photographs, and study models, followed by the onset of levelling and alignment in the upper arch. Lateral cephalometric x-rays and photographs were repeated six months later at the termination of this stage (T1), and measurements were performed in comparison to T0. SPAD was assessed at the nasopharyngeal, oropharyngeal, and laryngopharyngeal levels. Mandibular position was evaluated using the Ba-Ar distance, while sagittal and vertical skeletal relationships were assessed using SNA°, SNB°, ANB°, and FMA°. Upper incisor inclination was accounted for using the U1-FH° measurement. RESULTS: Alignment of the maxillary arch in Class II/2 patients elicited a significant SPAD increase at the nasopharyngeal and the oropharyngeal levels (p < 0.001), with a non-significant change evoked at the laryngopharyngeal level (p = 0.42). Antero-posteriorly, a significant increase in the SNAº, SNBº, together with a significant decrease in the ANBº angle have been documented at T1. A significant increase in the Ba-Ar has been noted at T1 in contrast to T0, in addition to a significant reduction in the vertical dimension (FMAº). Dentally, a significant increase in the U1-FHº has been documented following the implemented treatment stage (p < 0.001). CONCLUSIONS: Results of this study may suggest that maxillary levelling and alignment in Class II/2 patients during the decelerating growth phase is associated with augmentation of the nasopharyngeal and oropharyngeal airways. These changes coincide with the significant labial tipping of the maxillary incisors and subsequent functional mandibular advancement, which collectively improve the intermaxillary relationship and reduce the vertical skeletal dimension. While natural growth cannot be entirely excluded, the rapid change suggests that these improvements are closely linked to the functional mandibular response. CLINICAL RELEVANCE: Maxillary alignment in Class II division 2 cases may enhance upper airway patency by triggering a forward functional mandibular response, highlighting a potential link between orthodontic correction and upper airway patency. Name of the Registry: ClinicalTrials.gov Trial Registration Number: NCT06602518 Date of Registration: 19/09/2024 "Retrospectively registered". URL: https://clinicaltrials.gov/study/NCT06602518?cond=nct06602518&rank=1.
7 June 2026
Read appraisal →Clinical oral investigations
Exploring orofacial myofunctional therapy and its core components in mixed-dentition anterior open bite: the ombit study
OBJECTIVES: To explore the therapeutic effects of OMT in children with AOB in the mixed dentition phase, including a reduced form of OMT (focusing exclusively on breathing pattern and resting postures; OMTBPO) and traditional OMT (also including strength training and correction of swallowing; OMTBPSS). MATERIALS AND METHODS: In this double-baseline, longitudinal study, eight children with AOB (mean age 8.1 years old) were randomly allocated to the OMTBPO (n = 4) or OMTBPSS group (n = 4). Each participant received 7.5 h of OMT across 10 sessions. Outcomes included occlusal parameters, Photographic Open bite Severity Index (POSI) and Peer Assessment Rating (PAR) using 3D intraoral scans, orofacial function based on clinical and standardized assessment (Orofacial Myofunctional Evaluation with Scores; OMES), strength and endurance measurements, and oral health-related quality of life (child oral health impact profile - ortho; COHIP-ortho). RESULTS: Immediately post-therapy, anterior tongue strength had significantly increased in the full sample. At three-month follow-up, palatal volume, lateral lip endurance, and OMES function and deglutition scores had significantly increased, and vertical overbite had decreased. The PAR index showed a significant proportional reduction of 35% from baseline to the last follow-up moment (on average 6 months after therapy). The OMTBPO group showed significantly lower rates of atypical tongue posture and interdental articulation at six-month follow-up. CONCLUSION AND CLINICAL RELEVANCE: This study took a first step in exploring the effectiveness of OMT components in mixed-dentition AOB. Measurable short- to mid-term functional and occlusal improvements were found, and comparable outcomes between protocols suggest that posture-focused OMT may be an efficient interceptive approach. Further research including randomized controlled trials with larger sample sizes is required to provide evidence on efficacy and long-term stability.
1 June 2026
Read appraisal →Progress in orthodontics
Evaluation of artificial intelligence-based cephalometric analysis compared to the manual method.
BACKGROUND: The application of artificial intelligence (AI) to cephalometry has aroused growing interest due to its potential to optimize clinical workflows and improve efficiency. This study aimed to compare the differences and reproducibility of cephalometric measurements obtained using three methods: manual, WebCeph™ and AngelAligner™. METHODOLOGY: 30 patients were analysed, evaluating 9 measurements generated by the three methods at two different timepoints (T1 and T2). RESULTS: WebCeph™ presented significant differences as compared to the manual method in 7 of the 9 measurements (SNA, SNB, ANB, U1-APg, L1-APg, GoGn-SN, U1-L1), while AngelAligner™ displayed significant differences with the manual method in 4 measurements (SNA, U1-APg, L1-APg and U1-L1). Significant differences were found between the two AI-based systems in 7 of the 9 measurements. Reproducibility was high at all angles with the manual method, except for U1-L1, NLA, and LFH. The ICC was 1 for all measurements for both AI-based systems, indicating optimal reproducibility. When grouping by malocclusions, however, WebCeph™ presented greater differences with the manual method than AngelAligner™. CONCLUSION: AI-based methods offered greater reproducibility than the manual method, but still require human supervision.
31 May 2026
Read appraisal →Dental materials journal
Domain shift analysis of deep learning models for tooth detection in pediatric panoramic radiographs
The aim of this study was to externally validate object detection models for comprehensive tooth detection on pediatric panoramic radiographs, quantify the impact of domain shift across institutions and imaging protocols, and compare YOLOv8 and YOLOv10. Two datasets were used: an internal set of 200 images of early mixed dentition without bite blocks, and an external, open-source set of 192 images acquired with bite blocks. Performance was assessed using mean average precision (mAP), per‑class AP, precision‑recall curves, and confusion matrix. Overall performance was high but domain dependent. mAP was 0.958/0.941 (YOLOv8/v10) in Experiment 1; 0.910/0.906 in Experiment 2; and 0.906/0.901 in Experiment 3. Error analysis revealed domain‑specific failure modes: bite blocks were occasionally mistaken for the primary central incisor. Deep learning enables accurate and comprehensive detection of teeth on pediatric panoramic radiographs. However, domain shift alters error patterns and impairs the detection of rare yet clinically important classes.
25 May 2026
Read appraisal →Clinical oral investigations
Three-dimensional stability during orthodontic retention: A comparative analysis of conventional, CAD/CAM-fabricated, and robotically bent fixed retainers versus removable appliances
OBJECTIVES: To evaluate three-dimensional anterior tooth position changes during orthodontic retention and to compare the effectiveness of three distinct fixed retainer fabrication designs including robotically bent retainers alongside with removable retainers. MATERIALS AND METHODS: This retrospective cohort study included 113 patients (226 dental arches; mean retention period 1.3 ± 0.6 years). Combined fixed lingual plus removable retention (group 1, n = 148 arches) comprised CAD/CAM Memotain, conventional multistranded stainless-steel (Twistflex), and robotically bent retainers; group 2 (n = 78 arches) received removable retention only. Digital model superimposition using a three-dimensional tooth-specific coordinate system quantified rotational and translational tooth movements, Little's Irregularity Index (LII), transverse arch widths, and bonding failures. RESULTS: Combined fixed and removable retention showed higher stability than removable-only retention (55% vs. 33% stable arches). Removable retention increased the odds of moderate-to-severe instability (maxilla OR 4.37, 95% CI 1.74-10.98; mandible OR 14.35, 95% CI 3.24-63.56), whereas fixed lingual retention was protective (maxilla OR 0.23, 95% CI 0.09-0.58; mandible OR 0.07, 95% CI 0.02-0.31). Instability mainly involved canine rotations and vertical translations. Robotically bent and CAD/CAM Memotain retainers showed the lowest movement magnitudes and variability, with minimal LII increases. Intercanine and intermolar widths remained stable across fixed retainers. Bonding failures (25%) strongly predicted severe instability. CONCLUSIONS: Three-dimensional tooth movements during retention mainly involve canine rotations and vertical translations. All fixed retainer designs (Twistflex, CAD/CAM-fabricated, and robotically bent) provided greater stability than removable-only retention. CLINICAL RELEVANCE: All fixed retainer designs require regular monitoring of bonding integrity; digitally fabricated and robotically bent retainers demonstrated marginally lower dimensional variability, which may be clinically relevant over longer observation periods.
25 May 2026
Read appraisal →Dental press journal of orthodontics
Accuracy of cephalometric landmark identification on artificial intelligence-based software: a comparative study
INTRODUCTION: Cephalometry has long been a cornerstone of orthodontic diagnosis and treatment planning. While traditionally performed manually, cephalometric analysis has increasingly shifted toward semi-automated or fully automated computer-based software, due to advances in technology. OBJECTIVE: This study aims to evaluate and compare the accuracy of anatomical landmarks identified manually by orthodontists using digital tools versus those identified automatically by various artificial intelligence (AI)-based cephalometric software. METHODS: A total of 25 lateral cephalometric radiographs were selected for analysis. Manual landmark identification was independently performed by two orthodontists using the digital cephalometric software NemoCeph. These were compared to the automatic landmark identifications generated by two AI-based programs: CephNinja and WebCeph™. A total of 17 anatomical landmarks, 15 hard tissue and 2 soft tissue points, were evaluated. The 17 cephalometric landmarks selected in this study are among the most commonly used and reliably identifiable reference points in the literature. To facilitate direct visual comparison, landmark positions derived from all three methods were overlaid using semi-transparent image layers. One-way ANOVA and intraclass correlation coefficient (ICC) tests were conducted to assess inter-method differences. The threshold for statistical significance was set at p < 0.05 and p < 0.01. RESULTS: Statistically significant differences were observed at the porion, basion, gonion, and the apexes of the upper and lower incisors (p < 0.05). Compared to the manual tracings in NemoCeph, AI-based software generally placed these landmarks in more distal and superior positions. Among the AI tools, CephNinja showed greater similarity to NemoCeph for the gonion and basion points, while WebCeph™ was closer at the incisor apexes. CONCLUSION: Although AI-based cephalometric software offers rapid and reproducible assessments, certain anatomical landmarks - particularly porion, basion, gonion, and the apexes of the upper and lower incisors - demonstrate statistically significant positional discrepancies, compared to manual tracings. Clinicians should be cautious when relying exclusively on automated analyses for diagnostic or treatment planning purposes, since AI struggles with certain landmarks, due to algorithmic limitations.
21 May 2026
Read appraisal →Clinical oral investigations
Comparison of W. Arnett's cephalometric analysis measurements performed using artificial intelligence and manual measurements.
OBJECTIVES: This study aimed to compare the linear and angular Arnett cephalometric measurements obtained from WebCeph Premium™ with those from manual cephalometric analysis and to assess the reliability of the automated measurements. METHODS: Thirty-two pre-treatment lateral cephalograms of patients were randomly selected. Images were calibrated, 29 landmarks were manually traced, and 40 parameters were recorded by two orthodontists (6 angular, 34 linear). WebCeph Premium™ (version 2.0.0, AssembleCircle Corp., Gyeonggi-do, Republic of Korea) automatically identified landmarks and performed Arnett's cephalometric analysis. After 15 days, orthodontists reassessed the radiographs. Reliability and repeatability were evaluated using the intraclass correlation coefficient (ICC), which exceeded 0.95, indicating excellent agreement. Normality was assessed using the Shapiro-Wilk test, and group comparisons were performed with paired t-tests. RESULTS: For Researcher 1, initial ICC values ranged from 0.003 to 0.984 and final values from 0.617 to 0.996. For Researcher 2, initial ICC values ranged from 0.022 to 0.999 and final values from 0.601 to 0.999. Inter-observer ICC values ranged from 0.03 to 0.984, with most measurements showing high agreement. Manual tracing and WebCeph Premium™ showed high agreement, with ICC values ranging 0.913 and 0.995. CONCLUSIONS: WebCeph Premium™ demonstrates high concordance with manual Arnett analyses; however, further refinement of certain parameters is necessary to enhance AI precision. CLINICAL RELEVANCE: The utilization of WebCeph Premium™ within clinical settings has the capacity to enhance the efficiency of cephalometric analysis for orthognathic cases by minimizing manual workload and inter-examiner variability. This can result in more consistent and efficient treatment planning.
13 May 2026
Read appraisal →Sleep & breathing = Schlaf & Atmung
Efficacy of the twin-block appliance for pediatric obstructive sleep apnea: an updated systematic review and meta-analysis
OBJECTIVE: To provide an updated and robust assessment of the efficacy of the Twin-Block appliance in the treatment of pediatric obstructive sleep apnea (OSA). A previous meta-analysis indicated a potential benefit, but was limited by the quality of available evidence and is now outdated. This study aims to synthesize all current evidence, including recent high-quality randomized controlled trials (RCTs). METHODS: This systematic review and meta-analysis was conducted following PRISMA guidelines. A comprehensive search of PubMed, Embase, Cochrane Library, and Web of Science was performed up to October 2025. We included RCTs, non-randomized controlled trials (NRCTs), and case series evaluating the effect of the Twin-Block appliance on respiratory parameters in children (< 18 years) with OSA. The primary outcome was the change in the Apnea-Hypopnea Index (AHI). A random-effects model was used to calculate the pooled Mean Difference (MD) and 95% Confidence Intervals (CIs). Sensitivity analyses were performed, and the risk of bias was assessed using design-specific tools (RoB 2, ROBINS-I, and the JBI Critical Appraisal Checklist). RESULTS: Eight studies, comprising a total of 259 patients, were included in the systematic review. The meta-analysis of seven studies (n = 219) demonstrated that Twin-Block therapy resulted in a statistically significant and clinically meaningful reduction in AHI (pooled MD: -6.68, 95% CI: -8.76 to -4.60; p < 0.0001). Significant heterogeneity was observed across studies (I² = 89.1%). A recent high-quality RCT, while not poolable due to a different outcome measure, provided mechanistic evidence by showing a significant increase in oropharyngeal airway volume with Twin-Block treatment compared to a control group. The findings remained robust in sensitivity and leave-one-out analyses. CONCLUSION: This updated meta-analysis provides strengthened evidence that the Twin-Block appliance is an effective treatment for reducing the severity of OSA in appropriately selected children. The therapeutic effect is likely mediated by structural enlargement of the upper airway. Despite the heterogeneity and limitations in the primary literature, these findings support the consideration of the Twin-Block appliance as a viable non-surgical intervention for pediatric OSA, particularly in patients with mandibular retrognathia. Future large-scale RCTs are warranted to confirm these findings and establish long-term effectiveness.
12 May 2026
Read appraisal →Medical image analysis
MICCAI STS 2024 challenge: Semi-supervised instance-level tooth segmentation in panoramic X-ray and CBCT images
Orthopantomogram (OPGs) and Cone-Beam Computed Tomography (CBCT) are vital for dentistry, but creating large datasets for automated tooth segmentation is hindered by the labor-intensive process of manual instance-level annotation. This research aimed to benchmark and advance semi-supervised learning (SSL) as a solution for this data scarcity problem. We organized the 2nd Semi-supervised Teeth Segmentation (STS 2024) Challenge at MICCAI 2024. We provided a large-scale dataset comprising over 90,000 2D images and 3D axial slices, which includes 2380 OPG images and 330 CBCT scans, all featuring detailed instance-level FDI annotations on part of the data. The challenge attracted 114 (OPG) and 106 (CBCT) registered teams. To ensure algorithmic excellence and full transparency, we rigorously evaluated the valid, open-source submissions from the top 10 (OPG) and top 5 (CBCT) teams, respectively. All successful submissions were deep learning-based SSL methods. The winning semi-supervised models demonstrated impressive performance gains over a fully-supervised nnU-Net baseline trained only on the labeled data. For the 2D OPG track, the top method improved the Instance Affinity (IA) score by over 44 percentage points. For the 3D CBCT track, the winning approach boosted the Instance Dice score by 61 percentage points. This challenge demonstrates the potential benefit benefit of SSL for complex, instance-level medical image segmentation tasks where labeled data is scarce. The most effective approaches consistently leveraged hybrid semi-supervised frameworks that combined knowledge from foundational models like SAM with multi-stage, coarse-to-fine refinement pipelines. Both the challenge dataset and the participants' submitted code have been made publicly available on GitHub (https://github.com/ricoleehduu/STS-Challenge-2024), ensuring transparency and reproducibility.
9 May 2026
Read appraisal →American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
Prediction of skeletal maturity using machine learning based on multiple biological indicators
INTRODUCTION: Accurate assessment of skeletal maturity is essential for orthodontic treatment planning and growth modulation. This study aimed to develop a comprehensive machine learning (ML) model to predict skeletal maturation stages using cervical vertebral morphology, dental maturation stage (DMS), gender, and age. METHODS: A total of 860 patients with lateral cephalograms, panoramic, and hand-wrist radiographs were included. A baseline model using 6 cervical morphologic parameters was compared with enhanced models incorporating DMS, gender, and age. Six ML algorithms were evaluated, with CatBoost showing the highest performance. Feature importance was analyzed using 2 methods, and the final model was assessed using a confusion matrix, receiver operating characteristic curves, and calibration curves. RESULTS: Incorporating DMS, gender, and age significantly improved predictive performance. The CatBoost model achieved an area under the receiver operating characteristic curve of 0.924, an area under the precision-recall curve of 0.806, and an F1-score of 0.752. Feature importance analysis confirmed the strong predictive contribution of DMS, especially the mandibular second molar. The final model demonstrated high accuracy, strong discrimination, and good calibration across all skeletal maturity stages. CONCLUSIONS: This study presents a novel and clinically practical ML model for skeletal maturity prediction, integrating routinely acquired orthodontic records. The results demonstrate that combining DMS with cervical morphology, age, and gender enhances prediction accuracy, offering a reliable alternative to traditional hand-wrist assessments.
5 May 2026
Read appraisal →Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
Machine learning-powered decoding of facial complexity: a diamond-and-triangle geometry for frontal facial analysis
Facial configuration is far more complicated and variable than the representations offered by a couple of conventionally used mathematic laws, necessitating development of a comprehensive and interracially comparable system for frontal facial analysis. In this study, based on morphological analysis of 33 Caucasian and 33 Asian female faces with high perceived facial attractiveness (i.e. facial esthetic appeal in frontal views), validated by independent layperson ratings, in contrast with 'ordinary' faces of 67 Caucasian and 67 Asian females, a machine learning-powered model, building on a diamond-and-triangle geometric framework, was constructed to decode the esthetic-determining facial traits and quantify the probability of a face conforming to the generally accepted attractiveness. The SVM models demonstrated excellent efficacy in this task, based on the input of 21 facial proportion indices derived from the diamond-and-triangle geometry. By interpreting the contribution of input features to each model's diagnosis, the Asian and Caucasian faces shared prioritization of proportional coordination of facial height with mid-facial width and outer canthal distance in determining facial harmony, and exhibited ethnicity-specific variations in hierarchical importance of other proportion indices. The highly coordinated diamond-and-triangle geometric framework distinguished the attractive faces from the ordinary, with cross-ethnic similarities between Caucasian and Asian females. The machine learning-powered model, integrated with the diamond-and-triangle geometry, provides references for cross-ethnic facial analysis and customized treatment planning.
4 May 2026
Read appraisal →PloS one
The impact of physical adjunctive interventions on outcomes of clear aligner treatment: A systematic review of randomized controlled trials
INTRODUCTION: Physical adjunctive interventions (PAIs), including vibration devices and low-level laser therapy, are promoted to accelerate tooth movement, improve aligner tracking, and reduce discomfort in clear aligner treatment (CAT), but randomized evidence remains inconsistent. This systematic review aimed to assess whether PAIs improve CAT outcomes in terms of objective tooth-movement metrics, aligner tracking, overall treatment efficiency, and patient-centered outcomes, and to appraise the risk of bias and the certainty of the evidence at the outcome level. METHODS: Comprehensive electronic searches of PubMed, Embase, Scopus, Web of Science, and CENTRAL were conducted from database inception (earliest available indexing date in each database) to 30 June 2025 (coverage cut-off). Grey literature searching and hand-searching were also performed, with no restrictions on language or publication status. Eligible studies were human randomized controlled trials (RCTs) comparing CAT combined with PAIs versus CAT alone or sham interventions. Two independent reviewers performed study selection and data extraction, with risk of bias assessed using the Cochrane RoB 2 tool and certainty of evidence appraised with GRADE. Random-effects meta-analyses were conducted when feasible; otherwise, results were narratively summarized. The protocol was prospectively registered (PROSPERO CRD420251132229). RESULTS: Seven RCTs involving 266 participants were included. No significant improvement in Little's Irregularity Index (maxilla MD = 0.08, p = 0.869; mandible MD = 0.44, p = 0.487). Vibration improved aligner tracking under a 7-day change schedule (p = 0.003) but not case completion (p = 0.999). Overall compliance was approximately 77%, and no between-group difference was observed (p = 0.390). Pain was slightly lower on days 1-3 only (p < 0.05); no quality-of-life benefits were observed; periodontal indices remained unchanged; biomarkers showed inconsistent results. The certainty of evidence was low to very low, suggesting that further well-designed RCTs are likely to change the effect estimates and may alter the conclusions. CONCLUSIONS: Across objective tooth-movement metrics, aligner tracking, treatment efficiency, and patient-centered outcomes, current randomized evidence does not demonstrate a consistent benefit of physical adjunctive interventions in clear aligner treatment. Interpretation is limited by outcome-level risk of bias concerns and low to very low certainty of evidence.
9 Apr 2026
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