Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 13 appraisals
BMC oral health
Accuracy of virtual facebow compared to conventional facebow: a systematic review
BACKGROUND: Digital technologies are rapidly evolving and have been introduced across many fields in dentistry, particularly prosthetic and restorative dentistry. In digital treatment planning, virtual articulators are helpful in simulating complex maxillomandibular movements and analyzing occlusion. Despite these advancements, evidence-based literature standardizing the transfer of clinical data to virtual environments remains limited. The aim of this review was to explore the accuracy of various methods in transferring maxillary digital models to virtual articulators compared with conventional facebow techniques. METHODS: The research (PICO) question focused on assessing the accuracy of virtual facebow transfer compared to that of conventional facebows. An electronic search was performed in 3 databases between January 2014 to December 2025: PubMed/MEDLINE, Scopus, and Web of Science. Eligibility criteria included in vitro studies, clinical trials, case reports, clinical reports, and clinical techniques that evaluated the accuracy of virtual articulation in fixed prosthodontics for dentate patients, published in English. Studies were excluded if they were not in English, involved orthognathic surgery, focused on edentulous patients, or were published before 2014. A comprehensive search was conducted in December 2025. Two reviewers (PE, HA) independently examined the identified papers in accordance with the predetermined qualifying criteria. For each included study, publication details, study characteristics, and methodological aspects were documented. RESULTS: The total number of articles retrieved was 1,916. Of these articles, 10 met the inclusion criteria and were used for qualitative synthesis. Several techniques were reported for transferring the maxillary model to virtual articulator, such as digital photographs, face scans with or without intraoral or extraoral scan body, smartphone applications, digital photographs using Kois glasses, CBCT, and stereophotogrammetry. Most studies showed comparable accuracy of different digital methods that transfer the maxillary cast to the articulator. However, one study showed that the photography-based method demonstrated superior trueness and precision compared to both facial scanning and the conventional facebow technique. Another study found that the accuracy of positioning a maxillary digital scan relative to a 3D facial scan using a virtual facebow technique is highly dependent on the facial scanning method employed. CONCLUSIONS: Virtual facebow transfer can be recorded using techniques such as digital photographs, face scans, virtual facebow software, and smartphone scanning. Virtual facebow systems demonstrate promising repeatability and clinically acceptable transfer accuracy in selected workflows; however, due to heterogeneity among available studies, definitive conclusions regarding any improvement over conventional techniques cannot yet be drawn. Further standardized in vitro and clinical studies with large sample sizes, standardized assessments, and comparison methods are needed to draw meaningful conclusions in order to prove precedence of one method over the other. However, the use of a device to facilitate the transfer of a virtual maxillary cast to a virtual articulator has been promising.
13 July 2026
Read appraisal →The Journal of prosthetic dentistry
A simulation-based study of 3D printing angle optimization by integrating deep learning and NSGA-III for prosthesis and retainer manufacturing
STATEMENT OF PROBLEM: Current dental 3-dimensional (3D) printing workflows lack automated tools for optimizing build orientations that preserve critical clinical surfaces. Existing slicing software programs often require manual adjustment and may place supports in functional areas. PURPOSE: The purpose of this simulation-based study was to develop and evaluate a computational framework by integrating deep learning and Non-dominated Sorting Genetic Algorithm III (NSGA-III) to optimize build orientations for dental crowns and retainers by minimizing the support area (SA) and printing height (PH). A secondary objective was to assess the effects of angular resolution and mesh simplification on optimization outcomes. MATERIAL AND METHODS: A 3-stage framework was developed using PointNet++ for cast classification, MeshSegNet for feature recognition, and NSGA-III for dual-objective optimization. Mesh simplification was applied to enhance computational efficiency. Twenty-four dental casts were tested in a repeated-measures design at 1, 5, and 10 degrees angular resolutions. Nonparametric statistical analyses were performed using the Friedman test (α=.05) with post hoc Wilcoxon signed-rank tests and Bonferroni correction. RESULTS: Mesh simplified reduced computation time by 87.0% without altering optimal orientation distributions. Reduced angular resolution significantly affected PH and SA outcomes under most conditions (P<.001), except for SA in the PH-optimized group (P=.004). Post hoc analysis confirmed the largest degradation in accuracy between 1 and 10 degrees. Compared with commercially available software programs, the proposed framework consistently avoided the placement of supports on critical surfaces. CONCLUSIONS: The proposed framework provides an automated and efficient approach to optimizing build orientations in dental 3D printing. Although simulation results demonstrated improved optimization performance, further experimental validation is required before clinical application.
2 July 2026
Read appraisal →The International journal of prosthodontics
Accuracy of Stereophotogrammetry in Implant Scanning Compared to Intraoral Scanning in Completely Edentulous Patients-A Systematic Review and Meta-analysis
PURPOSE: To evaluate the accuracy of full-arch implant impressions by comparing photogrammetry and intraoral scanning (IOS) techniques through a systematic review and meta-analysis following the Cochrane protocol. MATERIALS AND METHODS: A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science up to May 2024. Keywords based on the PICO question were used without time or language restrictions. In vitro studies assessing full-arch implant rehabilitations that compared photogrammetry to IOS were included. The risk of bias was assessed using the Quin Tool Method. Data were analyzed through metaanalysis in RevMan (Cochrane), grouping the results into three subgroups: angle deviation, precision (μm), and trueness (μm). RESULTS: The search yielded 6,348 studies: 11 met the eligibility criteria, and 7 provided sufficient data for quantitative meta-analysis. Photogrammetry demonstrated superior precision, with a mean difference (MD) of -0.03 (95% CI: -0.04, -0.02) and a high weight of 94.2%, indicating consistent evidence. The results for angle deviation were MD = -0.12 (95% CI: -0.16, -0.08, P < .00001), and for trueness the results were MD = -0.16 (95% CI: -0.19, -0.13, P < .00001). The photogrammetry group also showed favorable results. Although trueness exhibited expressive MD values, it had a lower overall weight (5.8%). CONCLUSIONS: Within the limitations of this study, photogrammetry significantly improves the accuracy of fullarch implant rehabilitations compared to IOS. These findings support the adoption of photogrammetry for more accurate and consistent outcomes in dental implant procedures.
28 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 →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 →Medical science monitor : international medical journal of experimental and clinical research
Preliminary Evaluation of Large Language Models in Kennedy Classification and Removable Partial Denture Planning: An In Silico Study
BACKGROUND This study evaluated text-based large language models for Kennedy classification and removable partial denture planning in partially edentulous cases, where comparative evidence on accuracy remains limited. MATERIAL AND METHODS Twenty-six partially edentulous cases, defined according to the Federation Dentaire Internationale tooth numbering system and classified as Kennedy Classes I to IV, were included. Case scenarios were constructed by an experienced prosthodontist. Questions were independently submitted to 4 large language models without prior training or prompting: ChatGPT-5.2, Claude Sonnet 4.6, Gemini 3 Flash, and Perplexity. Responses were evaluated by 2 independent prosthodontists (different from the case developer) - blinded to the identity of each model - using predefined criteria for Kennedy classification accuracy and prosthetic planning consistency. Statistical analyses included intergroup comparisons and effect size estimation. RESULTS Statistically significant differences were identified among the models in both tasks (P<0.001). For Kennedy classification, the effect size was moderate (Kendall's W=0.402). Gemini 3 Flash achieved the highest mean score, followed by ChatGPT-5.2, Perplexity, and Claude Sonnet 4.6. Similarly, significant differences were observed in removable partial denture planning performance (Kendall's W=0.424); Gemini 3 Flash scored significantly higher than the other models (P≤0.001). CONCLUSIONS Large language models showed variable performance under zero-shot conditions. Although Gemini 3 Flash achieved higher scores, the moderate effect sizes warrant cautious interpretation. These models may serve as adjunctive decision-support tools in prosthodontics but cannot replace clinical judgment.
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 prosthodontic research
AI-driven innovations for dental implant treatment planning: A systematic review
PURPOSE: This systematic review evaluates the effectiveness of artificial intelligence (AI) models in dental implant treatment planning, focusing on: 1) identification, detection, and segmentation of anatomical structures; 2) technical assistance during treatment planning; and 3) additional relevant applications. STUDY SELECTION: A literature search of PubMed/MEDLINE, Scopus, and Web of Science was conducted for studies published in English until July 31, 2024. The included studies explored AI applications in implant treatment planning, excluding expert opinions, guidelines, and protocols. Three reviewers independently assessed study quality using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies, resolving disagreements by consensus. RESULTS: Of the 28 included studies, four were high, four were medium, and 20 were low quality according to the JBI scale. Eighteen studies on anatomical segmentation have demonstrated AI models with accuracy rates ranging from 66.4% to 99.1%. Eight studies examined AI's role in technical assistance for surgical planning, demonstrating its potential in predicting jawbone mineral density, optimizing drilling protocols, and classifying plans for maxillary sinus augmentation. One study indicated a learning curve for AI in implant planning, recommending at least 50 images for over 70% predictive accuracy. Another study reported 83% accuracy in localizing stent markers for implant sites, suggesting additional imaging planes to address a 17% miss rate and 2.8% false positives. CONCLUSIONS: AI models exhibit potential for automating dental implant planning with high accuracy in anatomical segmentation and insightful technical assistance. However, further well-designed studies with standardized evaluation parameters are required for pragmatic integration into clinical settings.
13 Apr 2026
Read appraisal →Journal of prosthodontic research
Clinical performance, accuracy, and physical-mechanical properties of 3D-printed removable partial denture metal frameworks compared with conventionally and partially digitally produced frameworks: A systematic review.
PURPOSE: This systematic review evaluated the clinical performance, physical-mechanical properties, and accuracy of removable partial denture (RPD) frameworks fabricated using three-dimensional (3D) printing technologies-specifically, selective laser sintering (SLS), selective laser melting (SLM), and direct metal laser sintering (DMLS)-compared to those produced by conventional casting or methods using a partial digital workflow. STUDY SELECTION: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, a literature search was conducted in the PubMed, Scopus, Web of Science, and Cochrane databases in October 2024. Studies were included if they compared the fit, accuracy, mechanical and physical properties, and clinical outcomes of metal RPD frameworks made using 3D printing technologies with those produced using conventional casting or partial digital methods. The risk of bias was assessed using appropriate tools (modified CONSORT, ROB2, and ROBINS-I) based on the study design and a qualitative analysis was conducted. This study received no funding and was registered with PROSPERO (#CRD42024597225). RESULTS: Thirty studies were included: 12 compared 3D printing technologies with conventional casting, eight with partial digital methods, and 10 with both. Clinically, 3D-printed frameworks could improve retention and patient satisfaction. The laboratory results showed higher density, better mechanical properties (yield strength, surface roughness, and microhardness), and varied accuracy by component and method, with SLM and DMLS often outperforming conventional casting. The evidence was limited by methodological variability, a moderate risk of bias in many studies, and inconsistencies across the study designs and parameters. CONCLUSIONS: 3D-printed RPD metal frameworks demonstrated clinical accuracy and mechanical-physical performance comparable or superior to those of conventional and partially digital methods for RPD frameworks, with ongoing advances expected to further enhance their precision and clinical applicability.
13 Apr 2026
Read appraisal →Journal of prosthodontic research
The clinical performance of CAD/CAM-fabricated hybrid ceramic restorations in cases with severe wear: A 3-year clinical trial
PURPOSE: To evaluate the clinical use of CAD/CAM hybrid ceramics in patients with severe tooth wear. METHODS: Forty-nine patients with severe tooth wear underwent prosthetic management with 116 hybrid ceramic restorations. The restorations were evaluated at 1 week and at 3, 6, 12, 18, 24, 30, and 36 months after placement. The amount of wear on the restorations, antagonist teeth, and opposite teeth was compared using digital superimposition analysis. A questionnaire was used to evaluate participants' satisfaction with the restorations. Complications were recorded, and survival rates were estimated using Kaplan-Meier analysis. RESULTS: The clinical performance of the restorations was stable for esthetic, functional, and biological parameters up to 3 years, except for greater changes in "surface and margin staining" and "fracture and retention." There was no significant difference in the amount of progressive wear among the restorations, antagonist teeth, and opposite side teeth until 18 months; however, the wear on the restorations increased gradually up to 36 months. Higher scores and identical satisfaction results were obtained for the chewing efficiency and cleanability during the observation period. The most common complications were restoration dislodgement (5.2%) and partial marginal chipping (4.3%), which were clinically repaired. Four restorations (3.4%) were extensively fractured and replaced. The estimated survival rate of the hybrid ceramic restorations at 3 years was 88.0%. CONCLUSIONS: The clinical use of hybrid ceramic restorations in patients with severe tooth wear improved chewing efficiency and esthetics, provided adequate resistance for load-bearing, and delivered a stable treatment outcome after functional use.
13 Apr 2026
Read appraisal →Dental clinics of North America
Artificial Intelligence in Diagnostic Oral and Maxillofacial Imaging, Surgical Applications, and Teledentistry
The article deals with the utilization of artificial intelligence (AI) and related technologies for the diagnosis of oral and maxillofacial anatomic variants and pathologic entities. The diagnosis of dental caries and periodontal disease is the most common aspect of dental diagnosis in a general dental setting and so, there was a subsection for this topic. There are dedicated portions within this article on applications of AI for oral and maxillofacial surgery.
4 Apr 2026
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