Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 7 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 →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 →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 →Scientific reports
3D‑printed training model for periodontal splinting: a randomized preclinical study
Preclinical simulation is essential for students to practice before clinical treatment. A low-cost 3D-printed model was developed with repositionable mobile maxillary anterior teeth to train periodontal splinting and evaluate learner perception and repositioning accuracy. In a two-period randomized cross-over course 43 students in their fourth year completed two palatal slot splints from the right to the left maxillary canine (FDI 13-23) in randomized material order. They used a polyethylene ribbon and a pre-impregnated unidirectional glass-fibre bundle. Outcomes included an electronic questionnaire using an anchored 6‑point ordinal scale from 1 for "Excellent" to 6 for "Unsatisfactory" following the German school grading system and geometric deviation of each tooth to an ideal position. The course was rated overall as good (Md 2.0, IQR 1.0) and highly clinically relevant (Md 1.0, IQR 1.0). Self-reported preparedness for splinting improved from poor (Md 5.0, IQR 1.0) before the course to good (Md 2.0, IQR 1.0; p < 0.001) after the course. In this pilot study, no statistically detectable positional deviations between materials or between the first and second session in this cohort were observed. This inexpensive, reproducible 3D-printed model enables realistic periodontal splinting training under simulated mobility conditions and may support structured preparation for clinical teaching.
25 May 2026
Read appraisal →Clinical oral investigations
Classification of occlusal caries severity using spectrophotometric CIELAB measurements and machine learning algorithms
OBJECTIVES: This study evaluated whether spectrophotometric CIELAB color measurements can support machine-learning (ML) classification of occlusal caries severity based on ICDAS categories. METHODS: Three hundred extracted human teeth were visually classified using ICDAS (0-4) and served as labels for supervised learning. Five occlusal sites per tooth were measured using a spectrophotometer under standardized conditions. Site-level CIELAB values were transformed into engineered color features and aggregated at the tooth level. Teeth were categorised as sound (ICDAS 0), initial carious lesions (1-2), and moderate carious lesions (3-4). Five ML models: Random Forest (RF), XGBoost, CatBoost, Multilayer Perceptron (MLP), and a Deep Sets architecture were trained. Performance was evaluated using accuracy, balanced accuracy (BA), and macro-F1, with additional binary analyses for early (ICDAS 1-4 vs. 0) and operative lesion detection (ICDAS 3-4 vs. 0-2), based on the included ICDAS range. Learning curve analysis was performed to evaluate the effect of training data size on model performance. RESULTS: Deep Sets achieved the highest multiclass performance (BA = 0.89) followed by the MLP (BA = 0.72). Tree-based models demonstrated lower performance overall. For early lesion detection, all models showed high sensitivity (SE) but reduced specificity (SP). At the operative level, the MLP achieved 100% SE and moderate SP. Learning-curve analysis showed that neural models benefited most from increased training data, whereas tree-based models showed limited improvement. CONCLUSION: Under controlled in-vitro conditions, spectrophotometric CIELAB measurements enabled machine-learning classification of occlusal caries severity. Deep sets achieved the highest overall performance, supporting the potential of color-based approaches as adjunctive tools for caries assessment. CLINICAL RELEVANCE: Spectrophotometric tooth color measurements may support machine learning classification of occlusal caries severity under controlled conditions, using an image-independent approach. Such color-based methods may complement visual assessment by providing a more standardized and reproducible evaluation of enamel changes following further validation.
16 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 →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
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