Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
Journal of dental research
AI in Oral Health Surveillance: Critical Review
Artificial intelligence (AI) holds transformative potential for advancing oral health surveillance by streamlining data collection, integration, and dissemination. This review critically synthesizes AI applications in oral health surveillance, highlighting its roles in 1) mapping population-level trends and oral health inequities using machine learning on epidemiological data; 2) enabling remote screening of oral diseases/conditions, including caries, oral hygiene, gingivitis, oral cancer, and malocclusion from intraoral images via computer vision models; and 3) integrating multimodal data through emerging large language models (LLMs) to enhance precision public health. We clarify the comparative strengths of distinct AI modeling for processing the primary data types in surveillance: structured clinical records, unstructured images, and integrated multimodal data. Traditional machine learning methods have been effectively applied to map population-level oral health disparities and identify risk factors but are constrained to structured data. Computer vision methods excel in individual-level diagnostics using intraoral photographs. To translate such capability into scalable surveillance, it is recommended to establish standardized imaging protocols for nonclinical settings, develop scalable models for fine-grained feature extraction, and implement reliable evaluation. These steps are essential to address pervasive challenges, including inconsistent image quality, domain shift, prevalence imbalance, and cost-effectiveness constraints. The future of AI-driven oral health surveillance lies in developing dental-adapted multimodal LLMs (MLLMs). Such MLLMs are uniquely capable of synthesizing disparate data streams, from structured clinical data to heterogeneous imaging modalities (e.g., intraoral photographs and radiographs), or even biomolecular data. This integration capacity facilitates a paradigm shift, moving current applications in dental consultation and clinical decision support toward a novel, tiered system for population-level monitoring. Such a system would provide actionable insights for public health policymaking via spatiotemporal analysis and causal inference. Next-generation AI-driven oral health surveillance systems can only succeed when built on a strong foundation of rigorous ethical principles and safeguards.
2 July 2026
Read appraisal →Quintessence international (Berlin, Germany : 1985)
Mapping sequential patterns of periodontal breakdown using longitudinal electronic health records data
OBJECTIVE: To identify probabilistic, sequential patterns of periodontal breakdown using sequential pattern mining (SPM) on a large longitudinal cohort. METHOD AND MATERIALS: Electronic health records from 19,428 patients with periodontitis (2011-2022) were analyzed. A custom SPM algorithm was developed to identify frequent temporal subsequences of tooth mobility and furcation involvement, enforcing strict temporal precedence. 'High-risk' states (eg, generalized clinical attachment loss [CAL] > 3.0 mm) were evaluated as precursors to localized failure. Association rules were ranked by confidence (conditional probability). RESULTS: The analysis revealed distinct biomechanical cascading patterns. In the mandibular anterior sextant, mobility in the central incisors was a strong probabilistic precursor to subsequent failure in the lateral incisors and canines (confidence 7.3%). A significant 'antagonistic cascade' was identified, where mandibular incisor mobility preceded maxillary incisor instability (confidence 8.1%). For molars, furcation involvement acted as a specific temporal sentinel for future mobility (confidence ~5% to 6%). Generalized high CAL was the strongest predictor of localized failure (confidence 18.9%), significantly outperforming active inflammation (bleeding on probing) as a driver of mechanical instability. CONCLUSION: Progressive biomechanical compromise (such as tooth mobility and advanced furcation involvement) follows predictable, probabilistic sequential patterns. The identification of specific 'sentinel events,' such as mandibular incisor mobility and molar furcation involvement, allows for a shift from reactive site-by-site management to predictive intervention. CLINICAL RELEVANCE: These findings underscore the critical role of generalized attachment loss in driving localized mechanical failure cascades.
22 May 2026
Read appraisal →Clinical oral investigations
Evaluation of a novel dual-headed toothbrush design for periodontal health: split-mouth randomized blind clinical trial
BACKGROUND: Optimal oral health largely depends on the successful removal of plaque through effective brushing, but success typically relies on user technique, time, and adherence. The aim was to compare the effectiveness of a newly introduced dual-headed toothbrush with a conventional single-headed toothbrush in control of plaque and gingival health using a split-mouth design. METHODS: In the study, 37 dental professionals used two types of toothbrushes: a dual-headed toothbrush on one side of the mouth (Test side) and a conventional single-headed toothbrush on the other side (Control side). The study compared plaque removal, gingival status, and brushing time, assessing clinical outcomes using plaque index (PI), gingival index (GI), and modified sulcus bleeding index (mSBI) at baseline, 2 weeks, and 4 weeks. At the end, participants completed a Likert scale questionnaire. RESULTS: Both types of toothbrushes were effective in enhancing oral hygiene over time; however, dual-headed toothbrush group showed significant short-term reductions in plaque accumulation, gingival inflammation, and bleeding, particularly at the 2-week interval. A significant majority of participants (89.2%) rated dual-headed brush as "very easy" to use. Using a dual-headed toothbrush significantly reduces the brushing time (0.54 ± 0.17 vs. 0.99 ± 0.05 min for a conventional single-headed toothbrush), suggesting enhanced time efficiency. CONCLUSION: A dual-headed toothbrush would be more effective, faster, and easier to use than a single-headed brush. It would be a convenient alternative, especially for individuals with time constraints.
21 May 2026
Read appraisal →Brazilian oral research
Effect of strict oral hygiene on self-rated halitosis and quality of life in patients with periodontitis: a secondary randomized controlled trial analysis
This single-blind, randomized clinical trial evaluated the impact of a strict oral hygiene (OH) phase on self-reported halitosis and quality of life (QoL) in patients with periodontitis. This study reports a secondary analysis of a single-blind randomized controlled trial (parallel, 1:1), registered at RBR-5jmx32v. The study included 24 individuals diagnosed with periodontitis who were randomly assigned to the intervention group (IG; n = 12) and the no-intervention group (NIG; n = 12). After receiving personalized OH instructions, the IG participants followed a rigorous OH phase for 45 days, whereas the NIG participants received no guidance during this period. The halitosis-associated QoL test (HALT) was administered at baseline and after 45 days. Data were analyzed using t-tests, chi-square tests, and multilevel linear regression, with significance set at p < 0.05. The average age of the study participants was 47.2 years, with a balanced sex distribution and no differences in income or education. Self-reported halitosis decreased from 100% to 50% in the IG after 45 days, whereas it remained at 91.6% in the NIG (p = 0.034). The IG also showed a mean HALT score reduction of 11.9 points (-11.88; 95% CI: -19.40 to -4.36), with significant improvements in social impact and self-esteem (p < 0.05). Clinically, BOP (p < 0.001) and VPI (p = 0.03) improved significantly in the IG, with no significant changes in NIG. This exploratory trial demonstrates that implementing a strict OH phase in patients with periodontitis may help reduce self-reported halitosis, improve HALT scores, and improve clinical outcomes, supporting its role as an adjunctive strategy in periodontal care.
21 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
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