Diagnostics

Research

19 pages, 1247 KB

Open AccessArticle

An Exploratory Assessment of Early Feeding Modality and Associated Stomatognathic, Occlusal, and Language Outcomes in Preterm Children: An Observational Cross-Sectional Study

by Ștefan Lucian Burlea, Laura Elisabeta Checheriță, Ovidiu Stamatin, Loredana Golovcenco, Vlad Ștefan Proca, Maria Antonela Beldiman, Gabriel Goian, Tudor Hamburda, Violina Budu, Bogdan Petru Bulancea, Anamaria Ciubară, Crînguța Mariana Paraschiv, Liana Aminov, Oana-Irina Gavril and Ana Elena Sîrghe

Diagnostics 2026, 16(17), 2727; https://doi.org/10.3390/diagnostics16172727 - 26 Aug 2026

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Abstract

Background/Objectives: Malocclusion and delayed expressive language are generally managed as separate clinical problems, although both may be influenced by early feeding experiences and stomatognathic development. This study investigated whether neonatal feeding modality was associated with occlusal morphology and expressive language development in preterm [...] Read more.

Background/Objectives: Malocclusion and delayed expressive language are generally managed as separate clinical problems, although both may be influenced by early feeding experiences and stomatognathic development. This study investigated whether neonatal feeding modality was associated with occlusal morphology and expressive language development in preterm children, and explored the relationship between these outcomes. Methods: Forty preterm children (gestational age < 37 weeks) stratified: breastfeeding (Group A, n = 14), tube feeding with structured non-nutritive sucking (NNS) (Group B, n = 13), and total parenteral nutrition without oral stimulation (Group C, n = 13). Seven of 47 children assessed were excluded according to predefined eligibility criteria. Occlusal morphology was independently evaluated by two blinded paediatric dentists (Cohen’s κ = 0.91). Expressive language was evaluated using the MacArthur–Bates Communicative Development Inventories and a structured phonological checklist. Group differences were analysed using Kruskal–Wallis and Dunn–Bonferroni tests; associations were examined using Spearman ‘s rank correlation and multivariable ordinary least squares regression adjusted for sex, area of residence, and socioeconomic status. Results: Significant differences were observed among the three feeding groups in occlusion score, language score, and age at first words (all p < 0.001; ε² = 0.81–0.90). Occlusion and language scores were strongly associated (Spearman’s ρ = 0.986, p < 0.001), and this association remained essentially unchanged after adjustment for demographic covariates (β = 2.10, 95% CI 2.01–2.19, p < 0.001). Occlusal morphology subtypes also differed significantly across groups (χ²(6) = 19.1, p = 0.004). Conclusions: Neonatal feeding modality was associated with both occlusal morphology and expressive language outcomes in this cohort of preterm children. Breastfeeding was associated with the most favorable outcomes, structured NNS with intermediate outcomes, and TPN with the least favorable outcomes. Given the observational design, modest sample size, and potential for residual confounding, findings should be considered exploratory and require confirmation in larger prospective multicenter studies. Full article

(This article belongs to the Special Issue Advances in Dental Diagnostics)

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14 pages, 3678 KB

Open AccessArticle

Algorithmovigilance in AI-Based Oral-Health Surveillance: Temporal Drift, Cross-Survey Differences, and Predictor-Level Structural Stability in Population-Level Severe Tooth Loss Prediction

by Quang Tuan Lam, Fang-Yu Fan, Yung-Li Wang, Sheng-Wei Feng, Thi Thuy Tien Vo, Minh Huu Nhat Le, Giang Vu, Nguyen Quoc Khanh Le and I-Ta Lee

Diagnostics 2026, 16(16), 2569; https://doi.org/10.3390/diagnostics16162569 - 14 Aug 2026

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Abstract

Background/Objectives: Artificial intelligence models may retain acceptable discrimination while calibration and predictor–risk relationships change across populations and data-collection systems. We evaluated a structured algorithmovigilance framework for detecting temporal drift, cross-survey differences, and predictor-level structural instability in severe tooth loss prediction. Methods: [...] Read more.

Background/Objectives: Artificial intelligence models may retain acceptable discrimination while calibration and predictor–risk relationships change across populations and data-collection systems. We evaluated a structured algorithmovigilance framework for detecting temporal drift, cross-survey differences, and predictor-level structural instability in severe tooth loss prediction. Methods: Across five BRFSS cycles from 2016 to 2024 (total N = 2,176,039), a survey-weighted main-effects Explainable Boosting Machine trained in 2016 was evaluated chronologically through 2024. NHANES 2015–2018 served as an external reference for an exploratory cross-survey temporal comparison. Results: AUC declined modestly from 0.8638 in 2016 to 0.8495 in 2024. The frozen unrecalibrated model yielded AUC = 0.8681 and Brier Score = 0.1131 in pooled NHANES. The survey-system-by-period interaction was positive (beta = 0.0412; HC1 p = 0.014), although a stratified survey-weighted bootstrap sensitivity produced a wider interval including zero (95% CI, −0.0085 to 0.0895; p = 0.093). Smoking (p < 0.001) and income (p = 0.030) showed nominal predictor-level structural shifts, whereas BMI did not. An interaction-enabled EBM improved AUC by 0.0010 in 2016 and 0.0016 in 2024. Conclusions: Aggregate discrimination alone did not capture calibration and predictor-level changes. The framework provides retrospective monitoring signals for governed review, not evidence of causal survey-mode effects, formal algorithmic fairness, or clinical deployment readiness. Full article

(This article belongs to the Special Issue Advances in Dental Diagnostics)

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16 pages, 7295 KB

Open AccessArticle

Diagnostic Performance of Vertical and Sagittal Cephalometric Parameters in Differentiating Skeletal Malocclusion in Saudi Adults: A Cephalometric Study

by Mohammad A. Hamidaddin, Guna Shekhar Madiraju, Faris Yahya I. Asiri, Salem Abdulrahman Albalawi, Abdulelah Abdulrahman Alfalah and Hatim D. Alqurashi

Diagnostics 2026, 16(13), 1977; https://doi.org/10.3390/diagnostics16131977 - 25 Jun 2026

Viewed by 471

Abstract

Background/Objective: This study evaluated the diagnostic performance of vertical growth patterns and mandibular morphology, alongside the anteroposterior dysplasia indicator (APDI), for classifying skeletal malocclusions in a Saudi adult population using cephalometric analysis. Materials and Methods: This retrospective cross-sectional discriminatory performance study [...] Read more.

Background/Objective: This study evaluated the diagnostic performance of vertical growth patterns and mandibular morphology, alongside the anteroposterior dysplasia indicator (APDI), for classifying skeletal malocclusions in a Saudi adult population using cephalometric analysis. Materials and Methods: This retrospective cross-sectional discriminatory performance study analyzed 162 archived lateral cephalometric radiographs of Saudi adults aged 18–44 years. The assessed variables included Frankfort-mandibular plane angle (FMA), gonial angle, ANB angle, and APDI. Statistical analysis involved descriptive statistics, ANOVA with post hoc testing, Pearson correlation, logistic regression, and receiver operating characteristic (ROC) curve analysis. Results: Significant differences among skeletal classes were observed for all evaluated variables (p < 0.05). APDI showed the largest effect size and the highest diagnostic performance, particularly for Class III malocclusion, with excellent discriminatory ability reflected by area under the curve (AUC) values, high sensitivity, and acceptable specificity at optimal cutoff points. FMA showed moderate discriminatory performance, with higher specificity but limited sensitivity, while the gonial angle exhibited comparatively weaker diagnostic performance. In logistic regression analysis, APDI was the only significant independent associated variable of Class II malocclusion. Conclusions: Within the ANB-based classification framework used in this study, APDI showed the highest discriminatory performance for skeletal malocclusion classification, supporting its role as a primary sagittal indicator. FMA contributed adjunctive information on vertical skeletal pattern, while the gonial angle showed limited diagnostic value. Combined assessment of sagittal and vertical parameters may improve cephalometric diagnosis. Full article

(This article belongs to the Special Issue Advances in Dental Diagnostics)

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12 pages, 1535 KB

Open AccessArticle

An Attention-Enhanced RegNetY Framework for Detection and Classification of Vertical Misfit in Implant-Supported Restorations: A Retrospective Study

by Tuba Talo Yildirim, Aybike Cengiz Dagtekin, Nurullah Düger, Ayşe Rençber Kizilkaya, Furkan Talo, Emre Arslan, Mucahit Karaduman and Muhammed Yildirim

Diagnostics 2026, 16(11), 1613; https://doi.org/10.3390/diagnostics16111613 - 25 May 2026

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Abstract

Background/Objectives: The aim of this study is to test different convolutional neural network (CNN) and Transformer-based models to detect and classify vertical misfit at the abutment-prosthesis interface on panoramic radiographs, and to develop a hybrid deep learning model enhanced with attention mechanisms. [...] Read more.

Background/Objectives: The aim of this study is to test different convolutional neural network (CNN) and Transformer-based models to detect and classify vertical misfit at the abutment-prosthesis interface on panoramic radiographs, and to develop a hybrid deep learning model enhanced with attention mechanisms. Methods: A dataset consisting of a total of 566 images, manually classified as 249 ‘fit’ and 317 ‘misfit’ cases by two experts, was created. Images were resized to 224 × 224 and divided into training, validation, and test groups. The deep learning model yielding the most successful results was determined as the backbone; a hybrid model was developed by integrating three different attention modules (SE, CBAM, and ECA) into this structure. Model performance was evaluated using accuracy, precision, sensitivity, and F1 score metrics. Results: CNN-based models (RegNetY-800MF, ConvNeXt-Tiny, EfficientNetV2-S, ResNet50) performed better than Transformer-based models (DeiT, Swin-Tiny) in all metrics. The proposed hybrid model exhibited the highest success among all tested models with a 99.12% accuracy rate. This model reached a 100% precision value in the misfit group and yielded no false positive results. The F1 scores of the hybrid model were recorded as 99.01% for the fit group and 99.21% for the misfit group. Conclusions: The findings of this study demonstrate that attention-enhancing deep learning frameworks have the potential to significantly improve the diagnostic utility of routine panoramic radiographs. It shows that panoramic imaging, when supported by advanced artificial intelligence, can provide valuable diagnostic support in detecting vertical misfit. The developed model has the potential to become a reliable clinical decision support system. Full article

(This article belongs to the Special Issue Advances in Dental Diagnostics)

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17 pages, 5672 KB

Open AccessArticle

Prevalence of Unfilled MB2 Canals and Their Association with Apical Periodontitis: A CBCT-Based Cross-Sectional Study in a German Population

by Maythem Al Fartousi and Christian Ralf Gernhardt

Diagnostics 2026, 16(5), 796; https://doi.org/10.3390/diagnostics16050796 - 7 Mar 2026

Cited by 3 | Viewed by 1584

Abstract

Background/Objectives: The presence of untreated second mesio-buccal canals (MB2) in maxillary first molars is usually associated with endodontic treatment failure. Previous CBCT-based investigations have evaluated the quality of root canal fillings and the prevalence of apical lesions in endodontically treated teeth. However, [...] Read more.

Background/Objectives: The presence of untreated second mesio-buccal canals (MB2) in maxillary first molars is usually associated with endodontic treatment failure. Previous CBCT-based investigations have evaluated the quality of root canal fillings and the prevalence of apical lesions in endodontically treated teeth. However, evidence specifically addressing untreated MB2 canals and their association with apical periodontitis remains limited. Therefore, the aim of this cross-sectional study was to evaluate the prevalence of unfilled MB2 canals in endodontically treated maxillary first molars and their association with apical periodontitis. Methods: CBCT scans of 75 patients from an endodontic practice were retrospectively analyzed. Maxillary first molars (teeth 16 and 26) were evaluated for the presence and filling status of root canals (MB1, MB2, palatal, distal) and the presence of periapical radiolucency using the CBCT periapical index. Two calibrated examiners independently assessed all images. The association between unfilled MB2 canals and apical periodontitis was analyzed using chi-square tests, and odds ratios with 95% confidence intervals were calculated. Results: The mean patient age was 53.4 ± 15.5 years (range: 14–80). An MB2 canal was present in 84% (63/75) of eligible teeth. Among teeth with an MB2 canal, only 20.6% (13/63) were endodontically filled, while 79.4% remained untreated. Apical periodontitis was observed in 65.3% (49/75) of all teeth. A significant association was found between unfilled MB2 canals and apical periodontitis (p < 0.001), with an odds ratio of 0.095 (95% CI: 0.022–0.402), indicating that filled MB2 canals significantly reduced the possible risk of periapical pathology. Conclusions: A high prevalence of unfilled MB2 canals was observed in this German population (79.4%). Furthermore, unfilled MB2 canals were strongly associated with apical periodontitis. Therefore, clinicians should utilize all available diagnostic tools, including CBCT and dental microscopes, to maximize MB2 canal identification and improve endodontic treatment outcomes. Full article

(This article belongs to the Special Issue Advances in Dental Diagnostics)

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16 pages, 600 KB

Open AccessArticle

Prevalence and Distribution of Apical Periodontitis in Root Canal-Treated Teeth: A Cone-Beam Computed Tomography Study in a Saudi Subpopulation

by Obadah Austah, Lama Alghamdi, Amjad Alshamrani, Taggreed Wazzan, Mohammed Barayan, Mohammed A. Alharbi, Abdullah Bokhary and Loai Alsofi

Diagnostics 2026, 16(4), 618; https://doi.org/10.3390/diagnostics16040618 - 20 Feb 2026

Cited by 1 | Viewed by 1417

Abstract

Background: Apical periodontitis (AP) is a common inflammatory condition of the periapical tissues, most often associated with persistent endodontic infection. Conventional two-dimensional radiography may underestimate AP because of anatomical superimposition and limited sensitivity. Cone-beam computed tomography (CBCT) allows three-dimensional visualization of periapical structures [...] Read more.

Background: Apical periodontitis (AP) is a common inflammatory condition of the periapical tissues, most often associated with persistent endodontic infection. Conventional two-dimensional radiography may underestimate AP because of anatomical superimposition and limited sensitivity. Cone-beam computed tomography (CBCT) allows three-dimensional visualization of periapical structures and has been increasingly used in epidemiological research. Objective: This study aimed to evaluate the prevalence and distribution of apical periodontitis, with particular emphasis on apical periodontitis associated with root canal-treated teeth (AP-RCT), in a Saudi subpopulation using CBCT imaging. Methods: This retrospective cross-sectional study analyzed CBCT scans of Saudi patients obtained for routine diagnostic purposes between 2017 and 2021. Apical periodontitis was identified using standardized radiographic criteria requiring the presence of periapical radiolucency in more than one imaging plane. Demographic and clinical variables were recorded. Descriptive statistics were used to estimate prevalence. Associations between demographic factors and AP-RCT counts were evaluated using multivariable negative binomial regression. Regional tooth distribution was analyzed using generalized estimating equation models accounting for within-participant clustering. Results: A total of 320 CBCT scans were analyzed. Apical periodontitis was detected in 231 participants (72.2%) and in 667 teeth (8.3% of examined teeth). Of the affected teeth, 457 (68.5%) were associated with root canal treatment. The mean number of AP-RCT per participant was 1.36 ± 1.81 (median: 1; IQR: 0–2). Multivariable analysis identified age as the only significant predictor of AP-RCT. Compared with individuals aged 21–30 years, higher AP-RCT rates were observed in the 31–40-year and 41–50-year age groups, while participants ≤20 years showed lower rates. Tooth-level analysis demonstrated higher AP-RCT prevalence in maxillary premolars, maxillary molars, and mandibular molars, whereas mandibular anterior teeth showed the lowest prevalence. Conclusions: Apical periodontitis, particularly AP-RCT, was frequently observed in this Saudi subpopulation when assessed using CBCT. Age and tooth location were the primary determinants of disease distribution. These findings provide population-level epidemiological data on the prevalence and anatomical distribution of apical periodontitis in root canal-treated teeth. Clinical Significance: CBCT-based epidemiological assessment enables detailed evaluation of the distribution of apical periodontitis in dentate populations and may assist in characterizing disease patterns in anatomically complex regions, without implying comparative diagnostic accuracy or treatment outcome assessment. Full article

(This article belongs to the Special Issue Advances in Dental Diagnostics)

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26 pages, 44941 KB

Open AccessArticle

Advanced Deep Learning Models for Classifying Dental Diseases from Panoramic Radiographs

by Deema M. Alnasser, Reema M. Alnasser, Wareef M. Alolayan, Shihanah S. Albadi, Haifa F. Alhasson, Amani A. Alkhamees and Shuaa S. Alharbi

Diagnostics 2026, 16(3), 503; https://doi.org/10.3390/diagnostics16030503 - 6 Feb 2026

Cited by 1 | Viewed by 2378

Abstract

Background/Objectives: Dental diseases represent a great problem for oral health care, and early diagnosis is essential to reduce the risk of complications. Panoramic radiographs provide a detailed perspective of dental structures that is suitable for automated diagnostic methods. This paper aims to investigate [...] Read more.

Background/Objectives: Dental diseases represent a great problem for oral health care, and early diagnosis is essential to reduce the risk of complications. Panoramic radiographs provide a detailed perspective of dental structures that is suitable for automated diagnostic methods. This paper aims to investigate the use of an advanced deep learning (DL) model for the multiclass classification of diseases at the sub-diagnosis level using panoramic radiographs to resolve the inconsistencies and skewed classes in the dataset. Methods: To classify and test the models, rich data of 10,580 high-quality panoramic radiographs, initially annotated in 93 classes and subsequently improved to 35 consolidated classes, was used. We applied extensive preprocessing techniques like class consolidation, mislabeled entry correction, redundancy removal and augmentation to reduce the ratio of class imbalance from 2560:1 to 61:1. Five modern convolutional neural network (CNN) architectures—InceptionV3, EfficientNetV2, DenseNet121, ResNet50, and VGG16—were assessed with respect to five metrics: accuracy, mean average precision (mAP), precision, recall, and F1-score. Results: InceptionV3 achieved the best performance with a 97.51% accuracy rate and a mAP of 96.61%, thus confirming its superior ability for diagnosing a wide range of dental conditions. The EfficientNetV2 and DenseNet121 models achieved accuracies of 97.04% and 96.70%, respectively, indicating strong classification performance. ResNet50 and VGG16 also yielded competitive accuracy values comparable to these models. Conclusions: Overall, the results show that deep learning models are successful in dental disease classification, especially the model with the highest accuracy, InceptionV3. New insights and clinical applications will be realized from a further study into dataset expansion, ensemble learning strategies, and the application of explainable artificial intelligence techniques. The findings provide a starting point for implementing automated diagnostic systems for dental diagnosis with greater efficiency, accuracy, and clinical utility in the deployment of oral healthcare. Full article

(This article belongs to the Special Issue Advances in Dental Diagnostics)

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15 pages, 517 KB

Open AccessArticle

Qualitative Alterations of Mandibular Kinematics in Patients with Myogenous Temporomandibular Disorders: An Axiographic Study Using the Cadiax Diagnostic System

by Daniel Surowiecki, Malgorzata Tomasik and Jolanta Kostrzewa-Janicka

Diagnostics 2025, 15(23), 3044; https://doi.org/10.3390/diagnostics15233044 - 28 Nov 2025

Cited by 1 | Viewed by 912

Abstract

Background: Myogenous temporomandibular disorders (TMDs) typically present with pain but without obvious restriction of mandibular motion, making subtle dysfunctions difficult to detect clinically. In this study, we evaluated mandibular kinematics in myogenous TMDs using an electronic axiography system (Cadiax Diagnostic). The specific [...] Read more.

Background: Myogenous temporomandibular disorders (TMDs) typically present with pain but without obvious restriction of mandibular motion, making subtle dysfunctions difficult to detect clinically. In this study, we evaluated mandibular kinematics in myogenous TMDs using an electronic axiography system (Cadiax Diagnostic). The specific objective of this study was to evaluate whether patients with myogenous temporomandibular disorders exhibit qualitative abnormalities in mandibular movements that are not detectable using conventional clinical examination. Methods: Twenty-six patients with myogenous TMD (muscle pain without intra-articular disorders, diagnosed per DC/TMD) and 26 matched controls were examined. Clinical assessment (DC/TMD Axis I) measured mandibular range of motion and deviations. Instrumental recordings of maximal opening, protrusion, and laterotrusion were obtained with Cadiax 4. Quantitative (excursion ranges) and qualitative (movement symmetry and sagittal deviations) parameters were analyzed. Condylar position changes between the reference position and maximum intercuspation were evaluated (Condyle Position Measurement, CPM). Exact χ² or Fisher tests were applied with effect sizes (φ) and 95% confidence intervals (CI). Results: Maximal opening, lateral excursions, and protrusion ranges were statistically similar between groups (mean opening: 47.96 ± 6.5 mm in TMDs vs. 49.46 ± 5.4 mm in controls, p = 0.40; 95% CI of difference −1.8 to 4.8 mm). However, qualitative deviations were more frequent in TMD. Of note, 12/26 (46.2%) patients vs. 6/26 (23.1%) controls showed a ΔY deflection during protrusion (χ² = 3.06, p = 0.08; φ ≈ 0.24; difference = 23.1%, 95% CI −2.0–48.2%). Identical proportions (46.2% vs. 23.1%) showed a ΔY deflection upon opening (χ² = 3.06, p = 0.08). Inferior condylar shifts (distractions) on closing into intercuspation occurred only in the mTMD group: 5/26 (19.2%) left condyles vs. 0% (p ≈ 0.05; 95% CI diff 4.1–34.4%) and 2/26 (7.7%) right vs. 0% (p ≈ 0.49; 95% CI −2.5–17.9%). Condylar compressions (superior shifts) were similar between groups. In summary, roughly half of TMD patients exhibited lateral jaw deflections (ΔY) and exclusive condylar “distraction” on closure; upon comparison, these conditions were rare in controls. Conclusions: Despite normal mandibular range of motion, patients with myogenous TMDs exhibited qualitative abnormalities in jaw kinematics, including movement deflections, condylar asymmetries, and centric–intercuspal discrepancies. Axiographic analysis with Cadiax enabled detection of subtle functional changes not identifiable in routine examinations, underscoring its diagnostic value in early dysfunction and potential therapeutic planning. The detection of kinematic abnormalities could influence early diagnosis or treatment planning for myogenous TMDs. Full article

(This article belongs to the Special Issue Advances in Dental Diagnostics)

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21 pages, 1769 KB

Open AccessArticle

Evaluation of the Proximity of the Maxillary Teeth Root Apices to the Maxillary Sinus Floor in Romanian Subjects: A Cone-Beam Computed Tomography Study

by Vlad Ionuţ Iliescu, Vanda Roxana Nimigean, Cristina Teodora Preoteasa, Lavinia Georgescu and Victor Nimigean

Diagnostics 2025, 15(14), 1741; https://doi.org/10.3390/diagnostics15141741 - 9 Jul 2025

Cited by 5 | Viewed by 4891

Abstract

Background/Objectives: Among the paranasal sinuses, the maxillary antrum holds unique clinical relevance due to its proximity to the alveolar process of the maxilla, which houses the teeth. This study aimed to evaluate the position of the root apices of the maxillary canines [...] Read more.

Background/Objectives: Among the paranasal sinuses, the maxillary antrum holds unique clinical relevance due to its proximity to the alveolar process of the maxilla, which houses the teeth. This study aimed to evaluate the position of the root apices of the maxillary canines and posterior teeth relative to the maxillary sinus floor in Romanian subjects. Methods: Data for the study were retrospectively obtained from cone-beam computed tomography (CBCT) scans. The evaluation considered the pattern of proximity to the sinus floor for each tooth type, comparisons of the sinus relationships of teeth within the same dental hemiarch, as well as those of homologous teeth, and variation in root-to-sinus distance in relation to sex and age. Nonparametric tests were used for statistical analysis, and multiple comparisons were performed using Bonferroni post hoc correction. Results: The study included 70 individuals aged 20 to 60 years. The distance to the sinus floor decreased progressively from the first premolar to the second molar, with median values of 3.68 mm (first premolar), 1.45 mm (second premolar), 0.50 mm (first molar), and 0.34 mm (second molar) (p < 0.01). Stronger correlations were observed between adjacent teeth than between non-adjacent ones. The distances to the sinus floor were greater on the right side compared to the left; however, these differences were not statistically significant (p > 0.05 for all teeth). Concordance between left and right dental hemiarches regarding the closest tooth to the sinus floor was found in 70% of cases (n = 49), most frequently involving the second molars (n = 38; 54.3%). On average, the distance from the sinus floor was smaller in males compared to females, with statistically significant differences observed only for the second molar. Increased age was associated with a greater distance to the sinus floor. Conclusions: Of all the teeth investigated, the second molar showed the highest combined prevalence of penetrating and tangential relationships with the maxillary sinus. At the dental hemiarch level, the second molar was most frequently the closest tooth to the sinus floor, and in the majority of cases, at least one posterior tooth was located within 0.3 mm. Accurate preoperative assessment of tooth position relative to the sinus floor is essential when performing non-surgical or surgical root canal therapy and extractions of maxillary molars and premolars. CBCT provides essential three-dimensional imaging that improves diagnostic precision and supports safer treatment planning for procedures involving the posterior maxilla. Full article

(This article belongs to the Special Issue Advances in Dental Diagnostics)

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Review

Jump to: Research, Other

15 pages, 664 KB

Open AccessReview

Clinical Utility of Small Extracellular Vesicles as Liquid Biopsy for Oral Mucosal Disease Diagnostics: Emerging Perspectives

by Olawande Funmilola Adebayo, Dada Oluwaseyi Temilola, Foluso John Owotade and Manogari Chetty

Diagnostics 2026, 16(7), 1044; https://doi.org/10.3390/diagnostics16071044 - 30 Mar 2026

Cited by 1 | Viewed by 941

Abstract

Some diseases affecting the oral mucosa can be life-threatening and/or associated with life-threatening complications. Conventional diagnostic methods for most oral mucosal diseases are usually employed at a fully established disease state. All these peculiarities usually result in late diagnosis, poor prognosis, poor treatment [...] Read more.

Some diseases affecting the oral mucosa can be life-threatening and/or associated with life-threatening complications. Conventional diagnostic methods for most oral mucosal diseases are usually employed at a fully established disease state. All these peculiarities usually result in late diagnosis, poor prognosis, poor treatment outcomes, and reduced overall survival rates, hence the need for novel methods for the early detection of these disease conditions. Small extracellular vesicle (sEV)-based diagnosis carries great potential for early diagnosis of oral mucosal diseases, as sEVs reflect the physiological status of their parent cells. sEVs are also widely distributed in body fluids, which helps overcome the problem of inaccessibility in sample or specimen collection in some cases. Furthermore, the composition of sEVs can be used as diagnostic biomarkers for several disease conditions, including oral mucosal diseases. This review critically examines the emerging role of sEVs-derived biomarkers from saliva and blood in the diagnosis of some oral mucosal diseases, such as hand, foot, and mouth disease (HFMD), oral lichen planus (OLP), oral leukoplakia (OL), and oral squamous cell carcinoma (OSCC). It also discusses the need for the validation and standardization of the potential sEV-derived diagnostic biomarkers of these oral mucosal diseases for clinical application. Full article

(This article belongs to the Special Issue Advances in Dental Diagnostics)

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Other

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16 pages, 5618 KB

Open AccessSystematic Review

Artificial Intelligence for Diagnosis of Temporomandibular and Cranio-Cervico-Mandibular Musculoskeletal Disorders: A Systematic Review and Exploratory Diagnostic Test Accuracy Meta-Analysis

by Arturo Arbeláez Ramírez and Daniel Botero Rosas

Diagnostics 2026, 16(15), 2468; https://doi.org/10.3390/diagnostics16152468 - 5 Aug 2026

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Abstract

Objectives: To systematically evaluate the diagnostic accuracy, clinical applicability, and methodological maturity of artificial intelligence (AI)-based methods for temporomandibular disorders (TMD), temporomandibular joint (TMJ) abnormalities, and related cranio-cervico-mandibular (CCM) musculoskeletal conditions compared with conventional diagnostic methods and accepted reference standards. Materials and [...] Read more.

Objectives: To systematically evaluate the diagnostic accuracy, clinical applicability, and methodological maturity of artificial intelligence (AI)-based methods for temporomandibular disorders (TMD), temporomandibular joint (TMJ) abnormalities, and related cranio-cervico-mandibular (CCM) musculoskeletal conditions compared with conventional diagnostic methods and accepted reference standards. Materials and Methods: This systematic review and exploratory diagnostic test accuracy meta-analysis was conducted in accordance with PRISMA 2020 and PRISMA-DTA. The protocol was retrospectively registered in PROSPERO (CRD420261428138). PubMed/MEDLINE, Embase, and Scopus were searched from database inception through February 2026. Eligibility for the primary synthesis was restricted to published studies in English or Spanish involving adults aged 18 years or older. All extracted records were re-audited article by article to align the evidence with the diagnostic question. The domain-specific quantitative synthesis was restricted to TMJ osteoarthritis studies with explicit 2 × 2 diagnostic data or a unique, verifiable reconstruction from reported class totals and sensitivity/specificity. Risk of bias was assessed with QUADAS-2. Results: From 1471 records identified, 174 entered the master extraction dataset. After reclassification, 84 records were retained for primary TMD/TMJ qualitative synthesis, 8 as secondary CCM musculoskeletal evidence, 31 as conventional or reference standard supporting evidence, 33 as methodological or contextual evidence, 4 as differential orofacial pain evidence, and 14 as excluded or minimal-background records. Twenty-one studies were assessed as potential diagnostic accuracy candidates. Three TMJ osteoarthritis studies contributed to the domain-specific exploratory meta-analysis: two with explicit 2 × 2 data and one with a reproducible reconstruction. Pooled sensitivity was 0.791 (95% CI: 0.700–0.861) and pooled specificity was 0.869 (95% CI: 0.811–0.911). Heterogeneity was substantial for sensitivity (I² = 68.2%) and moderate for specificity (I² = 57.3%). Conclusions: AI demonstrates promising performance in selected image-based TMJ osteoarthritis tasks. Nevertheless, the evidence remains exploratory because only three studies were quantitatively comparable, one table was reconstructed, and modalities and validation designs differed. AI should be interpreted as an augmentative decision support tool rather than a replacement for MRI, CBCT, or validated clinical frameworks such as DC/TMD. Clinical Relevance: AI may support image-based TMD/TMJ workflows, but present evidence does not justify autonomous diagnosis or replacement of established clinical and imaging reference standards. Full article

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30 pages, 3196 KB

Open AccessSystematic Review

Deep Learning-Based Dental Caries Diagnosis: A Modality-Stratified Systematic Review and Meta-Analysis of Faster R-CNN and Mask R-CNN

by Quang Tuan Lam, Minh Huu Nhat Le, Fang-Yu Fan, Nguyen Quoc Khanh Le and I-Ta Lee

Diagnostics 2026, 16(5), 731; https://doi.org/10.3390/diagnostics16050731 - 1 Mar 2026

Cited by 3 | Viewed by 2685

Abstract

Background: Deep convolutional neural networks (DCNNs) are increasingly used in computer-aided dental diagnostics. However, the relative diagnostic performance of commonly applied architectures, particularly Faster R-CNN and Mask R-CNN, has not been systematically synthesized across imaging modalities. This systematic review and meta-analysis compared the [...] Read more.

Background: Deep convolutional neural networks (DCNNs) are increasingly used in computer-aided dental diagnostics. However, the relative diagnostic performance of commonly applied architectures, particularly Faster R-CNN and Mask R-CNN, has not been systematically synthesized across imaging modalities. This systematic review and meta-analysis compared the diagnostic accuracy of Faster R-CNN and Mask R-CNN for dental caries detection using radiographic and photographic images. Methods: PubMed (MEDLINE), EMBASE, Web of Science, and Scopus were systematically searched for studies published up to 15 June 2025. Studies applying Faster R-CNN and/or Mask R-CNN to dental caries detection were included. Binary diagnostic data were extracted, and pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were estimated using a bivariate random-effects model. Study quality was assessed with QUADAS-AI, and radiomics-based radiographic studies were additionally evaluated using the Radiomics Quality Score (RQS). The protocol was registered in PROSPERO (CRD420251074443). Results: Seventeen studies met the inclusion criteria. Across all imaging modalities, Mask R-CNN showed significantly higher pooled sensitivity (85.6% vs. 71.7%, p = 0.0244), specificity (94.2% vs. 81.4%, p = 0.00089), and AUC (0.95 vs. 0.84, p = 0.0053) than Faster R-CNN. In radiographic images, Mask R-CNN consistently outperformed Faster R-CNN in sensitivity (86.3% vs. 67.2%, p = 0.0497), specificity (96.5% vs. 85.0%, p = 0.00105), and AUC (0.97 vs. 0.86, p = 0.0067). In photographic images, Mask R-CNN achieved a higher AUC (0.91 vs. 0.83, p = 0.048), whereas differences in pooled sensitivity (83.5% vs. 77.3%, p = 0.435) and specificity (86.0% vs. 75.1%, p = 0.156) were not statistically significant. Conclusions: Faster R-CNN and Mask R-CNN both show potential for dental caries detection, but current evidence is limited by substantial heterogeneity, predominantly retrospective designs, and variability in imaging and labeling. Across the included studies, Mask R-CNN showed higher pooled performance estimates than Faster R-CNN, with the clearest differences in radiographic applications; however, this comparison is indirect and should be considered suggestive rather than definitive given study-level heterogeneity and uncertainty in the reference standard in a sizable proportion of studies. Prospective, multi-center studies with standardized imaging protocols, rigorous annotation, and independent external validation are required to support reliable clinical implementation. Full article

(This article belongs to the Special Issue Advances in Dental Diagnostics)

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Open AccessInteresting Images

Dilated Composite Odontoma in a Mesiodens

by Aakriti Chandra, Nilima Thosar, Ramakrishna Yeluri, Ishani Rahate and Dhruvi Solanki

Diagnostics 2025, 15(18), 2335; https://doi.org/10.3390/diagnostics15182335 - 15 Sep 2025

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Abstract

Dilated Composite Odontoma also known as Dens invaginatus, “dens in dente”, or “tooth within tooth” is a rare dental anomaly resulting from enamel organ infolding during tooth development, often leading to complications like caries and pulp infection. With a prevalence of 7.45%, it [...] Read more.

Dilated Composite Odontoma also known as Dens invaginatus, “dens in dente”, or “tooth within tooth” is a rare dental anomaly resulting from enamel organ infolding during tooth development, often leading to complications like caries and pulp infection. With a prevalence of 7.45%, it commonly affects upper lateral incisors, predominantly as a Type I morphology. Mesiodens, a supernumerary tooth in the anterior maxillary midline, occurs in 89.7% of single cases and 10.3% of bilateral cases. The coexistence of dens invaginatus in a mesiodens is extremely rare, posing diagnostic and treatment challenges. This report presents a unique case of dentin invagination in a mesiodens. Full article

(This article belongs to the Special Issue Advances in Dental Diagnostics)

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16 pages, 4492 KB

Open AccessCase Report

Lip Schwannoma—A Rare Presentation in a Pediatric Patient: Case Report and a Literature Review

by Cinzia Casu, Mara Pinna, Andrea Butera, Carolina Maiorani, Girolamo Campisi, Clara Gerosa, Antonella Caiazzo, Andrea Scribante and Germano Orrù

Diagnostics 2025, 15(14), 1825; https://doi.org/10.3390/diagnostics15141825 - 20 Jul 2025

Viewed by 1828

Abstract

Background/Objectives: Schwannoma is a rare tumor, typical in young adults, originating from the myelin sheath that surrounds Schwann cells. It can occur in any part of the Peripheral Nervous System (PNS). It develops in the head and neck region in 25–48% of [...] Read more.

Background/Objectives: Schwannoma is a rare tumor, typical in young adults, originating from the myelin sheath that surrounds Schwann cells. It can occur in any part of the Peripheral Nervous System (PNS). It develops in the head and neck region in 25–48% of cases, and the eighth pair of cranial nerves (vestibulocochlear nerves) are the most hit (vestibular schwannoma). Oral cavity involvement is exceedingly rare, accounting for about 1–2% of all cases. The most affected oral site is the tongue, especially its anterior third, while localization on the lip is one of the least common sites for the development of this lesion. Case Presentation: A lower lip schwannoma on a 17-year-old boy, present for about 7 years, was documented. Material and Methods: PubMed and Google Scholar were used as research engines; English scientific works published in the last 20 years (2005–2024) regarding oral cavity involvement, using the keywords “Schwannoma”, “Oral Schwannoma”, “Pediatric Oral Schwannoma”, and “Schwannoma of the lip”, were considered. Results: In total, 805 and 16,890 items were found on PubMed and Google Scholar search engines, respectively. After title, abstract, full text evaluation, and elimination of duplicates, 26 articles were included in the review process. Discussion: Clinically, oral schwannoma presents as an asymptomatic hard–elastic fluctuating mass, often misdiagnosed on the lip as a traumatic or inflammatory lesion (e.g., mucocele). Biopsy is mandatory, and histological examination reveals positivity to the neuronal marker S-100. Conclusions: Complete excision also prevents recurrence. Malignant transformation is extremely rare. Full article

(This article belongs to the Special Issue Advances in Dental Diagnostics)

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