Search Results (117)

Accurate assessment of radiographic bone loss (RBL) is essential for periodontal diagnosis and staging; however, manual measurement from dental radiographs is labor-intensive, time-consuming and subject to inter- and intra-examiner variability. Existing AI-based methods primarily formulate bone loss assessment as classification, landmark prediction, or direct segmentation of thin anatomical structures, limiting measurement interpretability and robustness. This study proposes clinically interpretable two-phase framework for automated and clinically interpretable RBL estimation from periapical radiographs. The framework explicitly separates anatomical structure recognition from geometric measurement, improving transparency and reducing error propagation. In the first phase, deep learning models segment key anatomical structures, including the crown, root, third root and alveolar bone. In the second phase, a deterministic geometric algorithm extracts clinically relevant landmarks, including the cemento–enamel junction (CEJ), bone crest, and root apex, and computes root length, CEJ–bone crest distance, and radiographic bone loss following established periodontal measurement principles. The framework was evaluated on a curated dataset of annotated radiographs. DS-TransUNet achieved the best segmentation performance. Quantitative evaluation yielded mean absolute errors of 0.81 mm for CEJ–bone crest distance, 0.71 mm for root length, and 5.89% for RBL estimation. Bland–Altman analysis demonstrated minimal systematic bias (−1.03%) and good agreement with expert measurements across different disease severities, supporting the framework’s potential as an objective and clinically applicable tool for periodontal bone loss assessment. Full article

Background/Objectives: Periodontitis is a highly prevalent inflammatory disease and a major cause of tooth loss worldwide. Accurate diagnosis requires integration of clinical and radiographic findings, but interpretation of panoramic radiographs is subject to variability. Artificial intelligence (AI) has emerged as a promising adjunct for radiographic assessment. This systematic review evaluated the diagnostic performance of AI-based models for detecting periodontitis using panoramic radiographic images. Methods: A systematic search of PubMed, Scopus, and Web of Science identified studies published between 1 January 2015 and 1 March 2026. Eligible studies assessed AI models for periodontitis detection on panoramic radiographs and used either clinically confirmed periodontal diagnosis or expert radiographic annotation as the reference standard. Data extraction and quality assessment were performed independently by two reviewers using the QUADAS-2 tool. Owing to heterogeneity in AI architectures, datasets, and outcome measures, a narrative synthesis was conducted. Results: Nine studies met the inclusion criteria, comprising more than 20,000 radiographs. AI models included convolutional neural networks (CNNs), segmentation-based systems, and hybrid architectures. Sensitivity ranged from 0.795 to 1.00, specificity from 0.784 to 0.99, and AUC values from 0.843 to 0.967. Studies using clinical periodontal diagnosis as the reference standard generally reported lower performance than those relying solely on expert annotation. Only four studies performed external validation, and dataset sizes varied widely. One study combining panoramic and periapical radiographs showed moderate diagnostic performance. Conclusions: AI-based diagnostic models demonstrate promising performance for detecting periodontitis on panoramic radiographs, with several studies reporting high sensitivity and AUC values. However, heterogeneity in reference standards, limited external validation, and inconsistent dataset quality restrict generalizability. AI should be considered an adjunct to, rather than a replacement for, comprehensive clinical periodontal examination. Standardized datasets and robust external validation are needed to support clinical implementation. Full article

Chronic periapical periodontitis is a persistent inflammatory disease characterized by progressive bone destruction around the tooth apex. Manual radiographic detection of these lesions is subjective and time-consuming, highlighting the need for automated diagnostic tools. This paper presents a unified deep learning framework for joint tooth segmentation and periapical lesion detection in panoramic radiographs. Our approach employs a joint process: first, a deep learning model identifies and segments individual teeth according to standard dental numbering systems, while a second one detects periapical lesions within the tooth regions obtained from the segmentation outputs in the first stage. The framework incorporates an advanced loss function (Powerful IoU v2) to improve bounding-box regression accuracy and a spatial association mechanism to map detected lesions to specific teeth based on geometric overlap analysis. Our proposed tooth segmentation model achieves an mAP@50 of 97.7% and a mean Dice coefficient of 93.5%, while the periapical lesion detector reaches an mAP@50 of 91.9%. Furthermore, our region-of-interest approach yields a 3.49× computational speedup, averaging 0.1589 s per radiograph when compared to full-image processing. Trained exclusively on open-source datasets, this reproducible framework achieves explicit tooth-to-lesion mapping, providing an efficient and practical tool for periapical lesion screening. Full article

Dental diseases have long been taught and treated as separate entities: cariology, operative dentistry, endodontics, and periodontology, each working within its own boundaries. However, increasing biological and clinical evidence suggests that this classified view does not fully reflect how disease progresses in the mouth. Instead, dental disease should be understood as a continuum within the interconnected tooth–pulp–periodontium complex. This review provides current evidence showing how dental caries can serve as the starting point of a process that can progress through pulpitis and apical periodontitis and eventually affect surrounding periodontal tissues. Caries is now widely known as a biofilm-driven and host-influenced condition shaped by ecological imbalance rather than specific pathogens alone. As lesions penetrate deeper into dentin, the structure becomes more permeable, permitting diffusion of microbial metabolites and signaling molecules toward the pulp. This initiates a multifaceted inflammatory reaction within the pulp tissue. At this stage, pulpitis becomes a critical turning point, where the outcome depends on microbial load, lesion activity, host response, and quality of clinical intervention. If the disease is not well controlled, it may lead to pulp necrosis, allowing infection to spread beyond the root canal and initiate periapical inflammation. Through anatomical pathways such as apical foramina and lateral canals, these processes can extend further, sometimes resembling or overlapping with periodontal disease. This overlap creates diagnostic challenges, as conventional tests may not always distinguish between conditions. A structured, pathway-based diagnostic approach is therefore essential. From a treatment perspective, this continuum model highlights early intervention, minimally invasive care, preservation of pulp vitality when possible, and maintenance of a strong coronal seal. Ultimately, stronger integration across dental disciplines can improve diagnosis, guide treatment decisions, support long-term tooth preservation, and promote unified dental education. This article presents a narrative review supported by a structured literature search and proposes a clinically actionable framework that extends established endodontic–periodontal concepts upstream to include caries initiation and restorative modulation. Full article

The term “periapical lesion” refers to a pathological change in the tissues surrounding the apex of a tooth root, defined by its anatomical location rather than a distinct disease entity. Periapical lesions may be of endodontic origin, most commonly resulting from microbial infection of the root canal system following pulp necrosis due to caries, trauma, or other insults, or of non-endodontic origin, such as developmental cysts, benign and malignant odontogenic and non-odontogenic tumors, and fibro-osseous lesions. Accurate diagnosis requires a systematic approach combining patient history, clinical examination, pulp vitality testing, and radiographic assessment; histopathological evaluation is indicated when clinical and radiographic findings are inconsistent or suspicious. The pathophysiology of these lesions involves dynamic interactions between root canal microorganisms and the host immune-inflammatory response. The primary management for endodontic periapical lesions is root canal treatment, which aims to reduce or eliminate root canal microorganisms through mechanical debridement and chemical disinfection. Persistent or extensive endodontic lesions and non-endodontic lesions may require surgical intervention. Molecular and inflammatory biomarkers have been investigated as adjunctive tools for assessing disease activity and prognosis; however, these remain largely investigational and are not yet part of routine clinical practice. Future developments in artificial intelligence, advanced imaging, molecular diagnostics, and personalized therapies may enhance the diagnosis and management of periapical lesions, although further clinical validation is required. Full article

Matrix metalloproteinases (MMPs) are zinc-dependent proteolytic enzymes involved in extracellular matrix remodelling in oral and dental tissues, including the periodontal ligament, alveolar bone, dentin, dental pulp, and periapical tissues. This narrative review summarises selected evidence on the role of MMPs and tissue inhibitors of metalloproteinases (TIMPs) in orthodontic tooth movement, dental trauma and root resorption, restorative adhesive dentistry, and pulp/periapical disease. Particular attention is given to signalling pathways that regulate MMP/TIMP activity, including nuclear factor kappa B (NF-κB), mitogen-activated protein kinase (MAPK), Wnt/β-catenin, and transforming growth factor beta (TGF-β)/Smad-related mechanisms. The review also discusses the biomarker potential and translational status of MMP-targeted strategies. Across clinical contexts, MMP activity contributes to both matrix degradation and tissue repair, and its biological effect depends on local stimuli, TIMP-mediated regulation, pathway crosstalk, and the stage of disease or treatment. Full article

Introduction: Odontogenic sinusitis (ODS) is an underrecognized cause of maxillary sinus inflammation and is frequently associated with dental, periodontal, endodontic, and iatrogenic factors. Accurate identification of the odontogenic source is essential for appropriate treatment planning. Cone-beam computed tomography (CBCT) allows detailed evaluation of the maxillary sinus, adjacent teeth, alveolar bone, and periodontal structures, and may improve the radiologic differentiation of ODS. Materials and Methods: This retrospective observational study analyzed radiologic data from patients evaluated and treated by the authors for suspected odontogenic sinusitis between 2019 and 2026. The final study group included 85 patients with CBCT-based evidence of odontogenic pathology affecting the maxillary sinus. CBCT scans were reviewed to identify tooth-related and treatment-related etiologic factors associated with ODS. Based on the radiologic findings, the authors developed a CBCT-based classification of odontogenic etiologies and proposed an imaging-guided management algorithm. Results: CBCT identified a broad spectrum of odontogenic factors associated with maxillary sinus disease. The most relevant radiologic patterns included endodontic and periapical pathology, periodontal or combined endo-periodontal disease, post-extraction inflammatory changes, odontogenic cysts, oro-antral communication or fistula, retained roots or teeth, displaced endodontic materials, and grafting or implant-related complications. These findings were organized into 16 radiologic categories reflecting the principal etiologic pathways of ODS. The proposed classification facilitated correlation between radiologic presentation and the recommended dental, surgical, and otolaryngologic treatment approach. Conclusions: CBCT is a valuable imaging modality for identifying odontogenic causes of maxillary sinus inflammation and provides more precise diagnostic information than conventional radiography alone. A structured CBCT-based evaluation may improve etiologic diagnosis, support multidisciplinary decision-making, and help guide individualized management of patients with ODS. Full article

Linear measurements are used on radiographs for various purposes, such as measuring lengths in endodontics, analysing bone loss in periodontal disease, and making age determinations in forensic dentistry. The purpose of this study was to analyse the accuracy of periapical radiographs for the measurement of the contact point to the crest of the bone compared to the actual measurements of a dried skull. A dried skull had lead squares measuring 1 × 1 mm attached to the contact point and the bone crest. Each site was radiographed using a parallel technique with a Rinn holder. The radiographs were taken perpendicular to the tooth and repeated at 10° and 20° horizontal angulations. The results showed that variation in the angle of the radiograph had a significant effect on the resulting measurements, F(1.7, 26.9) = 218.265, p < 0.001. The results from this study indicate that when measuring the contact point to the crest of the bone, a shift of 20° from the perpendicular of the tooth has a significant effect on the radiograph measurement and the actual measurement of the contact point to the crest of the bone on the actual skull. Full article

Radiographic assessment plays an important role in the diagnosis of periodontal disease. However, intraoral radiographs often underestimate interproximal bone level. Cone beam CT (CBCT) has been shown to outperform 2D radiography in the detection of furcation involvement, but research on bone level measurements is scarce. This study aimed to assess the accuracy of CBCT and intraoral periapical radiographs (PARX) as diagnostic tools for evaluating bone morphology compared to clinical measurements in an open field setting. This prospective observational diagnostic accuracy study consisted of 29 patients who were planned for periodontal surgery. Pre-surgical assessment consisted of PARX and CBCT compared to open field measurements, using a periodontal probe prior to any bone correction. The following parameters were evaluated: CEJ-BD (cementoenamel junction-base of the defect), AC-BD (alveolar crest–base of the defect), furcation involvement, and bone morphology (angular vs. horizontal). Twenty-nine patients were included, and a total of 173 interproximal sites were assessed. The ICC for the primary variable (CEJ-BD) indicated poor reliability for PARX (0.47), while CBCT showed moderate reliability (0.63). For infrabony measurements, ICC values indicated poor reliability for PARX (0.38) and moderate reliability for CBCT (0.56). Regarding furcation involvement, periapical radiography showed fair agreement (κ = 0.32), whereas CBCT demonstrated substantial agreement (κ = 0.72). Bone morphology assessment showed slight agreement for PARX (κ = 0.11) and fair agreement for CBCT (κ = 0.40). CBCT provides more consistent and reliable assessments of bone level, defect morphology and furcation involvement compared to conventional periapical radiographs. Full article

Background and Clinical Significance: Dentinogenesis imperfecta is a hereditary dentin disorder that compromises tooth structure, esthetics, and function. Case Presentation: We report the case of a 1.5-year-old female presenting with generalized discoloration of the primary dentition and intermittent sensitivity to thermal stimuli. The diagnosis of dentinogenesis imperfecta was established based on characteristic clinical features, radiographic findings, and a positive family history. The patient was followed longitudinally from 2020 to 2025, with documentation of diagnostic findings, radiographic changes, therapeutic interventions, and outcomes. Management included placement of composite veneers on the maxillary incisors for esthetic rehabilitation and sealants on second primary molars as a preventive measure. Although various management approaches have been described in the literature, evidence regarding optimal strategies and long-term outcomes in the primary dentition remains limited. This case highlights the occurrence of asymptomatic periapical pathology and root resorption despite minimal clinical symptoms, underscoring the challenges of relying on symptom-based assessment alone. Conclusions: Early diagnosis, regular radiographic monitoring, and individualized, risk-based treatment planning are essential in managing dentinogenesis imperfecta. This case emphasizes the importance of recognizing asymptomatic disease progression and integrating psychosocial considerations into comprehensive care. Full article

Background and Clinical Significance: Vascular lesions of the face, particularly arteriovenous malformations (AVM) and mixed hemangiomas (MH), pose significant diagnostic and therapeutic challenges because of their complex anatomy, unpredictable behavior, and high risk of bleeding. Surgical planning should be individualized and often requires a staged approach with meticulous interdisciplinary coordination to ensure patient safety. The presence of a concomitant odontogenic infection further complicates management, as local inflammation may exacerbate vascular instability and increase the risk of life-threatening complications. Local inflammation and infection might cause some life-threatening conditions, especially when an abscess occurs in the area of any vascular lesion. Ensuring that the oral cavity is free from potential odontogenic infections is a particularly important issue in many complex cases, especially in patients treated for oral, head, and neck cancer or in those with other coexisting morbidities affecting the oral and facial regions. Case Presentation: A 72-year-old man was referred for management of a severe odontogenic infection associated with an extensive facial vascular lesion. The patient’s medical history was significant for arterial hypertension and chronic liver dysfunction (CLD) of unclear etiology. Complete blood testing, including coagulation assessment and liver ultrasonography, was performed, with no contraindication to surgery identified. The scope of odontogenic-related infections was scheduled for simultaneous removal during initial surgery. Preparation for surgery included the local application of sclerotherapy agents. Conclusions: Quite often, a routine panoramic radiograph can help in assessing the status of bone and dentition to undertake all necessary treatment. Severe odontogenic disease, including multiple retained roots, periapical infections, and odontogenic cystic lesions in the context of poor oral hygiene, may lead to the occurrence of possible inflammation. In case of any vascular lesion, a careful diagnostic and therapeutic strategy is needed. This case report highlights that maintaining an infection-free oral environment is a critical component of care in patients with complex facial MH and should be regarded as an essential element of overall treatment planning. Full article

Background: Odontogenic sinusitis (ODS) represents a distinct form of maxillary sinus inflammation arising from dental pathology and is most commonly unilateral. Despite its polymicrobial nature and predominance of anaerobic organisms, molecular characterization of the bacterial profile and its relationship to clinical severity remains limited. This study aimed to evaluate associations between targeted quantitative PCR (qPCR) findings from paired maxillary sinus and periapical lesion samples and clinical, endoscopic, and radiological features of disease. Additionally, the influence of oroantral communication on microbial concordance between odontogenic and sinus sites was examined. Methods: Twenty-eight patients with confirmed ODS were included for analytical cross-sectional study and underwent combined otolaryngological and dental assessment. During endoscopic sinus surgery with extraction of the causative tooth, paired specimens were collected from sinus mucosa and periapical lesions under sterile conditions and preserved for molecular analysis. Targeted qPCR assays using 16S rRNA–based primers were performed to detect predefined odontogenic pathogens. Associations between bacterial detection patterns and clinical, endoscopic, and imaging variables were analyzed. Results: Detection of Streptococcus anginosus group organisms was significantly associated with complete maxillary sinus opacification. Fusobacterium nucleatum and Porphyromonas endodontalis demonstrated higher detection rates in patients with more advanced radiological disease, although statistical significance was not reached. Purulent nasal discharge correlated with detection of Fusobacterium nucleatum, Porphyromonas endodontalis, and streptococcal species. Cases with intraoperative oroantral communication exhibited greater concordance between sinus and dental microbial profiles. Conclusions: ODS is characterized by a polymicrobial environment dominated by anaerobic bacteria, with specific organisms associated with markers of disease severity such as purulent secretion and extensive sinus opacification. Targeted molecular profiling may improve recognition of odontogenic origin and support individualized therapeutic strategies, although larger studies integrating clinical outcomes are required to clarify prognostic implications. Full article

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

Background: Dental disorders, such as cavities, periodontal disease, and periapical infections, remain major global health issues, often resulting in pain, tooth loss, and systemic complications if not identified early. Traditional diagnostic methods rely heavily on visual inspection and manual interpretation of panoramic X-ray images by dental professionals, making them time-consuming, subjective, and less accessible in resource-limited settings. Objectives: Accurate and timely diagnosis is vital for effective treatment and prevention of disease progression, reducing healthcare costs and patient discomfort. Recent advances in deep learning (DL) have demonstrated remarkable potential to automate and improve the precision of dental diagnostics by objectively analyzing panoramic, periapical, and bitewing X-rays. Methods: In this research, a hybrid feature-fusion framework is proposed. It integrates handcrafted Histogram of Oriented Gradients (HOG) features with deep representations from DenseNet-201 and the Shifted Window (Swin) Transformer models. Sequential dependencies among the fused features were learned utilizing the Long Short-Term Memory (LSTM) classifier. The framework was evaluated on the Dental Radiography Analysis and Diagnosis (DRAD) dataset following preprocessing steps, including resizing, normalization, Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement, and image cropping. Results: The proposed LSTM-based hybrid model achieved 96.47% accuracy, 91.76% specificity, 94.92% precision, 91.76% recall, and 93.14% F1-score. Conclusions: The proposed framework offers flexibility, interpretability, and strong empirical performance, making it suitable for various image-based recognition applications and serving as a reproducible framework for future research on hybrid feature fusion and sequence-based classification. Full article

Background: Periapical lesions are common consequences of pulp necrosis but may remain undetectable on conventional intraoral radiographs, becoming evident only on cone-beam computed tomography (CBCT). Improving lesion recognition on plain radiographs is therefore of high clinical relevance. Methods: This retrospective, single-center study analyzed 56 matched pairs of intraoral periapical radiographs (RVG) and CBCT scans. A total of 109 regions of interest (ROIs) were included, which were classified as CBCT-positive/RVG-negative (onlyCBCT, n = 64) or true negative (noLesion, n = 45). Radiomic texture features were extracted from circular ROIs on RVG images using PyRadiomics. Feature distributions were compared using Mann–Whitney U tests with false discovery rate correction, and classification was performed using a logistic regression model with nested cross-validation. Results: Forty-four radiomic texture features showed statistically significant differences between onlyCBCT and noLesion ROIs, predominantly with small to medium effect sizes. For a 40-pixel ROI radius, the classifier achieved a mean area under the ROC curve of 0.71, mean accuracy of 68%, and mean sensitivity of 73%. Smaller ROIs (20–40 pixels) yielded higher AUCs and substantially better accuracy than larger sampling regions (≥60 pixels). Conclusions: Quantifiable radiomic signatures of periapical pathology are present on conventional radiographs even when lesions are visually occult. Radiomics may serve as a complementary decision support tool for identifying CBCT-only periapical lesions in routine clinical imaging. Full article