Dento maxillo facial radiology最新文献

{"title":"Comparative assessment of marginal alveolar bone using ultrasonography and cone-beam computed tomography.","authors":"Sefa Aydindogan, Hsun-Liang Chan, Oliver D Kripfgans, Yunus Emre Balaban, Muslu Kazım Körez, Elif Mutafcılar Velioğlu, Kaan Orhan, Sema S Hakki","doi":"10.1093/dmfr/twaf080","DOIUrl":"10.1093/dmfr/twaf080","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to compare crestal facial bone measurements obtained from ultrasonography (USG) and cone-beam computed tomography (CBCT).</p><p><strong>Methods: </strong>A total of 200 teeth from 15 systemically healthy individuals were included. Teeth were categorized as maxillary anterior (n = 50), maxillary posterior (n = 50), mandibular anterior (n = 50), and mandibular posterior (n = 50). Marginal bone level (MBL) and facial bone thickness at 1 mm (MBT-1), 2 mm (MBT-2), and 3 mm (MBT-3) apical to the bone crest were measured using both USG and CBCT. USG imaging utilized an 18 MHz transducer in B-mode, with standardized settings. Measurements were repeated twice by 2 independent examiners to assess intra- and inter-observer reliability. Interclass correlation coefficients (ICCs) and Bland-Altman plots were used for statistical comparisons.</p><p><strong>Results: </strong>The ICCs between examiners ranged from 0.812 to 0.980. MBL, MBT-1, and MBT-2 measurements between ultrasound and CBCT readings showed excellent agreement (ICCs > 0.75). The agreement for MBT-3 in mandibular anterior was fair (ICC: 0.528). Overall, mean difference between the 2 methods for MBL was 0.06 mm and for MBT-1 was 0.018 mm, without systematic bias.</p><p><strong>Conclusions: </strong>Ultrasound can be a valuable and reproducible tool for MBL and MBT-1 measurements, and it can serve as an alternative to CBCT. Despite reasonable agreement in MBT-2 and MBT-3, potential variability should be considered.</p><p><strong>Advances in knowledge: </strong>While widely used for soft tissue measurements, USG has limited application in marginal alveolar bone assessment in living humans. This study demonstrates the potential use of USG in the evaluation of facial marginal alveolar bone in different regions of the oral cavity.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"217-227"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145370099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-step prediction of inferior alveolar nerve injury after mandibular third-molar extraction using contrastive learning and Bayesian auto-tuned deep learning model.","authors":"Kyubaek Yoon, Yiseul Choi, Myoungho Lee, Jaeyeon Kim, Jun-Young Kim, Jin-Woo Kim, Jongeun Choi, Wonse Park","doi":"10.1093/dmfr/twaf069","DOIUrl":"10.1093/dmfr/twaf069","url":null,"abstract":"<p><strong>Objective: </strong>Inferior alveolar nerve (IAN) injury is a critical complication of mandibular third-molar extraction. This study aimed to construct and evaluate a deep learning framework that integrates contrastive learning and Bayesian optimization to enhance predictive performance using cone-beam computed tomography (CBCT) and panoramic radiographs.</p><p><strong>Methods: </strong>A retrospective dataset of 902 panoramic radiographs and 1,500 CBCT images was used. Five deep learning architectures (MobileNetV2, ResNet101D, Vision Transformer, Twins-SVT, and SSL-ResNet50) were trained with and without contrastive learning and Bayesian optimization. Model performance was evaluated using accuracy, F1-score, and comparison with oral and maxillofacial surgeons (OMFSs).</p><p><strong>Results: </strong>Contrastive learning significantly improved the F1-scores across all models (e.g., MobileNetV2: 0.302 to 0.740; ResNet101D: 0.188 to 0.689; Vision Transformer: 0.275 to 0.704; Twins-SVT: 0.370 to 0.719; SSL-ResNet50: 0.109 to 0.576). Bayesian optimization further enhanced the F1-scores for MobileNetV2 (from 0.740 to 0.923), ResNet101D (from 0.689 to 0.857), Vision Transformer (from 0.704 to 0.871), Twins-SVT (from 0.719 to 0.857), and SSL-ResNet50 (from 0.576 to 0.875). The AI model outperformed OMFSs on CBCT cross-sectional images (F1-score: 0.923 vs. 0.667) but underperformed on panoramic radiographs (0.666 vs. 0.730).</p><p><strong>Conclusions: </strong>The proposed single-step deep learning approach effectively predicts IAN injury, with contrastive learning addressing data imbalance and Bayesian optimization enhancing model performance. While artificial intelligence surpasses human performance on CBCT images, panoramic radiograph analysis still benefits from expert interpretation. Future work should focus on multi-center validation and explainable artificial intelligence for broader clinical adoption.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"150-157"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis of nasopalatine duct and nasopalatine duct cyst in CBCT images: a radiomics-based machine learning approach.","authors":"Hazal Duyan Yüksel, Beyzanur Büyük, Burcu Evlice","doi":"10.1093/dmfr/twaf076","DOIUrl":"10.1093/dmfr/twaf076","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to evaluate the diagnostic performance of machine learning (ML) algorithms based on radiomic features extracted from cone-beam CT (CBCT) images in differentiating the nasopalatine duct (NPD) from the nasopalatine duct cyst (NPDC), and to compare their performance with that of a dentomaxillofacial radiologist.</p><p><strong>Methods: </strong>CBCT scans from 101 histopathologically confirmed NPDC cases and 101 age- and sex-matched controls with normal NPD were retrospectively analysed. Manual segmentation was performed to extract 1037 radiomic features (original, Laplacian of Gaussian, and wavelet-transformed). After dimensionality reduction, 5 ML models (support vector machine [SVM], random forest [RF], decision tree [DT], k-nearest neighbours [KNN], and logistic regression [LR]) were trained using 5-fold cross-validation. Performance was evaluated using the area under the ROC curve (AUC), sensitivity, specificity, precision, recall, and F1-score.</p><p><strong>Results: </strong>Among the 11 optimal features identified through feature selection, large area high grey level emphasis and zone variance from the grey level size zone matrix (GLSZM) class were the most prominent. SVM achieved the highest performance in the test set (AUC and all other metrics = 1.00). The radiologist showed comparable but slightly lower overall performance than SVM (AUC = 0.94, with other metrics between 0.93 and 0.95).</p><p><strong>Conclusions: </strong>ML algorithms based on radiomic features extracted from CBCT images can effectively differentiate NPD from NPDC. Unlike standard visual interpretation, this approach analyses quantitative image features via mathematical models, yielding objective and reproducible results. It may serve as a non-invasive, complementary decision-support tool, particularly in diagnostically challenging cases.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"184-193"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145250319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does ambient light exposure of photostimulable phosphor plates compromise the radiographic diagnosis of simulated internal root resorption?","authors":"Matheus Sampaio-Oliveira, Fernanda Bulhões Fagundes, Luiz Eduardo Marinho-Vieira, Taruska Ventorini Vasconcelos, Frederico Sampaio Neves, Matheus L Oliveira","doi":"10.1093/dmfr/twaf068","DOIUrl":"10.1093/dmfr/twaf068","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the impact of ambient exposure of photostimulable phosphor (PSP) plates and digital enhancement on detecting internal root resorption (IRR).</p><p><strong>Methods: </strong>Thirty-five single-rooted teeth were selected, including 15 with artificially induced IRR (via 3-hour immersion in 37% hydrochloric acid) and 20 controls. Three repeated periapical radiographs were acquired of each tooth using the parallelling technique and PSP plates from the Express, VistaScan Mini, and CS 7600 digital radiographic imaging systems. For each set of 3 X-ray exposures, prior to scanning, one PSP plate was kept shielded from ambient light, another was exposed to ambient light for 5 seconds, while the third was exposed for 10 seconds. The presence of IRR in the total sample of 315 radiographs was assessed by 4 independent examiners using a 5-point scale. Initially, digital enhancement was not allowed, and these images were considered originals. A second round was conducted with adjustments permitted (enhanced radiographs). Sensitivity, specificity, and area under the receiver operating characteristic curve were calculated and compared using 2-way analysis of variance (α = 0.05).</p><p><strong>Results: </strong>No significant differences were found among different light exposure times across all systems (P > .05). In the CS 7600, enhanced radiographs showed significantly higher sensitivity and lower specificity compared to originals (P < .05).</p><p><strong>Conclusions: </strong>Ambient light exposure of PSP for up to 10 seconds does not compromise IRR diagnosis. Digital enhancement in CS 7600 may increase detection but reduce specificity, requiring cautious interpretation to avoid overdiagnosis.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"144-150"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic contrast-enhanced MRI and diffusion kurtosis imaging for the differentiation of salivary gland tumours.","authors":"Jinru Yu, Huan Yang, Ning Zheng, Shuo Shao","doi":"10.1093/dmfr/twaf078","DOIUrl":"10.1093/dmfr/twaf078","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to evaluate the diagnostic ability of relative values of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion kurtosis imaging (DKI) quantitative parameters for salivary gland tumours (SGTs).</p><p><strong>Methods: </strong>A total of 107 patients with histopathologically confirmed SGTs (18 malignant [MTs], 38 pleomorphic adenomas [PAs], 31 Warthin tumours [WTs], 20 basal cell adenomas [BCAs]) underwent MRI with DKI and DCE-MRI sequences. Quantitative parameters included DCE-MRI-derived volume transfer constant (Ktrans), rate constant (Kep), fractional volume of the extravascular-extracellular space (Ve), plasma fraction (Vp), and DKI-derived mean kurtosis (MK) and mean diffusion (MD).The receiver operating characteristic (ROC) curve were used for statistical analysis. Statistical significance was set at P < 0.05.</p><p><strong>Results: </strong>PAs exhibited the lowest MK (0.49 ± 0.15) among all groups (P < 0.05). Compared to WTs, PAs showed lower Kep (366.89 [260.06, 568.32]×10-³ min-1), higher MD (2.02 ± 0.42 × 10-³ mm2/s), and higher Ve (551.83 [388.10, 883.19]×10-³). PAs also displayed higher Ve, lower Kep, and lower Vp (85.42 [20.53, 332.72])×10-³) than BCAs, and lower Vp with higher Ve than MTs (all P < 0.05). WTs had significantly lower Ve (218.86 [142.07, 341.76]×10-³) than MTs (P = 0.001). BCAs demonstrated lower MK (0.61 ± 0.23) and higher MD (1.97 ± 0.44 × 10-³ mm2/s) compared to WTs and MTs (P < 0.05), alongside lower Ktrans (355.25 [211.88, 506.92]×10-³ min-1) and Ve (380.89 [271.28, 589.53]×10-³) than WTs (P < 0.05). Logistic regression analysis revealed enhanced discrimination: MK+Ve (AUC = 0.895) and MK + MD + Ve + Kep (AUC = 0.936) differentiated PAs from WTs; Kep + Vp + Ve distinguished PAs from BCAs (AUC = 0.843); MD + MK + Vp separated PAs from MTs (AUC = 0.854); Ktrans + MK differentiated WTs from BCAs (AUC = 0.856).</p><p><strong>Conclusions: </strong>DKI and DCE-MRI parameters complement each other, enabling accurate SGT subtype differentiation. Combined use of these parameters achieves high diagnostic accuracy, and a stepwise diagnostic flowchart was designed to facilitate systematic discrimination among the four tumour types.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"194-206"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145376377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-based fully automatic 3D paranasal sinus segmentation.","authors":"Meryem Kaygısız Yiğit, Alp Pınarbaşı, Meryem Etöz, Şuayip Burak Duman, İbrahim Şevki Bayrakdar","doi":"10.1093/dmfr/twaf057","DOIUrl":"10.1093/dmfr/twaf057","url":null,"abstract":"<p><strong>Objectives: </strong>Precise 3D segmentation of paranasal sinuses is essential for accurate diagnosis and treatment. This study aimed to develop a fully automated segmentation algorithm for the paranasal sinuses using the nnU-Net v2 architecture.</p><p><strong>Methods: </strong>The nnU-Net v2-based segmentation algorithm was developed using Python 3.6.1 and the PyTorch library, and its performance was evaluated on a dataset of 97 cone beam CT (CBCT) scans. Ground truth annotations were manually generated by expert radiologists using the 3D Slicer software, employing a polygonal labelling technique across sagittal, coronal, and axial planes. Model performance was assessed using several quantitative metrics, including accuracy, Dice coefficient (DC), sensitivity, precision, Jaccard index, area under the curve (AUC), and 95% Hausdorff distance (95% HD).</p><p><strong>Results: </strong>The nnU-Net v2-based algorithm demonstrated high segmentation performance across all paranasal sinuses. DC values were 0.94 for the frontal, 0.95 for the sphenoid, 0.97 for the maxillary, and 0.88 for the ethmoid sinuses. Accuracy scores exceeded 99% for all sinuses. The 95% HD values were 0.51 mm for both the frontal and maxillary sinuses, 0.85 mm for the sphenoid sinus, and 1.17 mm for the ethmoid sinus. Jaccard indices were 0.90, 0.91, 0.94, and 0.80, respectively.</p><p><strong>Conclusions: </strong>This study highlights the high accuracy and precision of the nnU-Net v2-based CNN model in the fully automated segmentation of paranasal sinuses from CBCT images. The results suggest that the proposed model can significantly contribute to clinical decision-making processes, facilitating diagnostic and therapeutic procedures.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"61-72"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a fully automated dental age estimation framework from panoramic radiographs using tooth-level information with an attention-weighting module.","authors":"Witsarut Upalananda, Sangsom Prapayasatok, Sakarat Na Lampang, Ornicha Dilokrattanaphichit, Chawin Chairat, Sitthichok Chaichulee","doi":"10.1093/dmfr/twaf063","DOIUrl":"10.1093/dmfr/twaf063","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to develop a fully automated and explainable framework for dental age estimation from panoramic radiographs in young individuals.</p><p><strong>Methods: </strong>A dataset of 1639 radiographs from individuals aged 8 to 23 years was used. The proposed 2-stage pipeline involved: (1) oriented tooth detection using the YOLO11-OBB model and (2) age estimation using deep learning-based regression models with an attention-weighting module to aggregate predictions from individual teeth. Auxiliary features, including the presence of deciduous teeth and sex, were also evaluated for their impact on model performance.</p><p><strong>Results: </strong>For the first stage, the tooth detection model achieved an F1 score of 0.981, demonstrating accurate tooth localization and identification. In the later stage, the best-performing model, DenseNet-121 with the deciduous teeth feature, achieved a mean absolute error (MAE) of 1.05 ± 0.95 years. Compared to traditional methods, the proposed framework significantly reduced the MAE.</p><p><strong>Conclusions: </strong>This study developed an explainable, high-performing deep learning framework that offers a promising solution for real-world age estimation in the forensic domain.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"88-98"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144946578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiological assessment of dissected cervical lymph nodes in level III affected by the area of supraomohyoid neck dissection.","authors":"Yohei Takeshita, Yoshio Ohyama, Soichiro Ibaragi, Yuki Matsushita, R Shane Tubbs, Norio Kitagawa, Toshiyuki Kawazu, Miki Hisatomi, Shunsuke Okada, Mamiko Fujikura, Yoshinobu Yanagi, Joe Iwanaga","doi":"10.1093/dmfr/twaf065","DOIUrl":"10.1093/dmfr/twaf065","url":null,"abstract":"<p><strong>Objectives: </strong>To compare the number of dissected cervical lymph nodes in the anatomical level III with that in supraomohyoid neck dissection (SOHND) level III affected by the anatomical relationship between the omohyoid muscle and cricoid cartilage using contrast-enhanced CT (CE-CT) images to assess the validity of the current SOHND.</p><p><strong>Methods: </strong>CE-CT images of the patients who suffered from malignant tumours in the oral and maxillofacial regions were reviewed. The number of cervical lymph nodes both in the anatomical level III (area between the centre of the inferior border of the hyoid bone [HB] and the inferior border of the cricoid cartilage [CC]) and SOHND level III (area between HB and the intersection of the omohyoid muscle and internal jugular vein [OM-IJ]) were recorded, respectively.</p><p><strong>Results: </strong>The rate of patients whose number of lymph nodes in level III was affected by the positional relationship between the OM-IJ and CC was almost equal in males and females. As for the patients with OM-IJ below the CC, the number of lymph nodes in SOHND level III increased from that of anatomical level III. Females showed significantly higher values than males (P < .05). Meanwhile, for patients with OM-IJ at or above the CC, the number of lymph nodes in SOHND level III decreased from that of anatomical level III.</p><p><strong>Conclusions: </strong>The number of dissected cervical lymph nodes differed between the SOHND dissection area and levels I, II, and III. In most cases, SOHND dissects more cervical lymph nodes, especially in female patients.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"106-111"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12796652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145091344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does the disinfection of a photostimulable phosphor plate receptor with peracetic acid impair carious lesions diagnosis?","authors":"Débora Costa Ruiz, Francisco Haiter-Neto, Hugo Gaêta-Araujo, Deborah Queiroz Freitas","doi":"10.1093/dmfr/twaf067","DOIUrl":"10.1093/dmfr/twaf067","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the impact of disinfecting a photostimulable phosphor plate (PSP) receptor with peracetic acid on proximal carious lesions diagnosis. Additionally, to evaluate and measure artefacts caused by disinfection.</p><p><strong>Methods: </strong>The proximal surfaces of 40 posterior teeth were evaluated by microcomputed tomography to confirm whether they were sound or carious. These teeth were then mounted in 20 phantoms. Using a PSP receptor (Express System), 1 radiograph of each phantom was acquired. Then, 20 disinfections with peracetic acid were performed, corresponding to a disinfection cycle. After the first disinfection cycle, new radiographs were acquired. This process was repeated 9 times, totalling 200 disinfections (10 cycles × 20 disinfections) and 220 radiographs (200 radiographs + 20 baseline radiographs). The radiographs were assessed independently by 5 examiners for carious lesions diagnosis. A radiologist identified and measured the size of the artefacts. Area under the receiver-operating characteristic curve, sensitivity, and specificity were calculated and compared among the number of disinfections by analysis of variance (α = 0.05). The weighted Kappa index evaluated intra- and inter-examiner agreements.</p><p><strong>Results: </strong>The disinfections did not affect the diagnostic metrics for detecting proximal carious lesions (P > .05). After 200 disinfections, the biggest artefact measured 0.24 cm. Intra- and inter-examiner agreements ranged from moderate to substantial (0.60-0.69) and from fair to moderate (0.34-0.60).</p><p><strong>Conclusions: </strong>After 200 disinfections, the radiographic detection of proximal carious lesions remains unaffected by the disinfection with peracetic acid, and the artefacts are still small.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"112-118"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145130113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for osteoporosis screening in dental practice: a systematic review.","authors":"Raiza Querrer, Lorena S Vieira, Ana Beatriz Teodoro, Nilce Santos Melo, Carla Ruffeil M Mesquita, Maria A G Silva, Paulo Tadeu de Souza Figueiredo, André Ferreira Leite","doi":"10.1093/dmfr/twaf052","DOIUrl":"10.1093/dmfr/twaf052","url":null,"abstract":"<p><strong>Objectives: </strong>To conduct a systematic review evaluating the performance of deep learning (DL) tools in osteoporosis screening using dental imaging and to assess whether these models have been implemented in dental practice.</p><p><strong>Methods: </strong>Search was performed across 7 electronic databases and 2 sources of grey literature. Included studies applied DL algorithms to dental radiographs or CT in adults diagnosed with osteoporosis, using dual-energy X-ray absorptiometry (DXA) or expert assessment as the reference standard. Risk of bias was assessed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool, alongside an AI-specific checklist and GRADE to evaluate certainty of evidence.</p><p><strong>Results: </strong>Thirteen studies met the inclusion criteria. DXA was the most common reference standard (n = 08), and panoramic radiography was the predominant imaging modality (n = 12). Accuracy was the most frequently reported metric (n = 12). Most models used pretrained convolutional neural networks, such as VGG16, GoogleNet, ResNet, and AlexNet. ResNet and EfficientNet architectures showed superior performance, particularly when combined with ensemble techniques and clinical covariates. However, no study reported external validation or implementation in dental practice, limiting their applicability.</p><p><strong>Conclusions: </strong>All DL models demonstrated potential as supportive tools for osteoporosis screening in dentistry. However, the absence of external validation and clinical integration limits their real-world use. Future research should focus on standardization and development of accessible, validated systems.</p><p><strong>Advances in knowledge: </strong>This systematic review is the first to show that, despite advancements in DL for osteoporosis screening, clinical applicability remains limited. It underscores the need for robust, user-friendly interfaces to facilitate integration into routine dental practice.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"1-27"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}