Dento maxillo facial radiology最新文献

{"title":"Preoperative evaluation of lingual cortical plate thickness and the anatomical relationship of the lingual nerve to the lingual cortical plate via 3T MRI nerve-bone fusion.","authors":"Dongmei Jiang, Junhuan Hong, Yalan Yan, Hao Huang, Peiying You, Weilin Huang, Xiance Zhao, Dejun She, Dairong Cao","doi":"10.1093/dmfr/twae060","DOIUrl":"10.1093/dmfr/twae060","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the reliability of 3T MRI nerve-bone fusion in assessing the lingual nerve (LN) and its anatomical relationship to the lingual cortical plate prior to the impacted mandibular third molar (IMTM) extraction.</p><p><strong>Methods: </strong>The MRI nerve and bone sequences used in this study were 3D T2-weighted fast field echo (3D-T2-FFE) and fast field echo resembling a CT using restricted echo-spacing (FRACTURE), respectively. Both sequences were performed in 25 subjects, and the resulting 3D-T2-FFE/FRACTURE fusion images were assessed by 2 independent observers. Semi-quantitative analyses included assessments of overall image quality, image artefacts, nerve continuity, and the detectability of 5 intermediate points (IPs). Quantitative analyses included measurements of the lingual cortical plate thickness (LCPT), vertical distance (V1* and V2*), and the closest horizontal distance (CHD) between the LN and the lingual cortical plate. Reliability was evaluated using weighted Cohen's kappa coefficient (κ), intraclass correlation coefficient (ICC), and Bland-Altman plots. Differences in LCPT between 3D-T2-FFE/FRACTURE fusion images and cone-beam computed tomography (CBCT) were compared using independent samples t-tests or Mann-Whitney U tests.</p><p><strong>Results: </strong>The fusion images demonstrated that the LN continuity score was 3.00 (1.00) (good), with 88% (44/50) of LNs displayed continuously at the IMTM level. Intra-reader agreement for nerve continuity was moderate (κ = 0.527), as was inter-reader agreement (κ = 0.428). The intra-reader and inter-reader agreement for LCPT measurements at the neck, mid-root, and apex of the IMTM were all moderate (ICC > 0.60). Intra-reader agreements for V1*, V2*, and CHD were moderate to excellent (ICC = 0.904, 0.967, and 0.723, respectively), and inter-reader agreements for V1*, V2*, and CHD were also moderate to excellent (ICC = 0.948, 0.941 and 0.623, respectively). The reliability of LCPT measurements between 3D-T2-FFE/FRACTURE fusion and CBCT was moderate (ICC = 0.609-0.796).</p><p><strong>Conclusions: </strong>The 3D-T2-FFE/FRACTURE fusion technique demonstrated potential feasibility for the identification of the LN and its relationship to the lingual cortical plate, as well as for the measurement of LCPT. This study has generated a dataset that is capable of simultaneously defining the LN and LCPT.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"163-172"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142727042","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":"Interference of titanium and zirconia implants on dental-dedicated MR image quality: ex vivo and in vivo assessment.","authors":"Katrine M Johannsen, Jennifer Christensen, Louise Hauge Matzen, Brian Hansen, Rubens Spin-Neto","doi":"10.1093/dmfr/twae071","DOIUrl":"10.1093/dmfr/twae071","url":null,"abstract":"<p><strong>Objectives: </strong>To assess the impact of titanium and zirconia implants on dental-dedicated MR image (ddMRI) quality ex vivo (magnetic field distortion [MFD]) and in vivo (artefacts).</p><p><strong>Methods: </strong>ddMR images were acquired (MAGNETOM Free.Max, 0.55 T, Siemens Healthineers AG, Forchheim, Germany) using a dental-dedicated coil (Rapid Biomedical, Rimpar, Germany). Ex vivo: three phantoms were manufactured: one agar-embedded titanium implant, one agar-embedded zirconia implant, and one control phantom (agar 1.5%). Field map analysis of images acquired at 0.55 T, 1.5 T, and 3.0 T (MAGNETOM Sola and MAGNETOM Lumina, respectively, Siemens Healthineers AG, Forchheim, Germany) was done to illustrate the extent and severity of MFD caused by the implants. In vivo (0.55 T only): a splint was designed to serve as an implant carrier, allowing diverse implant positions (0, 1, 2, or 5 implants). A volunteer was imaged using multiple pulse sequences. Three blinded observers scored the images twice for the presence, severity, and type of artefacts, illustrated by descriptive statistics and inter- and intra-observer reproducibility (kappa statistics).</p><p><strong>Results: </strong>Ex vivo: titanium produced more severe MFD than zirconia. MFD extent and amplitude increased with field strength (0.55 T < 1.5 T < 3.0 T). In vivo: titanium produced more artefacts than zirconia, generally as signal voids in tooth crowns close to implants. Inter- and intra-observer reproducibility ranged from 0.28 to 0.64 and 0.32 to 0.57, respectively.</p><p><strong>Conclusions: </strong>The prevalence of artefacts increased with magnetic field strength. Titanium generated larger MFD than zirconia. For both materials, artefacts were visible mainly in the crown area. Observer reproducibility needs improvement by dedicated ddMRI training.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"132-139"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142846075","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":"Gray values and noise behavior of cone-beam computed tomography machines-an in vitro study.","authors":"Nicolly Oliveira-Santos, Hugo Gaêta-Araujo, Rubens Spin-Neto, Dorothea Dagassan-Berndt, Michael M Bornstein, Matheus L Oliveira, Francisco Haiter-Neto, Deborah Q Freitas, Ralf Schulze","doi":"10.1093/dmfr/twae053","DOIUrl":"10.1093/dmfr/twae053","url":null,"abstract":"<p><strong>Objectives: </strong>To systematically evaluate the mean gray values (MGVs) and noise provided by bone and soft tissue equivalent materials and air imaged with varied acquisition parameters in 9 cone-beam computed tomography (CBCT) machines.</p><p><strong>Methods: </strong>The DIN6868-161 phantom, composed of bone and soft tissue equivalent material and air gap, was scanned in 9 CBCT machines. Tube current (mA) and tube voltage (kV), field of view (FOV) size, and rotation angle were varied over the possible range. The effect of the acquisition parameters on the MGV and contrast-to-noise indicator (CNI) was analyzed by Kruskal Wallis and Dunn-Bonferroni tests for each machine independently (α = 0.05).</p><p><strong>Results: </strong>Tube current did not influence MGV in most machines. Viso G7 and Veraview X800 presented a decrease in the MGV for increasing kV. For ProMax 3D Max and X1, the kV did not affect the MGV. For the majority of machines, MGV decreased with increasing FOV height. In general, the rotation angle did not affect the MGV. In addition, CNI was lower with lower radiation and large FOV and did not change from 80 kV in all machines.</p><p><strong>Conclusions: </strong>The MGV and noise provided by the tested phantom vary largely among machines. The MGV is mainly influenced by the FOV size, especially for bone equivalent radiodensity. For most machines, when the acquisition parameters affect the MGV, the MGV decrease with the increase in the acquisition parameters.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"140-148"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11784915/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675449","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":"Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: systematic review employing APPRAISE-AI and meta-analysis.","authors":"Yahia H Khubrani, David Thomas, Paddy J Slator, Richard D White, Damian J J Farnell","doi":"10.1093/dmfr/twae070","DOIUrl":"10.1093/dmfr/twae070","url":null,"abstract":"<p><strong>Objectives: </strong>Periodontitis is a serious periodontal infection that damages the soft tissues and bone around teeth and is linked to systemic conditions. Accurate diagnosis and staging, complemented by radiographic evaluation, are vital. This systematic review (PROSPERO ID: CRD42023480552) explores artificial intelligence (AI) applications in assessing alveolar bone loss and periodontitis on dental panoramic and periapical radiographs.</p><p><strong>Methods: </strong>Five databases (Medline, Embase, Scopus, Web of Science, and Cochrane's Library) were searched from January 1990 to January 2024. Keywords related to \"artificial intelligence\", \"Periodontal bone loss/Periodontitis\", and \"Dental radiographs\" were used. Risk of bias and quality assessment of included papers were performed according to the APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support. Meta analysis was carried out via the \"metaprop\" command in R V3.6.1.</p><p><strong>Results: </strong>Thirty articles were included in the review, where 10 papers were eligible for meta-analysis. Based on quality scores from the APPRAISE-AI critical appraisal tool of the 30 papers, 1 (3.3%) were of very low quality (score < 40), 3 (10.0%) were of low quality (40 ≤ score < 50), 19 (63.3%) were of intermediate quality (50 ≤ score < 60), and 7 (23.3%) were of high quality (60 ≤ score < 80). No papers were of very high quality (score ≥ 80). Meta-analysis indicated that model performance was generally good, eg, sensitivity 87% (95% CI, 80%-93%), specificity 76% (95% CI, 69%-81%), and accuracy 84% (95% CI, 75%-91%).</p><p><strong>Conclusion: </strong>Deep learning shows much promise in evaluating periodontal bone levels, although there was some variation in performance. AI studies can lack transparency and reporting standards could be improved. Our systematic review critically assesses the application of deep learning models in detecting alveolar bone loss on dental radiographs using the APPRAISE-AI tool, highlighting their efficacy and identifying areas for improvement, thus advancing the practice of clinical radiology.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"89-108"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827399","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 and evaluation of a deep learning model to reduce exomass-related metal artefacts in cone-beam CT: an ex vivo study using porcine mandibles.","authors":"Matheus L Oliveira, Susanne Schaub, Dorothea Dagassan-Berndt, Florentin Bieder, Philippe C Cattin, Michael M Bornstein","doi":"10.1093/dmfr/twae062","DOIUrl":"10.1093/dmfr/twae062","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and evaluate a deep learning (DL) model to reduce metal artefacts originating from the exomass in cone-beam CT (CBCT) of the jaws.</p><p><strong>Methods: </strong>Five porcine mandibles, each featuring six tubes filled with a radiopaque solution, were scanned using four CBCT units before and after the incremental insertion of up to three titanium, titanium-zirconium, and zirconia dental implants in the exomass of a small field of view. A conditional denoising diffusion probabilistic model, using DL techniques, was employed to correct axial images from exomass-related metal artefacts across the CBCT units and implant scenarios. Three examiners independently scored the image quality of all datasets, including those without an implant (ground truth), with implants in the exomass (original), and DL-generated ones. Quantitative analysis compared contrast-to-noise ratio (CNR) to validate artefact reduction using repeated measures analysis of variance in a factorial design followed by Tukey test (α = .05).</p><p><strong>Results: </strong>The visualisation of the hard tissues and overall image quality was reduced in the original and increased in the DL-generated images. The score variation observed in the original images was not observed in the DL-generated images, which generally scored higher than the original images. DL-generated images revealed significantly greater CNR than both the ground truth and their corresponding original images, regardless of the material and quantity of dental implants and the CBCT unit (P < .05). Original images revealed significantly lower CNR than the ground truth (P < .05).</p><p><strong>Conclusions: </strong>The developed DL model using porcine mandibles demonstrated promising performance in correcting exomass-related metal artefacts in CBCT, serving as a proof-of-principle for future applications of this approach.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"109-117"},"PeriodicalIF":2.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11784918/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142727041","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":"Enhancing panoramic dental imaging with AI-driven arch surface fitting: Achieving improved clarity and accuracy through an optimal reconstruction zone.","authors":"Nayeon Kim, Hyeonju Park, Yun-Hoa Jung, Jae-Joon Hwang","doi":"10.1093/dmfr/twaf006","DOIUrl":"https://doi.org/10.1093/dmfr/twaf006","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Objectives: &lt;/strong&gt;This study aimed to develop an automated method for generating clearer, well-aligned panoramic views by creating an optimized three-dimensional (3D) reconstruction zone centered on the teeth. The approach focused on achieving high contrast and clarity in key dental features, including tooth roots, morphology, and periapical lesions, by applying a 3D U-Net deep learning model to generate an arch surface and align the panoramic view.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This retrospective study analyzed anonymized cone-beam CT (CBCT) scans from 312 patients (mean age 40 years; range 10-78; 41.3% male, 58.7% female). A 3D U-Net deep learning model segmented the jaw and dentition, facilitating panoramic view generation. During preprocessing, CBCT scans were binarized, and a cylindrical reconstruction method aligned the arch along a straight coordinate system, reducing data size for efficient processing. The 3D U-Net segmented the jaw and dentition in two steps, after which the panoramic view was reconstructed using 3D spline curves fitted to the arch, defining the optimal 3D reconstruction zone. This ensured the panoramic view captured essential anatomical details with high contrast and clarity. To evaluate performance, we compared contrast between tooth roots and alveolar bone and assessed intersection over union (IoU) values for tooth shapes and periapical lesions (#42, #44, #46) relative to the conventional method, demonstrating enhanced clarity and improved visualization of critical dental structures.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The proposed method outperformed the conventional approach, showing significant improvements in the contrast between tooth roots and alveolar bone, particularly for tooth #42. It also demonstrated higher IoU values in tooth morphology comparisons, indicating superior shape alignment. Additionally, when evaluating periapical lesions, our method achieved higher performance with thinner layers, resulting in several statistically significant outcomes. Specifically, average pixel values within lesions were higher for certain layer thicknesses, demonstrating enhanced visibility of lesion boundaries and better visualization.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The fully automated AI-based panoramic view generation method successfully created a 3D reconstruction zone centered on the teeth, enabling consistent observation of dental and surrounding tissue structures with high contrast across reconstruction widths. By accurately segmenting the dental arch and defining the optimal reconstruction zone, this method shows significant advantages in detecting pathological changes, potentially reducing clinician fatigue during interpretation while enhancing clinical decision-making accuracy. Future research will focus on further developing and testing this approach to ensure robust performance across diverse patient cases with varied dental and maxillofacial structures, thereby increasing the model's utility ","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001872","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":"Automated tooth segmentation in magnetic resonance scans using deep learning - A pilot study.","authors":"Tabea Flügge, Shankeeth Vinayahalingam, Niels van Nistelrooij, Stefanie Kellner, Tong Xi, Bram van Ginneken, Stefaan Bergé, Max Heiland, Florian Kernen, Ute Ludwig, Kento Odaka","doi":"10.1093/dmfr/twae059","DOIUrl":"10.1093/dmfr/twae059","url":null,"abstract":"<p><strong>Objectives: </strong>The main objective was to develop and evaluate an artificial intelligence model for tooth segmentation in magnetic resonance (MR) scans.</p><p><strong>Methods: </strong>MR scans of 20 patients performed with a commercial 64-channel head coil with a T1-weighted 3D-SPACE (Sampling Perfection with Application Optimized Contrasts using different flip angle Evolution) sequence were included. Sixteen datasets were used for model training and 4 for accuracy evaluation. Two clinicians segmented and annotated the teeth in each dataset. A segmentation model was trained using the nnU-Net framework. The manual reference tooth segmentation and the inferred tooth segmentation were superimposed and compared by computing precision, sensitivity, and Dice-Sørensen coefficient. Surface meshes were extracted from the segmentations, and the distances between points on each mesh and their closest counterparts on the other mesh were computed, of which the mean (average symmetric surface distance) and 95th percentile (Hausdorff distance 95%, HD95) were reported.</p><p><strong>Results: </strong>The model achieved an overall precision of 0.867, a sensitivity of 0.926, a Dice-Sørensen coefficient of 0.895, and a 95% Hausdorff distance of 0.91 mm. The model predictions were less accurate for datasets containing dental restorations due to image artefacts.</p><p><strong>Conclusions: </strong>The current study developed an automated method for tooth segmentation in MR scans with moderate to high effectiveness for scans with respectively without artefacts.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"12-18"},"PeriodicalIF":2.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11664100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142727040","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":"The impact of cone beam CT on outcomes associated with endodontic access cavity preparation: a controlled human analogue study using 3D-printed first maxillary molars.","authors":"Margarete B McGuigan, Henry F Duncan, Gabriel Krastl, Julia Ludwig, Bahman Honari, Keith Horner","doi":"10.1093/dmfr/twae048","DOIUrl":"10.1093/dmfr/twae048","url":null,"abstract":"<p><strong>Objectives: </strong>To identify if supplemental preoperative cone beam CT (CBCT) imaging could improve outcomes related to endodontic access cavity preparation, using 3D-printed maxillary first molars (M1Ms) in a rigorously simulated, controlled human analogue study.</p><p><strong>Methods: </strong>Eighteen operators with 3 experience-levels took part in 2 simulated clinical sessions, 1 with and 1 without the availability of CBCT imaging, in a randomized order and with an intervening 8-week washout period. Operators attempted the location of all 4 root canals in each of 3 custom-made M1Ms (2 non-complex and 1 complex mesiobuccal [MB] canal anatomy). The primary outcome was tooth volume removed. Secondary outcomes were linear cavity dimensions, canals located, and procedural time. Operator confidence and \"helpfulness\" of available imaging were recorded. Statistical analysis of data included: paired t-tests, Fisher's exact test, linear mixed-effect modelling, and Mann-Whitney U test, with an alpha level of .05 for all.</p><p><strong>Results: </strong>When supplemental preoperative CBCT was available, there were significant reductions in volume of the access cavity and procedural times, with significantly increased MB2 canal location, but only for teeth with non-complex anatomies and for more experienced operators. Linear mixed-effect modelling identified image type and operator experience as significant predictors of tooth volume removed and procedural time. There was significantly lower confidence in canal location and perceived \"helpfulness\" (all Experience Groups) when conventional imaging only was used compared with when CBCT was available.</p><p><strong>Conclusions: </strong>Supplemental preoperative CBCT had several beneficial impacts on access cavity preparation, although this only applied to teeth with non-complex anatomy and for more experienced operators.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"43-55"},"PeriodicalIF":2.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11664103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142343506","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":"Carotid calcifications in panoramic radiographs can predict vascular risk.","authors":"Maria Garoff, Jan Ahlqvist, Eva Levring Jäghagen, Per Wester, Elias Johansson","doi":"10.1093/dmfr/twae057","DOIUrl":"10.1093/dmfr/twae057","url":null,"abstract":"<p><strong>Objectives: </strong>Carotid artery calcification (CAC) is occasionally detected in panoramic radiographs (PRs). Bilateral vessel-outlining (BVO) CACs are independent risk markers for future vascular events and have been associated with large plaque area. If accounting for plaque area, BVO CACs may no longer be an independent risk marker for vascular events. The aim of this study was to explore the association between BVO CACs and vascular events and its relationship with carotid ultrasound plaque area.</p><p><strong>Methods: </strong>In this cohort study we prospectively included 212 consecutive participants with CACs detected in PR that were performed to plan and evaluate odontologic treatment. Of these 212, 43 (20%) had BVO CACs. Plaque area was assessed with ultrasound at baseline. Primary outcome was major adverse cardiovascular events (MACEs) during follow-up.</p><p><strong>Results: </strong>Vessel-outlining CAC was associated with larger plaque area on the same side (P = .03) and BVO CACs were associated with larger total plaque area (both sides summed) than other CAC features (P = .004). Mean follow-up was 7.0 years and 72 (34%) participants had more than 1 MACE. In bivariable analyses, both BVO CACs (HR 2.5, P < .001) and total plaque area (HR 1.8 per cm2, P = .008) were associated with MACE. When entering BVO CACs, plaque area and other relevant co-variates in a multivariable model, BVO CACs were virtually unchanged (HR 2.4, P = .001), but total plaque area was no longer significant (HR 1.0, P = .92).</p><p><strong>Conclusion: </strong>Present results support the contention that BVO CACs are a stronger predictor for future vascular events than carotid ultrasound plaque area.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"28-34"},"PeriodicalIF":2.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11664099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142681094","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":"Temporomandibular joint assessment in MRI images using artificial intelligence tools: where are we now? A systematic review.","authors":"Mitul Manek, Ibraheem Maita, Diego Filipe Bezerra Silva, Daniela Pita de Melo, Paul W Major, Jacob L Jaremko, Fabiana T Almeida","doi":"10.1093/dmfr/twae055","DOIUrl":"10.1093/dmfr/twae055","url":null,"abstract":"<p><strong>Objectives: </strong>To summarize the current evidence on the performance of artificial intelligence (AI) algorithms for the temporomandibular joint (TMJ) disc assessment and TMJ internal derangement diagnosis in magnetic resonance imaging (MRI) images.</p><p><strong>Methods: </strong>Studies were gathered by searching 5 electronic databases and partial grey literature up to May 27, 2024. Studies in humans using AI algorithms to detect or diagnose internal derangements in MRI images were included. The methodological quality of the studies was evaluated using the Quality Assessment Tool for Diagnostic of Accuracy Studies-2 (QUADAS-2) and a proposed checklist for dental AI studies.</p><p><strong>Results: </strong>Thirteen studies were included in this systematic review. Most of the studies assessed disc position. One study assessed disc perforation. A high heterogeneity related to the patient selection domain was found between the studies. The studies used a variety of AI approaches and performance metrics with CNN-based models being the most used. A high performance of AI models compared to humans was reported with accuracy ranging from 70% to 99%.</p><p><strong>Conclusions: </strong>The integration of AI, particularly deep learning, in TMJ MRI, shows promising results as a diagnostic-assistance tool to segment TMJ structures and classify disc position. Further studies exploring more diverse and multicentre data will improve the validity and generalizability of the models before being implemented in clinical practice.</p>","PeriodicalId":11261,"journal":{"name":"Dento maxillo facial radiology","volume":" ","pages":"1-11"},"PeriodicalIF":2.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800278/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142674990","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}