Dento maxillo facial radiology最新文献

Objectives: To identify landmarks in ultrasound periodontal images and automate the image-based measurements of gingival recession (iGR), gingival height (iGH), and alveolar bone level (iABL) using machine learning.

Methods: We imaged 184 teeth from 29 human subjects. The dataset included 1580 frames for training and validating the U-Net CNN machine learning model, and 250 frames from new teeth that were not used in training for testing the generalization performance. The predicted landmarks including the tooth, gingiva, bone, gingival margin (GM), cementoenamel junction (CEJ), and alveolar bone crest (ABC), were compared to manual annotations. We further demonstrated automated measurements of the clinical metrics iGR, iGH, and iABL.

Results: Over 98% of predicted GM, CEJ, and ABC distances are within 200 µm of the manual annotation. Bland-Altman analysis revealed biases (bias of machine learning versus ground truth) of -0.1 µm, -37.6 µm, and -40.9 µm, with 95% limits of agreement of [-281.3, 281.0] µm, [-203.1, 127.9] µm, and [-297.6, 215.8] µm for iGR, iGH, and iABL, respectively, when compared to manual annotations. On the test dataset, the biases were 167.5 µm, 40.1 µm, and 78.7 µm with 95% CIs of [-1175, 1510] µm, [-910.3, 990.4] µm, and [-1954, 1796] µm for iGR, iGH, and iABL, respectively.

Conclusions: The proposed machine learning model demonstrates robust prediction performance, with the potential to enhance the efficiency of clinical periodontal diagnosis by automating landmark identification and clinical metrics measurements.

Objectives: The main objective was to develop and evaluate an artificial intelligence model for tooth segmentation in magnetic resonance (MR) scans.

Methods: 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.

Results: 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.

Conclusions: The current study developed an automated method for tooth segmentation in MR scans with moderate to high effectiveness for scans with respectively without artefacts.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Objectives: 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.

Methods: 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.

Results: 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).

Conclusion: Present results support the contention that BVO CACs are a stronger predictor for future vascular events than carotid ultrasound plaque area.

Aim: 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).

Material and methods: 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 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).

Results: 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 0.28-0.64, and 0.32-0.57, respectively.

Conclusion: 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.

Objectives: 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.

Methods: Five databases (Medline, Embase, Scopus, Web of Science, and Cochran'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.

Results: Thirty articles were included in the review, where ten 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, e.g.: sensitivity 87% (95% CI: 80% to 93%), specificity 76% (95% CI: 69% to 81%), and accuracy 84% (95% CI: 75% to 91%).

Conclusion: 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.

Title: The Influence of a Deep Learning Tool on the Performance of Oral and Maxillofacial Radiologists in the Detection of Apical Radiolucencies.

Objectives: This study aimed to assess the impact of a deep learning model on oral radiologists' ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effects of years of experience, time to diagnose, and specialty.

Methods: This study used an annotated dataset and a beta-version of a deep learning model (Denti.AI). The testing subset comprised 68 intraoral periapical radiographs confirmed with cone-beam computed tomography for presence/absence of apical radiolucencies. Four oral radiologists participated in a crossover reading scenario, analyzing the radiographs under two conditions: initially without AI assistance and later with AI predictions. The study evaluated reader performance using AFROC-AUC, sensitivity, specificity, and ROC-AUC per case. It also assessed sensitivity per lesion. Regression analysis investigated how experience, time spent on images, and specialty influenced reader performance.

Results: No statistically significant differences were found in AFROC-AUC, sensitivity, specificity, and ROC-AUC. Regression analysis identified factors influencing diagnostic outcomes: unaided reading significantly prolonged diagnostic time (Beta = 12, 95% CI [11, 13], p < 0.001), while radiologists' professional status was positively associated with diagnostic accuracy (Beta = 0.02, 95% CI [0.00, 0.04], p = 0.015). These findings underscore the impact of AI on diagnostic efficiency and the critical role of radiologists' experience in diagnostic accuracy.

Conclusion: AI did not significantly enhance radiologists' overall diagnostic accuracy. However, it showed potential to enhance efficiency, particularly advantageous for non-expert clinicians. The expertise of radiologists remains vital for accuracy, underscoring the complementary role of AI in dental diagnostics.

Advances in knowledge: AI algorithms may have more notable effects on radiologists' workflow than on the accuracy of detecting apical radiolucencies.

Objectives: Cysts in jaws may have similar radiographic features. However, it is important to clarify the diagnosis prior to surgery. The aim of this study was to compare the radiomic features of radicular cysts (RCs), dentigerous cysts (DCs) and odontogenic keratocysts (OKCs) as a non-invasive diagnostic alternative to biopsy.

Methods: In total, 161 odontogenic cysts diagnosed histopathologically (55 RCs, 53 DCs and 53 OKCs) were included in the present study. Each cyst was semi-automatically segmented on CBCT images, and radiomic features were extracted by an observer. A second observer repeated 20% of the evaluations and the radiomic features. Those achieving an inter-observer agreement level above 0.850 were included in the study. Consequently, 5 shape-based and 22 textural features were investigated in the study. Statistical analysis was performed comparing both three cyst features and making pairwise comparisons.

Results: All features included in the study showed statistical differences between cysts, with the exception of one textural feature (NGTDM coarseness) (p < 0.05). However, only one shape-based feature (shericity) and one textural feature (GLSZM large area emphasis) were statistically different in pairwise comparisons of all three cysts (p < 0.05).

Conclusion: Radiomics features of the RCs, DCs and OKCs showed significant differences, and may have the potential to be used as a non-invasive method in the differential diagnosis of cysts.

Objective: To develop an accurate method for converting dose-area product (DAP) to patient dose for dental cone-beam computed tomography (CBCT) using deep learning.

Methods: 24,384 CBCT exposures of an adult phantom were simulated with PCXMC 2.0, using permutations of tube voltage, filtration, source-isocenter distance, beam width/height and isocenter position. Equivalent organ doses as well as DAP values were recorded. Next, using the aforementioned scan parameters as inputs, neural networks (NN) were trained using Keras for estimating the equivalent dose per DAP for each organ. Two methods were explored for positional input features: (1) 'Coordinate' mode, which uses the (continuous) XYZ-coordinates of the isocenter, and (2) 'AP/JAW' mode, which uses the (categorical) anteroposterior and craniocaudal position. Each network was trained, validated and tested using a 3/1/1 data split. Effective dose (ED) was calculated from the combination of NN outputs using ICRP 103 tissue weighting factors. The performance of the resulting NN models for estimating ED/DAP was compared with that of a multiple linear regression (MLR) model as well as direct conversion coefficients (CC).

Results: The mean absolute error (MAE) for organ dose/DAP on the test data ranged from 0.18% (bone surface) to 2.90% (oesophagus) in 'Coordinate' mode and from 2.74% (red bone-marrow) to 14.13% (brain) in 'AP/JAW' mode. The MAE for ED was 0.23% and 4.30%, respectively, for the two modes, vs. 5.70% for the MLR model and 20.19%-32.67% for the CCs.

Conclusion: NNs allow for an accurate estimation of patient dose based on DAP in dental CBCT.

Objectives: To compare the accuracy, duration, and certainty of diagnosing approximal caries in bitewing radiographs displayed in three monitors under two luminance conditions.

Methods: A total of 39 teeth without evident caries were selected from 11 patients undergoing partial jaw resection. Before the operation, 13 bitewing radiographs were captured by a digital imaging system. Eight observers evaluated the images under the dark (9 lux) and bright (200 lux) conditions, using two medical-grade monitors and a commercial monitor. Using histological results as the gold standard, the areas under the receiver operating characteristic curves under different conditions were compared using the Z-test. Multivariate analysis of variance was conducted to assess the impact of various factors on diagnostic duration, while ordinal logistic regression was used to examine factors influencing diagnostic certainty level. It was considered significant when P＜0.05.

Results: No significant difference was found in the diagnostic accuracy or duration for diagnosis approximal caries under two luminance conditions with the three distinct monitors (P > 0.05). Ambient light, clinical experience and the pathological grade of approximal caries have influence on the degree of diagnostic confidence (P＜0.05).

Conclusions: Different monitors and ambient luminance didn't influence the diagnostic accuracy or evaluation duration. Ambient luminance, clinical experience, and the depth of caries affect the degree of diagnostic confidence.

Advances in knowledge: The study employing bitewing radiographs from real patients indicates that ambient luminance, clinical experience, and the depth of caries affect the degree of diagnostic confidence.