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Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. Gates Foundation.
Enteric infectious diseases claim more than 1 million lives annually and are among the top ten causes of death in children younger than 5 years. Remarkable global investment has been dedicated to enteric infectious disease prevention and control; however, the shifting global health landscape is testing the continuance of progress. To evaluate the current status and guide future interventions, we present the latest epidemiological estimates of enteric infectious diseases from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 and assess progress towards the Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea (GAPPD) mortality target of fewer than 20 deaths per 100 000 children younger than 5 years by 2025. We quantified the incidence, mortality, and disability-adjusted life-years (DALYs) of enteric infectious diseases by age, sex, and year across 204 countries and territories from 1990 to 2023. In GBD 2023, the following were considered under the category of enteric infectious diseases: diarrhoeal diseases, enteric fever (typhoid and paratyphoid), invasive non-typhoidal Salmonella spp (iNTS) infections, and other intestinal infectious diseases. We also examined 15 aetiologies contributing to diarrhoeal diseases. Incidence and prevalence were estimated with DisMod-MR (version 2.1), a Bayesian meta-regression tool, drawing on data from systematic reviews, population-based surveys, claims data, and hospital sources. Cause-specific mortality was modelled with Cause of Death Ensemble Modelling based on data from sources including vital registration, mortality surveillance, verbal autopsy, and minimally invasive tissue sampling. Years of life lost and years lived with disability were computed and combined to derive DALYs. For aetiology-specific estimation, population-attributable fractions (PAFs) for 15 pathogens were derived with a counterfactual framework. Point estimates and 95% uncertainty intervals (UIs) were generated from 250 draws from the posterior distribution. In 2023, enteric infectious diseases resulted in an estimated 1·27 million (95% UI 0·963-1·68) deaths globally, declining from 3·69 million (3·04-4·56) in 1990. The global age-standardised mortality rate (ASMR) decreased from 74·1 (62·0-92·9) per 100 000 population to 16·4 (12·6-21·3) per 100 000 population during the same period. Diarrhoeal diseases accounted for most deaths in 2023 (1·11 million [0·811-1·54]), followed by enteric fever and iNTS. South Asia and sub-Saharan Africa remained the most affected regions in 2023, with 599 000 (441 000-882 000) and 501 000 (373 000-648 000) deaths due to enteric infectious diseases, respectively, predominantly from diarrhoeal disease. Rotavirus was the leading cause of all-age diarrhoeal disease deaths (PAF 16·3% [12·0-21·5]), followed by norovirus (10·2% [2·4-17·0]) and Shigella spp (9·3% [5·4-15·2]). Among children younger than 5 years, PAFs of deaths due to diarrhoeal diseases were 40·2% (32·5-48·5) for rotavirus, 24·0% (15·1-36·7) for Shigella spp, and 23·4% (13·7-34·3) for adenovirus. Across 204 countries and territories, 141 met the GAPPD mortality target in 2023. The driving aetiologies among countries that did not meet the target in 2023 varied slightly by GBD super-region, but the highest or second-highest number of deaths in children younger than 5 years were consistently attributed to rotavirus. Astrovirus and sapovirus, newly included in GBD 2023, were responsible for 24 600 (6290-49 000) and 18 800 (4650-44 400) deaths, respectively, in 2023, mainly in children younger than 5 years. Our findings show that mortality and ASMRs of enteric infectious diseases declined substantially between 1990 and 2023. This decline is consistent with the expansion of public health measures and broader socioeconomic development. However, the burden in 2023 remains considerably high, with the highest mortality concentrated in sub-Saharan Africa and south Asia. Considering that more than a quarter of all countries had yet to meet the GAPPD mortality target in 2023, sustained efforts are needed to address the persistent burden in affected countries and to adapt to the changing global health landscape. Gates Foundation.
The accuracy of intraoral scanning is crucial for the long-term survival of indirect restorations. It remains unclear if preparation protocols, surface characteristics, and humidity play a role in the quality of an intraoral scan. The purpose of this in vitro study was to assess the impact of margin preparation on both dentin and enamel using different finishing protocols. These assessments were conducted on specimens stored in both dry and wet environments, with a specific focus on evaluating their influence on the accuracy of intraoral scans. Six maxillary canines were prepared using three different sequences. In the first sequence, a coarse diamond rotary instrument (DC) was used. In the second, DC was followed by a fine grit diamond rotary instrument (DCDF). In the third, the DC was followed by a tungsten carbide rotary instrument. Margins were scanned 10 times using an intraoral scanner (TRIOS 3; 3Shape A/S) compared to a laboratory scanner (D2000; 3Shape A/S). Accuracy was determined by measuring precision and trueness (Geomagic Control; Hexagon). To assess the impact of 3-dimensional (3D) topography on accuracy, roughness was measured using a laser microscope (Lext OLS 4000; Olympus). The data were analyzed using the Levene test, followed by a 3-way ANOVA with Bonferroni post hoc testing to assess the effects and interactions of surface finish, environment, and tissue on the outcomes (α=.05). Improved precision was observed in enamel (15.7 µm) compared to dentin (24.3 µm) specimens (P<.001). Furthermore, the environment had a significant impact on accuracy (P<.001). Higher precision and trueness (P<.001) were observed for dentin in dry (18.3 µm) compared to wet (37.3 µm) conditions. The use of fine grit diamond rotary instruments improved precision and trueness for dry dentin (DC/DCDF P=.017; DC/DCCF P=.005; DCDF/DCCF P>.999), while no difference was detected for dry enamel (DC/DCDF P=.966; DC/DCCF P=.822; DCDF/DCCF P=.519). The use of a second finishing rotary instrument decreased Sa (µm) for dentin (DC/DCDF/DCCF) (7.11/5.63/4.53), whereas enamel values were similar (7.94/8.53/9.97). Surface roughness, tooth substrate, and scanning environment (or humidity conditions) were found to influence the accuracy of intraoral scans.
Alam M K, Alftaikhah S A A, Issrani R et al. Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: a systematic review and meta-analysis of in-vitro studies. Heliyon 2024; https://doi.org/10.1016/j.heliyon.2024.e24221 . A systematic review of in vitro studies utilising artificial intelligence (AI) in dental imaging. Searches were carried out across multiple databases: CINAHL, Cochrane Library, Embase, Google Scholar, IEEE Xplore, PubMed/MEDLINE, Scopus, and Web of Science as well as hand-searching references from eligible articles. Studies were eligible if i) classed as in vitro, defined as simulations or laboratory tests outside a living organism, ii) studied the performance of AI techniques and iii) involved analysis of dental imaging. Studies not in English or with insufficient data were excluded. An adapted version of the CONSORT bias tool was used for the assessment of studies. Outcome measures were extracted including: odds ratios, true positive rate, true negative rate, positive predictive value, and negative predictive value. A meta-analysis using a fixed-effects model assessed accuracy with a 95% confidence interval. Heterogeneity and overall effect tests were applied to evaluate the reliability of the meta-analysis. After screening, nine studies were identified, eight of which focused on Cone Beam Computed Tomography (CBCT) imaging. Endpoints included caries detection, segmentation tasks and virtual 3D model creation. Across the nine studies, and when pooled in meta-analysis, AI performance was shown to be superior to reference standards. This systematic review of in-vitro studies highlights the potential of AI to improve the speed or quality of dental imaging tasks. However clinical studies are required to ensure evidence from laboratory studies can translate into clinical practice.
The success of root canal treatment largely depends on the canal shaping procedure, which influences subsequent steps, such as cleaning, obturation, and sealing. The present study aimed to evaluate the canal-centering ability, dentin removal, and dentinal crack incidence following root canal preparation with different rotary and reciprocating nickel-titanium file systems using cone beam computed tomography (CBCT) and a stereomicroscope. Forty freshly extracted single-rooted human mandibular premolars were selected and scanned preoperatively by CBCT (KAVO OP 3D Pro, Germany) to evaluate canal curvature. The samples were then randomly distributed into 4 groups (n = 10): Group 1 - ProTaper Next (continuous rotary), Group 2 - MicroMega One RECI (reciprocating), Group 3 - Race Evo (continuous rotary), and Group 4 - R-Motion (reciprocating). Root canals were prepared in accordance with the respective manufacturer's recommendations. Post-instrumentation CBCT scans were obtained to assess canal centering and remaining dentin thickness at 3, 6, and 9 mm from the apex. Root sections were also examined under a stereomicroscope to assess dentinal cracks. The MicroMega One RECI (Group 2) demonstrated superior canal centering, with mean ratios of 0.20 ± 0.05, 0.18 ± 0.04, and 0.17 ± 0.03 in the apical, middle, and coronal thirds, respectively. In contrast, ProTaper Next (Group 1) showed the highest centering ratio (0.35 ± 0.08 at 3 mm), indicating greater deviation from the canal axis. Reciprocating systems exhibited better dentin preservation and fewer dentinal cracks compared with continuous rotary systems. Based on observations from this in vitro study, it can be concluded that the variations in design of file systems and motion patterns critically determine the efficiency and safety of root canal shaping. Reciprocating and rotary motions show different effects on canal centering, dentin removal, crack propagation, and the preservation of root integrity.
Vertical root fractures (VRFs) remain a major diagnostic challenge in endodontics due to image noise and artifacts in cone-beam computed tomography (CBCT), particularly those caused by intracanal posts. This study evaluated the effect of an advanced noise reduction (ANR) algorithm on CBCT performance in detecting VRFs in maxillary second premolars and examined how different post materials influenced diagnostic outcomes. Seventy extracted maxillary second premolars with single canals were divided into 5 groups according to post material (cast, fiberglass, titanium, stainless steel, and brass). VRFs were induced in half of the specimens using a universal testing machine under standardized conditions. CBCT scans were obtained using a Carestream 9600 device with fixed parameters, both with and without ANR. Two experienced radiologists independently evaluated the images using a 5-point scale. Sensitivity, specificity, predictive values, and interobserver agreement were analyzed using SPSS version 21 (IBM Corp., Armonk, NY, USA). The application of ANR increased overall sensitivity and interobserver agreement compared with conventional images. Specificity varied by post material: fiberglass posts demonstrated the highest diagnostic accuracy, while stainless steel and brass produced stronger artifacts and lower sensitivity. Incorporating ANR into CBCT imaging improves VRF detection by improving sensitivity and observer consistency, especially in cases with minimal metallic interference. These findings highlight the clinical benefits of ANR and support further research integrating noise and metal artifact reduction techniques with artificial intelligence to optimize diagnostic precision.
The choice of endodontic sealer plays a crucial role in preventing microleakage, maintaining apical integrity, and ensuring durable clinical performance. Among available materials, bioceramic sealers offer bioactivity and chemical bonding potential, whereas epoxy resin-based sealers provide proven stability and low solubility. This study aimed to compare the physicochemical properties of four bioceramic root canal sealers with an epoxy resin-based reference material (AH Plus), focusing on setting time, solubility (with and without thermal cycling), and dimensional change. Five sealers were tested: EndoSeal MTA, CeraSeal, Nano-MTA, NeoSealer Flo (bioceramic), and AH Plus (epoxy resin-based). Setting time was determined according to ISO 6876:2012 using Gilmore needles. Solubility was evaluated under thermocycled (1000 cycles, 5-55 °C) and non-thermocycled conditions. Dimensional change was assessed via pre- and post-immersion micro-CT imaging after 7 days in distilled water. EndoSeal MTA had the shortest setting time, and NeoSealer Flo the longest (p < 0.0001). AH Plus showed the lowest solubility; NeoSealer Flo had the highest. Thermal cycling did not significantly affect solubility (p > 0.05), but a strong correlation was found between thermocycled and non-thermocycled values (r² = 0.8084, p < 0.0001). AH Plus demonstrated significant volumetric increase, while bioceramic sealers showed varying degrees of shrinkage (p < 0.0001). Bioceramic sealers demonstrated comparable or superior solubility and adaptation compared with the epoxy resin-based control, although differences in setting time and dimensional change were material-dependent. These findings suggest that selecting sealers according to their physicochemical performance, such as faster setting in single-visit treatments or higher dimensional change in retreatments, may improve the long-term prognosis and clinical success of root canal therapy. This study identifies material-specific strengths and weaknesses that can guide evidence-based selection. Matching the sealer's physicochemical profile to the clinical scenario can optimize treatment longevity and reduce failure risk.
Lateral cephalograms are conventionally used for landmark identification and cephalometric analysis, but expose patients to ionizing radiation. Magnetic resonance imaging (MRI) is increasingly employed to evaluate various tissue types, with dental-dedicated MRI (ddMRI) emerging as a promising modality in clinical dentistry. High-quality MRI could offer a radiation-free alternative for orthodontic diagnosis and treatment planning. This pilot study aimed to evaluate the reliability of ddMRI for 2-dimensional (2D) orthodontic cephalometric landmark identification and annotation. Thirteen volunteers (7 men, 6 women; mean age 33±5.2 years) underwent ddMRI. Three independent raters identified 2D cephalometric landmarks and annotated their positions on the same ddMRI datasets twice. The 3D Slicer application was used to identify key orthodontic cephalometric landmarks on multiplanar reconstruction images in the sagittal plane. Intra-rater and inter-rater reproducibility were assessed using intraclass correlation coefficients (ICCs), and reliability was evaluated by nominal differences in linear distance (mm) between annotated points. Intra-rater ICCs ranged from 0.909 to 0.999, and inter-rater ICCs ranged from 0.988 to 0.999, indicating excellent intra- and inter-rater reliability. Bland-Altman plots were used to display mean differences (mm) and limits of agreement (±1.96 standard deviations) between annotations to assess potential biases. ddMRI is a feasible imaging modality for 2D orthodontic landmark identification. Based on intra- and inter-rater reproducibility values, ddMRI demonstrates high reliability in identifying key orthodontic cephalometric landmarks, providing a radiation-free alternative for orthodontic diagnosis and treatment planning.
Different reference landmarks can be used to guide the scan registration of an implant scanning workflow, including fixation screws. However, the effect of arch distribution on registration accuracy remains unknown. The purpose of this in vitro study was to evaluate the effect of the number and spatial distribution of fixation screws on the accuracy (trueness and precision) of the registration between tooth and tissue scans in a mandibular implant scanning workflow. A mandibular typodont was obtained. Six reference markers (2 on the buccal and 4 on the lingual) were placed to facilitate posterior measurements. Three fixation screws were placed: 1 in the anterior symphysis and 1 on each of the retromolar pads. A laboratory scan was recorded (control file). Thirty scans with the typodont teeth were obtained, including the 6 markers and 3 fixation screws, by using an intraoral scanner (IOS) (Aoralscan Elite). The typodont teeth were then removed, and a layer of putty polyvinyl siloxane was applied only over the edentulous areas. Then, 30 tissue scans (without the teeth) were obtained with the 6 markers and 3 fixation screws using the same IOS. Three groups were created based on the fixation screws used to register the tooth and tissue scans: 1 anterior (ANT group), 2 posterior (POST group), and 3 with tripod distribution (TRIPOD group). In the ANT group, each experimental tooth scan was modified by trimming the 2 posterior fixation screws. Then, each tooth and tissue pair of scans was aligned with the best fit algorithm using the anterior fixation screw as the common information. In the POST group, each experimental tooth scan was modified by trimming the anterior fixation screw. Then, each tooth and tissue pair of scans was aligned using the posterior fixation screws as the common information. In the TRIPOD group, each pair of tooth and tissue scans was aligned using the 3 fixation screws as the common information. In the control and each pair of aligned experimental scans, linear measurements were calculated between the buccal markers of the tooth scan and the anterior and posterior lingual markers of the tissue scan. The measurements obtained in the control scan were used to calculate registration discrepancies with each specimen. One-way ANOVA and Tukey tests were used to analyze trueness. The Levene test was used to analyze precision (α=.05). Significant anterior (P<.001) and posterior (P<.001) trueness discrepancies were found. The TRIPOD group obtained significantly better anterior trueness than the ANT and POST groups. The POST and TRIPOD groups obtained better posterior trueness than the ANT group. The Levene test revealed significant posterior precision discrepancies (P<.001). The posterior precision in the ANT group was significantly worse than in the POST and TRIPOD groups. Three fixation screws with a tripod spatial distribution obtained better accuracy than 1 fixation screw in the anterior or 2 fixation screws in the posterior areas of the arch.
This ex vivo study was performed to determine the optimal energy level for virtual monoenergetic images (VMIs) generated with photon-counting detector computed tomography (PCD-CT) to minimize metal artifacts from dental implants. Twelve implants from various manufacturers were placed in 6 pig mandibles and scanned with PCD-CT. VMIs were reconstructed at energy levels from 70 keV to 150 keV in 20-keV increments. Three readers with varying experience qualitatively assessed the image quality, artifact burden, and diagnostic interpretability of peri-implant soft and hard tissues using a 5-point discrete visual scale. Objective analyses included quantitative line profile analysis of implant-induced artifacts. Descriptive statistics were calculated, and inter-reader agreement was assessed using percentage agreement and the Krippendorff alpha coefficient. Qualitative analysis demonstrated excellent image quality for VMIs at ≥110 keV (median=5), with minimal artifacts observed at 130-150 keV. In contrast, lower-energy VMIs (70-90 keV) showed inferior performance due to artifact-related limitations in diagnostic interpretability. Inter-reader agreement ranged from moderate to perfect, with perfect reliability (α=1) for VMIs ≥110 keV. Quantitative line-profile analysis confirmed reduced artifact burden at higher energy levels, particularly for VMIs ≥110 keV. VMI at energy levels ≥110 keV on PCD-CT reduced dental implant-related metal artifacts and offered excellent image quality, including assessment of both peri-implant soft and hard tissues. These findings suggest that optimized PCD-CT VMI may enhance postoperative follow-up imaging. Future in vivo studies are warranted to validate these findings in clinical practice.
This study evaluated the performance of the YOLOv8m-seg model in detecting and delineating interproximal caries and cervical burnout on bitewing radiographs and examined whether increasing the number of training epochs improved segmentation accuracy and consistency. In total, 1,410 bitewing radiographs were annotated using polygon-based masks by a trained dental clinician. The YOLOv8m-seg model was trained for 50, 100, and 150 epochs on 1,128 images and validated on 282 images using the Ultralytics segmentation framework. Model performance was assessed using precision, recall, and mean average precision at intersection-over-union thresholds of 0.5 and 0.5 to 0.95 (mAP0.5, mAP0.5-0.95) for both bounding box and mask outputs. Additional evaluation was conducted on a non-augmented validation subset. Extended training duration was associated with improved segmentation performance. The highest mask mAP0.5-0.95 value was 0.828 at epoch 150. Both box-based precision and recall increased with longer training, whereas mask-based evaluation more accurately reflected the model's ability to delineate the boundaries of caries and cervical burnout. Performance appeared consistent across both classes in the augmented validation split but was reduced in the non-augmented validation subset. The YOLOv8m-seg model demonstrated high diagnostic accuracy in distinguishing proximal caries from cervical burnout on bitewing radiographs. Its mask-based outputs may assist clinicians in early lesion recognition and support improved diagnostic decision-making. Future studies should evaluate model generalizability across broader populations and diverse clinical environments and should prioritize assessment using non-augmented validation sets and independent test datasets.
The longevity of implant-supported prostheses is influenced by factors that include the distribution of stress and strains within the prosthetic components and surrounding bone. However, a consensus regarding the optimal combination of abutment and crown materials for implant-supported restorations is lacking. This in vitro study evaluated, through finite element analysis (FEA), the stress distribution within prosthetic components and at the bone-implant interface resulting from different combinations of esthetic abutment and crown materials. A 3-dimensional (3D) FEA model of an implant-supported maxillary central incisor was constructed with segmented computed tomography data of an edentulous premaxilla processed in the Mimics and 3-Matic software programs. The implant and components were integrated with 5 materials of varying elastic moduli (E) to create 6 abutment and crown combinations: polyetheretherketone (PEEK) and lithium disilicate, PEEK and composite resin, resin-modified ceramic and lithium disilicate, resin-modified ceramic and resin-modified ceramic, PEEK and resin-modified ceramic, and zirconia and lithium disilicate. A static axial loading of 500 N was applied, principal stresses, and von Mises (mvM) stresses were analyzed at the bone-implant interface, luting agent, and prosthetic components. All models exhibited similar mvM stress distribution patterns at the bone-implant interface. Abutments with higher E values (zirconia, resin-modified ceramic) generated lower mvM stresses in the crown and luting agent compared with lower E materials (PEEK). The PEEK and composite resin model showed the highest mvM stresses (180.7 MPa in the crown; 1542 MPa in luting agent), while zirconia and lithium disilicate exhibited the lowest (107.5 MPa and 146.6 MPa, respectively). Abutments with higher elastic moduli demonstrated more favorable stress distribution within the crown and luting agent than those with lower modulus values.
This study proposes an ex vivo imaging protocol using a dental-dedicated magnetic resonance imaging (ddMRI) system and qualitatively and quantitatively evaluates the effects of phosphate-buffered saline (PBS) washing on image quality. Four half-maxillae and 4 half-mandibles from human cadaveric donors were scanned using a ddMRI system with a dental-specific coil. Two pulse sequences ("anatomy" and "inflammation") were applied. Each specimen was imaged at 4 time points: immediately after PBS immersion and after 24, 48, and 72 hours of washing, totaling 64 images. Image quality was evaluated using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and conspicuity of anatomical structures: cortical bone, medullary bone, root contour, soft tissue, and the sinus floor/mandibular canal. Conspicuity was rated by 3 observers and analyzed using the Cochran Q test (α=0.05). Cortical bone, medullary bone, and the sinus floor were depicted in all images; root contours (91.1%) and soft tissue (85.9%) were visible in most images. PBS washing did not significantly impact conspicuity (P>0.05). Signal intensity was higher in "anatomy" than "inflammation." In the "anatomy" sequence, both SNR and CNR initially declined, then stabilized from 24 hours onward. CNR between medullary bone and soft tissue showed the greatest improvement with extended PBS washing, especially for the "inflammation" sequence. The proposed ddMRI protocol enabled consistent visualization of dentomaxillofacial structures in ex vivo samples. PBS represented a suitable carrier. Although PBS washing did not influence conspicuity, at least 24 hours of washing may improve image quality, as reflected by SNR and CNR.
Gap percentage at the restoration-tooth interface and internal voids remain significant limitations in resin-based composite restorations, contributing to marginal leakage, secondary caries, and restoration failure. Preheating composite resins has been proposed as a strategy to enhance adaptation and reduce polymerization-induced stress. To evaluate the influence of preheating resin composites and varying light-curing durations on gap percentage at the restoration-tooth interface, internal adaptation, and void formation in Class II restorations using high-resolution Nano-CT imaging. Fifteen human maxillary premolars were prepared with standardized Class II cavities and allocated into three groups (n = 5). Group 1 received room-temperature composite cured for 20 s. Groups 2 and 3 received composite preheated to 68 °C using the Compex HD warmer, cured for 20 and 5 s, respectively. All restorations were bulk-filled. Internal adaptation (gap percentage) and voids were assessed using a Bruker Skyscan 2214 Nano-CT, followed by quantitative analysis with ImageJ and CTAnalyser software. Statistical comparisons were performed using one-way ANOVA with Tukey's post hoc test. Preheated composites (Groups 2 and 3) demonstrated significantly reduced gap percentages compared to room-temperature controls (p < 0.0001). Group 3 exhibited the lowest gap percentages and void volume, even with a 5 s curing protocol. No significant differences were observed in void frequency between groups. Preheating resin composites enhances internal adaptation and reduces interfacial gap percentage and void volume, even with reduced curing time. This technique offers a promising, efficient approach for improving posterior composite restoration outcomes.
This study aimed to establish an expert consensus on a set of principles for radiation protection in oral and maxillofacial radiology in Korea. Although national and international guidelines exist, their practical application to dental radiology remains limited, with key clinical components not subject to mandatory enforcement. Therefore, guidelines tailored specifically to dental radiology are necessary to ensure consistent and effective radiation safety. A modified Delphi method was utilized, involving 20 experts-7 specialists in oral and maxillofacial radiology and 13 in medical radiology. A Guideline Development Committee initially drafted the principles, which were refined over 3 rounds of email-based surveys. Panelists evaluated each principle using a 9-point Likert scale, with quantitative scores and qualitative feedback informing the revision process. Consensus was reached on 10 principles, addressing radiographic justification, imaging scope limitations, pregnancy considerations, pediatric optimization, portable radiography, radiation dose monitoring and equipment operation. Final agreement scores approached 9.0, with standard deviations ≤0.7, confirming strong expert consensus. The finalized principles constitute a structured, evidence-based guideline aligned with international standards while addressing specific challenges unique to oral and maxillofacial radiology. They offer practical strategies to enhance patient safety and standardize radiographic decision-making. Further research should investigate their clinical implementation and recommend periodic updates to reflect evolving technologies.
This study was conducted to develop and evaluate a deep learning-based super-resolution approach for enhancing the quality of cone-beam computed tomography (CBCT) images in dentomaxillofacial imaging. A deep learning-based super-resolution method using the MIRNet-v2 model was developed to enhance CBCT image quality. The study used a dataset comprising 6,961 anonymized axial slices from 15 CBCT scans. High-resolution images served as ground truth, while low-resolution versions were created through artificial degradation, including downscaling, blurring, and noise addition. The model was evaluated using a 5-fold cross-validation strategy, employing peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) as metrics. Qualitative assessments conducted by 2 experienced radiologists involved criteria such as noise, sharpness, spatial resolution, and diagnostic quality, scored using a CBCT evaluation chart. The model significantly improved degraded CBCT images across all evaluation metrics. Enhanced images demonstrated mean PSNR values exceeding 35 dB and SSIM values over 0.85, with the highest performance achieved for blurred images (PSNR: 43.86±1.61, SSIM: 0.98±0.01). Subjective assessments indicated improvements in diagnostic quality, noise reduction, and spatial resolution, with outputs comparable to the original images in several degradation scenarios. Interobserver reliability was fair (Cohen kappa: 0.335). Notable improvements were observed for noise and artifact reduction in specific degradation groups, suggesting improved diagnostic utility. Deep learning-based super-resolution demonstrates considerable potential for enhancing CBCT image quality, especially in scenarios involving blur and downscaling. These results suggest possible applications in low-dose imaging protocols and improved clinical decision-making.
To quantify the three-dimensional (3D) deviation between planned and achieved maxillary positions after transfer using a surgical wafer under in vitro conditions, and assess clinical acceptability and repeatability for each workflow. A bracketed typodont mounted on a mannequin was used to define three groups-Conventional (alginate impressions/plaster casts/laboratory scanning), Trios (Trios 3 intraoral scanner (IOS)), and Prime (Primescan IOS)-each with 10 technical replicates. For each replicate, virtual surgical planning was performed, a wafer was designed and 3D-printed, and the maxilla was positioned; 3D deviation was quantified as: (1) point-based 3D positional deviation (three landmarks) and (2) matrix-based 3D translational and rotational deviations. Clinical acceptability was defined as the proportion of the 10 replicates per group with 3D deviation within predefined clinical tolerance limits (0.5 mm for positional/translational; 1.0° for rotational). Repeatability was summarized by the repeatability standard deviation (sr) as specified in ISO 5725. The Trios and Prime groups achieved 100% clinical acceptability across all 3D deviations. In the Conventional group, acceptability was 90% for the anterior landmark's positional and translational deviation, with all others achieving 100%. The sr was 0.13-0.20 mm (positional), 0.23 mm (translational), and 0.22° (rotational) for the Conventional group; 0.04-0.13 mm, 0.06 mm, and 0.24° for the Trios group; 0.04-0.10 mm, 0.11 mm, and 0.17° for the Prime group. Under in vitro conditions, the demonstrated clinical acceptability and repeatability provide preliminary evidence supporting the clinical feasibility of a fully digital IOS-based workflow for wafer-mediated maxillary positioning. An IOS-based workflow can be considered clinically feasible for wafer-mediated maxillary positioning without compromising accuracy, as evaluated in terms of clinical acceptability and repeatability, while potentially eliminating impression-taking and cast fabrication steps.
This systematic review evaluated the diagnostic performance of optical coherence tomography (OCT) for the early detection of oral cancer, particularly emphasizing sensitivity, specificity, and overall diagnostic accuracy. A comprehensive literature search was conducted across PubMed, ScienceDirect, and Wiley Online Library covering publications from 2015 to 2025, supplemented by manual hand-searching of relevant references. Studies were selected using the Population, Intervention, Comparison, Outcome, Study Design (PICOS) framework. Eligible studies evaluated the diagnostic accuracy of OCT using histopathology as the reference standard. Risk of bias was assessed using the QUADAS-2 tool, and the review methodology followed PRISMA 2020 and PRISMA-DTA guidelines. The review protocol was registered in PROSPERO (CRD420251112254). Seven studies met the predefined inclusion criteria. OCT demonstrated high diagnostic performance (sensitivity: 81.5%-100%, specificity: 68.8%-100%), with diagnostic accuracy reaching up to 100% in certain settings. Studies that incorporated machine learning approaches, including convolutional neural networks and support vector machines, consistently achieved superior diagnostic performance compared with conventional OCT interpretation alone. Overall methodological quality was generally low, with several studies exhibiting moderate to high risk of bias in specific domains. OCT, particularly when augmented with artificial intelligence, demonstrates high diagnostic accuracy as a non-invasive imaging modality for the early detection of oral cancer. Its capability to identify dysplastic and malignant changes at the microstructural level offers meaningful diagnostic advantages over conventional examination methods. Nevertheless, larger-scale studies employing standardized protocols are required to confirm its clinical utility and support integration into routine oral cancer screening and diagnostic pathways.
This scoping review examined the evidence on the use of supervised deep learning models for the classification of dental implants using radiographic images. A preliminary search was conducted in PubMed, Google Scholar, PROSPERO, JBI Evidence Synthesis, and the Open Science Framework, identifying a small number of relevant records. A comprehensive search was subsequently performed across 7 databases using adapted strategies without filters. Studies were included if they evaluated implant classification using supervised deep learning models applied to panoramic or periapical radiographs. Studies were excluded if they did not involve implant classification, were review articles, involved children, were unavailable in full text, or did not apply artificial intelligence methods. Data extraction was conducted by 2 independent reviewers, with disagreements resolved by a third reviewer. Descriptive statistics were used for data analysis. Of 274 records, 9 studies met the inclusion criteria. Studies published between 2020 and 2024 evaluated deep learning and machine learning approaches for dental implant identification and classification from radiographic images. A range of models was applied, predominantly convolutional neural networks. Dataset sizes ranged from 355 to 156,965 radiographs and included multiple implant brands. Few studies addressed ethical considerations related to recent data protection regulations. This scoping review indicates that most deep learning-based approaches to implant classification using dental radiographs primarily rely on implant brand or manufacturer as the labeling strategy. This reliance may limit model generalizability and long-term applicability due to the frequent discontinuation of implant systems. Future studies should focus on intrinsic radiographic characteristics, including macrogeometry and prosthetic connection types.
Chemotherapy-induced peripheral neuropathy (CIPN) is a common and disabling side-effect of various chemotherapeutic agents. This scoping review aimed to systematically map the existing literature on diagnostic methods used to identify, assess, and monitor CIPN. The review was guided by the research question: "What diagnostic methods have been used in the literature to identify, assess, or monitor chemotherapy-induced peripheral neuropathy in adult cancer patients?" We searched PubMed, Web of Science, Scopus, and the Cochrane Library from 2000 to 2024. Studies were included if they evaluated diagnostic methods for CIPN such as clinical assessments, patient-reported outcomes, biomarkers, neurophysiological tests, or digital tools. Data were extracted and narratively synthesized by diagnostic method type. The methodological quality of each included study was assessed using the Joanna Briggs Institute Critical Appraisal Tools. Twenty-nine studies met the inclusion criteria. The most frequently used tools were patient-reported questionnaires, notably the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire - Chemotherapy-Induced Peripheral Neuropathy 20 (EORTC QLQ-CIPN20) and the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE). Biomarkers such as neurofilament light chain and microRNAs, neurophysiological tests including nerve conduction studies, diffusion tensor imaging, functional magnetic resonance imaging, as well as digital technologies, such as mobile applications and wearable sensors, were also employed. Studies showed considerable heterogeneity in design, population, timing of assessments, and tool validation. Despite growing interest in multimodal approaches that integrate subjective and objective tools, a lack of standardization and validation limits the clinical applicability of many diagnostic methods. There is an urgent need to develop and validate reliable, reproducible, and feasible tools for the diagnosis and monitoring of CIPN in routine practice.