Search Results (439)

Adhesive fracture in layered structures is governed by subsurface crack evolution that cannot be accessed using surface-based diagnostics. Methods such as digital image correlation and optical spectroscopy measure surface deformation but implicitly assume a straight and uniform crack front, an assumption that becomes invalid for interfacial fracture with wide crack openings and asymmetric propagation. In this work, terahertz time-domain spectroscopy (THz-TDS) is combined with double-cantilever beam testing to directly map subsurface crack-front geometry in opaque adhesive joints. A strontium titanate-doped epoxy is used to enhance dielectric contrast. Multilayer refractive index extraction, pulse deconvolution, and diffusion-based image enhancement are employed to separate overlapping terahertz echoes and reconstruct two-dimensional delay maps of interfacial separation. The measured crack geometry is coupled with load–displacement data and augmented beam theory to compute spatially averaged stresses and energy release rates. The measurements resolve crack openings down to approximately 100 μm and reveal pronounced width-wise non-uniform crack advance and crack-front curvature during stable growth. These observations demonstrate that surface-based crack-length measurements can either underpredict or overpredict fracture toughness depending on the measurement location. Fracture toughness values derived from width-averaged subsurface crack fronts agree with J-integral estimates obtained from surface digital image correlation. Signal-to-noise limitations near the crack tip define the primary resolution limit. The results establish THz-TDS as a quantitative tool for subsurface fracture mechanics and provide a framework for physically representative toughness measurements in layered and bonded structures. Full article

Periapical lesions (PALs) are a common sequela of pulpal pathology, and accurate radiographic detection is essential for successful endodontic diagnosis and treatment outcome. With recent advancements in Artificial Intelligence (AI), deep learning systems have shown remarkable potential to enhance the diagnostic accuracy of PALs. This study highlights recent evidence on the use of AI-based systems in detecting PALs across various imaging modalities. These include intraoral periapical radiographs (IOPAs), panoramic radiographs (OPGs), and cone-beam computed tomography (CBCT). A literature search was conducted for peer-reviewed studies published from January 2021 to July 2025 evaluating artificial intelligence for detecting periapical lesions on IOPA, OPGs, or CBCT. PubMed/MEDLINE and Google Scholar were searched using relevant MeSH terms, and reference lists were hand screened. Data were extracted on imaging modality, AI model type, sample size, subgroup characteristics, ground truth, and outcomes, and then qualitatively synthesized by imaging modality and clinically relevant moderators (i.e., lesion size, tooth type and anatomical surroundings, root-filling status and effect on clinician’s performance). Thirty-four studies investigating AI models for detecting periapical lesions on IOPA, OPG, and CBCT images were summarized. Reported diagnostic performance was generally high across radiographic modalities. The study results indicated that AI assistance improved clinicians’ performance and reduced interpretation time. Performance varied by clinical context: it was higher for larger lesions and lower around complex surrounding anatomy, such as posterior maxilla. Heterogeneity in datasets, reference standards, and metrics limited pooling and underscores the need for external validation and standardized reporting. Current evidence supports the use of AI as a valuable diagnostic platform adjunct for detecting periapical lesions. However, well-designed, high-quality randomized clinical trials are required to assess the potential implementation of AI in the routine practice of periapical lesion diagnosis. Full article

Obstructive sleep apnea (OSA) is characterized by recurrent partial or complete upper airway collapse during sleep. Accurate assessment of airway anatomy is crucial for risk stratification, diagnosis, and treatment planning. While polysomnography (PSG) is considered the gold standard for OSA diagnosis, it provides limited anatomical insights. Cone-beam computed tomography (CBCT) has emerged as a valuable tool with lower radiation dose for three-dimensional (3D) assessment of the upper airway space and craniofacial structures. CBCT enables precise measurement of critical airway parameters including total airway volume and length, minimum cross-sectional area, linear dimensions of anteroposterior and lateral diameters, as well as soft tissue structures such as tongue, tonsils, and adenoids. This review aims to explore and comprehensively review the role of CBCT, primarily in upper airway assessment for OSA, with an emphasis on airway measurement parameters, anatomical reference landmarks, and the variabilities, in addition to its clinical applications in treatment planning and simulation and post-treatment efficacy evaluation. This review also highlights the technical considerations such image acquisition protocols, machine specifications and software algorithm, and patient positioning, which may affect measurement reliability and diagnostic accuracy. CBCT serves as a powerful adjunct in OSA diagnosis and management, enabling comprehensive assessment of the airway space and hard and soft tissue structures. It complements PSG by guiding personalized interventions such as maxillomandibular advancement or CPAP optimization. Standardized imaging protocols and consideration of patient positioning can further improve its clinical utility. Full article

This work presents a combined numerical and experimental investigation into the laser machining of aluminum alloy Al 1050 H14 using a high-power Continuous Wave (CW) fiber laser. Advanced three-dimensional, coupled thermal–structural Finite Element Method (FEM) simulations are developed to model key laser–material interaction processes, including laser-induced plastic deformation, laser etching, and engraving. Cases for both static single-shot and dynamic linear scanning laser beams are investigated. The developed numerical models incorporate a Gaussian heat source and the Johnson–Cook constitutive model to capture elastoplastic, damage, and thermal effects. The simulation results, which provide detailed insights into temperature gradients, displacement fields, and stress–strain evolution, are rigorously validated against experimental data. The experiments are conducted on an integrated setup comprising a 2 kW TRUMPF CW fiber laser hosted on a 3-axis CNC milling machine, with diagnostics including thermal imaging, thermocouples, white-light interferometry, and strain gauges. The strong agreement between simulations and measurements confirms the predictive capability of the developed FEM framework. Overall, this research establishes a reliable computational approach for optimizing laser parameters, such as power, dwell time, and scanning speed, to achieve precise control in metal surface treatment and modification applications. Full article

This study investigates the characteristics and causes of a low-level wind shear (LLWS) event induced by downward momentum transport at Xining Airport, China on 5 April 2023. By utilizing Doppler Wind Lidar (DWL), Automated Weather Observing System (AWOS), and ERA5 reanalysis data, the detailed structure and synoptic-scale mechanisms of the event were analyzed. The LLWS manifested as a non-convective, meso-γ scale (2–20 km) directional wind shear, characterized by horizontal variations in wind direction. The system moved from northwest to southeast and persisted for approximately three hours. The shear zone was characterized by westerly flow to the west and easterly flow to the east, with their convergence triggering upward motion. The Range Height Indicator (RHI) and Doppler Beam Swinging (DBS) modes of the DWL clearly revealed the features of westerly downward momentum transport. Diagnostic analysis of the synoptic-scale environment reveals that a developing 300-hPa trough steered the merging of the subtropical and polar front jets. This interaction provided a robust source of momentum. The secondary circulation excited in the jet entrance region promoted active vertical motion, facilitating the exchange of momentum and energy between levels. Simultaneously, the development of the upper-level trough led to the intrusion of high potential vorticity (PV) air from the upper levels (100–300 hPa) into the middle troposphere (approximately 500 hPa), which effectively transported high-momentum air downward and dynamically induced convergence in the low-level wind field. Furthermore, the establishment of a deep dry-adiabatic mixed layer in the afternoon provided a favorable thermodynamic environment for momentum transport. These factors collectively led to the occurrence of the LLWS. This study will further deepen the understanding of the formation mechanism of momentum-driven LLWS at plateau airports, and provide a scientific basis for improving the forecasting and warning of such hazardous aviation weather events. Full article

Background: Artificial intelligence (AI) integrated with cone-beam computed tomography (CBCT) has rapidly advanced the diagnostic capability of orthodontics, particularly for quantifying external root resorption (ERR). High-risk scenarios such as bilateral maxillary canine impaction require objective tools to guide treatment decisions and prevent irreversible damage. Objectives: To evaluate the diagnostic accuracy and clinical applicability of AI-assisted CBCT for orthodontically induced ERR, and to demonstrate its value in a complex clinical case where decision-making regarding canine traction versus extraction required precise risk quantification and definition of biological limits. Methods: A systematic review following PRISMA 2020 guidelines was conducted in PubMed, ScienceDirect, and Cochrane Library (2015–September 2025). Eligible studies applied AI-enhanced CBCT to assess ERR in orthodontic patients. Additionally, a clinical case with bilaterally impacted maxillary canines was evaluated using CBCT with automated AI segmentation and manual refinement to quantify root volume changes and determine prognostic thresholds for treatment modification. Results: Nine studies met the inclusion criteria. AI-based imaging, predominantly convolutional neural networks, showed high diagnostic accuracy (up to 94%), improving reproducibility and reducing operator dependency. In the clinical case, volumetric monitoring showed rapid progression of ERR in the lateral incisors (LI) associated with a persistent unfavorable 3D spatial relationship between the canines and incisor roots, despite controlled distal traction with skeletal anchorage, leading to a timely change in the treatment plan and extraction of the severely compromised incisors with substitution by the canines. AI-generated data provided objective evidence supporting safer decision-making and prevented further structural deterioration. Conclusions: AI-enhanced CBCT enables early, objective, and quantifiable ERR assessment, strengthening prognosis-based decisions in orthodontics. Findings of this review and the clinical case highlight the translational relevance of AI for managing high-risk cases, such as maxillary canine impaction with extensive LI resorption, supporting future predictive AI models for safer canine traction. Full article

Background/Objectives: Percutaneous transthoracic needle biopsy (PTNB) using advanced navigation techniques is increasingly performed; however, pulmonologists’ experience remains limited. This study reports an interventional pulmonologist’s initial experience with cone-beam computed tomography (CBCT) laser-guided PTNB and the diagnostic performance for lesions with diameters greater than or less than 20 mm. Methods: We retrospectively analysed the data of patients who underwent PTNB in a C-arm CBCT-equipped hybrid operating room between July 2020 and March 2024. All patients underwent the biopsy procedure under local anaesthesia. This was preceded by an initial 3D scan for planning of the needle route, followed by coaxial needle insertion. A post-procedural scan was also performed to identify complications. Results: Seventy-seven patients were enrolled in the study. The median distances of the needle path from the skin to the pleura and from the pleura to the lesion were 33.4 mm and 31.7 mm, respectively. The median number of tissue samplings was 4.9 ± 1.8. The median operating room duration was 51.5 ± 25.7 min, respectively. The median total dose area product was 8485.4 ± 5819.9 µGym². The sensitivity and specificity of our study findings were 93.3% (56/60) and 100%, while the accuracy was 94.8% (73/77). The overall complication rate was 13%. Conclusions: PTNB procedure by pulmonologists is a feasible and safe, single-operator workflow in a hybrid operating room. It can be performed under CBCT laser guidance with a similar diagnostic yield, acceptable radiation exposure and procedure duration, and minimal or manageable complications. Full article

Objectives: To assess the utility of Microsoft Copilot, a generative AI tool, in providing meaningful differential diagnosis and management strategies comparable with those generated by a board-certified radiologist using cone beam computed tomography (CBCT) studies in maxillofacial disease and thus assess its potential utility to help with the initial provisional diagnostic process. Study Design: A pilot project designed as a single-center, retrospective study using a convenient sample was conducted. De-identified data collected from patient charts in a consistent format was fed to Microsoft 365 Copilot (MCP) to generate a list of meaningful differential diagnosis (DD) and management protocols. Scores ranging of 0–3 were given for 0–3 matches in DD and management protocols, respectively. Results: Proportional analysis showed that the radiologist and Copilot agreed on the DD in 75.2% of cases and 94.6% of cases in management protocols. For biopsy recommendations, the radiologist and Copilot advised biopsy in 33 (89.2%) cases while they did not recommend biopsy in 23 (41.8%) cases. Conclusions: Generative AI platforms at this point may have value in generating DD and management protocols based on maxillofacial CBCT findings. However, the radiologist’s judgement based on clinical context, feature recognition, and critical analysis seemed to outperform MCP. Larger studies with statistical validation are warranted. Full article

This work investigates the nonlinear flexural dynamics of a macroscale cantilever beam by combining analytical modeling, symbolic solution techniques, numerical simulation, and vision-based experiments. Starting from the Euler–Bernoulli equation with geometric and inertial nonlinearities, a reduced-order model is derived via a single-mode Galerkin projection, justified by the experimentally confirmed dominance of the fundamental bending mode. The resulting nonlinear ordinary differential equation is solved analytically using two symbolic methods rarely applied in structural vibration studies: the Extended Direct Algebraic Method (EDAM) and the Sardar Sub-Equation Method (SSEM). Comparison with high-accuracy numerical integration shows that EDAM reproduces the nonlinear waveform with high fidelity, including the characteristic non-sinusoidal distortion induced by mid-plane stretching. High-speed vision-based measurements provide displacement data for a physical cantilever beam undergoing free vibration. After calibrating the linear stiffness, analytical and experimental responses are compared in terms of the dominant oscillation frequency. The analytical model predicts the classical hardening-type amplitude–frequency dependence of an ideal Euler–Bernoulli cantilever, whereas the experiment exhibits a clear softening trend. This contrast reveals the influence of real-world effects, such as initial curvature, boundary compliance, or micro-slip at the clamp, which are absent from the idealized formulation. The combined analytical–experimental framework thus acts as a diagnostic tool for identifying competing nonlinear mechanisms in flexible structures and provides a compact physics-based reference for reduced-order modeling and structural health monitoring. Full article

Panoramic radiography remains a cornerstone diagnostic tool in dentistry; however, its two-dimensional nature limits the visualisation of complex maxillofacial anatomy. Three-dimensional reconstruction from single panoramic images addresses this limitation by computationally generating spatial representations without additional radiation exposure or expensive cone-beam computed tomography (CBCT) scans. This systematic review and conceptual study traces the evolution of 3D reconstruction approaches, from classical geometric and statistical shape models to modern artificial intelligence-based methods, including convolutional neural networks, generative adversarial networks, and neural implicit fields such as Occudent and NeBLa. Deep learning frameworks demonstrate superior accuracy in reconstructing dental and jaw structures compared to traditional techniques. Building on these advancements, this paper proposes HoloDent3D, a theoretical framework that combines AI-driven panoramic reconstruction with real-time holographic visualisation. The system enables interactive, radiation-free volumetric inspection for diagnosis, treatment planning, and patient education. Despite significant progress, persistent challenges include limited paired 2D–3D datasets, generalisation across anatomical variability, and clinical validation. Continued integration of multimodal data fusion, temporal modelling, and holographic visualisation is expected to accelerate the clinical translation of AI-based 3D reconstruction systems in digital dentistry. Full article

Cone beam computed tomography (CBCT) allows for rapid and accessible acquisition of three-dimensional images with a lower radiation dose compared to conventional computed tomography (CT) scans. However, the quality of CBCT images is limited by a variety of artifacts. This systematic review attempts to explore different artificial intelligence-based solutions for enhancing the quality of CBCT scans and reducing different types of artifacts in these three-dimensional images. PubMed, Web of Science, Scopus, Embase, Cochrane, and Google Scholar were searched up to March 2025. Risk of bias of included studies was assessed using the QUADAS-II tool. Extracted data included bibliographic information, aim, imaging modality, anatomical site of interest, artificial intelligence modeling approach and details, data and dataset details, qualitative and quantitative performance metrics, and main findings. A total of 27 papers from 2018 to 2025 were included. These studies focused on five areas: metal artifact reduction, scatter correction, image reconstruction improvement, motion artifact reduction, and noise reduction. Artificial intelligence models mainly used U-Net variants, though hybrid and transformer-based models were also explored. The thoracic region was the most analyzed, and the structural similarity index measure and peak signal-to-noise-ratio were common performance metrics. Data availability was limited, with only 26% of studies providing public access and 15% sharing model source codes. Artificial intelligence-driven approaches have demonstrated promising results for CBCT artifact reduction. This review highlights a wide variability in performance assessments and that most studies have not received diagnostic validation, limiting conclusions on the true clinical impact of these artificial intelligence-based improvements. Full article

This systematic review evaluated the effectiveness of iterative reconstruction (IR) and deep learning reconstruction (DLR) in reducing radiation dose in computed tomography (CT) while preserving diagnostic image quality. We systematically searched PubMed, Scopus, and Web of Science (last search 22 March 2025); the protocol was registered in the OSF (DOI: 10.17605/OSF.IO/TUQDS). Eligible studies were English-language adult (≥18 years) investigations published between 2020 and 2025 that used IR or DLR and reported radiation-dose outcomes; studies on paediatric, phantom, cadaver, cone-beam, and spectral CT were excluded. In accordance with PRISMA 2020 guidelines, 4371 records were identified, and 30 met the inclusion criteria. Risk of bias was assessed using the NIH Quality Assessment Tool; most studies were deemed to be at low risk. Data were narratively synthesised and structured by a reconstruction approach and anatomical region. Across the 30 studies, IR achieved a dose reduction of 24–50% (mean ≈ 45%) and a DLR reduction of 34–89% (mean ≈ 58%); several DLR protocols enabled reductions of ≥75% without impairing diagnostic quality. Thirty studies in total were included (total N = 2581; range 24–289). It was determined that both approaches substantially reduce radiation exposure while maintaining diagnostic image quality; DLR generally demonstrates greater noise suppression and dose efficiency, especially in ultra-low-dose applications. However, heterogeneity in methods, designs, and scanner technologies limits the ability to draw uniform conclusions. Standardised protocols, multi-vendor prospective studies, and long-term evaluations are needed. Full article

Background/Objectives: The sella turcica (ST) is a central craniofacial and endocrinological landmark whose morphology reflects both local skeletal development and systemic influences. Alterations in its form have been observed in various genetic syndromes, including trisomy 21 (Down syndrome, DS). Considering the characteristic craniofacial morphology of DS, this review aimed to evaluate whether individuals with DS present distinctive morphometric features and shape variants of the ST compared with non-syndromic populations and to discuss their diagnostic and clinical relevance. Methods: A systematic literature search was carried out in PubMed, the Cochrane Library, Web of Science, Wiley, MDPI, and Google Scholar on 8 May 2024. Search terms included “sella turcica,” “Down syndrome,” and “morphology.” Studies employing lateral cephalograms, cone-beam computed tomography (CBCT), or computed tomography (CT) to assess ST morphology were included when quantitative or qualitative comparisons with control groups were available. The review followed the PRISMA 2020 guidelines and was prospectively registered in PROSPERO (CRD42024580071). Results: Only six studies fulfilled the inclusion criteria. Increased ST dimensions and a predominance of U-shaped and J-shaped variants in individuals with DS compared with controls were most frequently reported. Although the studies differed in methodology, the findings consistently indicated characteristic enlargement and remodeling of the ST in trisomy 21. Conclusions: Individuals with Down syndrome exhibit distinctive sella turcica morphology characterized by increased size and specific shape variants. The evidence base remains small and heterogeneous, with few observational studies and mixed age groups and imaging modalities, which limits the strength and generalizability of the conclusions. The present study aims to provide a modern, updated systematic review of current evidence on sella turcica morphology in patients with Down syndrome, to identify reported patterns of variation, and to explore their clinical and diagnostic significance. Recognition of these features enhances diagnostic accuracy in craniofacial evaluation, facilitates comprehensive orthodontic, endocrine, and oncological assessment, and advances understanding of cranial base development within the context of genetic syndromes. Full article

EuPRAXIA@SPARC_LAB is an FEL (Free-Electron Laser) user facility currently under construction at INFN-LNF in the framework of the EuPRAXIA collaboration. The electron beam will be accelerated to 1 GeV by an X-band RF linac followed by a plasma wakefield acceleration stage. This high-brightness linac requires diagnostic devices able to measure the beam parameters with high accuracy and resolution. To monitor the beam energy and its spread, magnetic dipoles and quadrupoles will be installed along the linac, in combination with viewing screens and CMOS cameras. Macroparticle beam dynamics simulations have been performed to determine the optimal energy measurement setup in terms of accuracy and resolution. Similar diagnostics evaluations have been carried out for the spectrometer installed at the 100 MeV RF linac of the radioactive beam facility SSRIP (IFIN-HH, Romania), whose commissioning, foreseen for 2026, will be performed by INFN-LNF in collaboration with IFIN-HH. Optics measurements have been performed to characterize the resolution and magnification of the optical system that will be used at SSRIP, and probably also at EuPRAXIA@SPARC_LAB, for beam energy monitoring. Full article

Background/Objectives: Artificial intelligence (AI) systems may enhance diagnostic accuracy in cone-beam computed tomography (CBCT) analysis. However, most validations focus on isolated tooth-level tasks rather than clinically meaningful full-mouth assessment outcomes. To evaluate the diagnostic accuracy of a commercial AI platform for detecting dental treatment features on CBCT images at both tooth and full-scan levels. Methods: In this retrospective single-center study, 147 CBCT scans (4704 tooth positions) were analyzed. Two experienced readers annotated treatment features (missing teeth, fillings, endodontic treatments, crowns, pontics, orthodontic appliances, implants), and consensus served as the reference. Anonymized datasets were processed by a cloud-based AI system (Diagnocat Inc., San Francisco, CA, USA). Diagnostic metrics—sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score—were calculated with 95% patient-clustered bootstrap confidence intervals. A “Perfect Agreement” criterion defined full-scan level success as an entirely error-free full-mouth report. Results: Tooth-level AI performance was excellent, with accuracy exceeding 99% for most categories. Sensitivity was highest for missing teeth (99.3%) and endodontic treatments (99.0%). Specificity and NPV exceeded 98.5% and 99.7%, respectively. Full-scan level Perfect Agreement was achieved in 82.3% (95% CI: 76.2–88.4%), with errors concentrated in teeth presenting multiple co-existing findings. Conclusions: The evaluated AI platform demonstrates near-perfect accuracy in detecting isolated dental features but moderate reliability in generating complete full-mouth reports. It functions best as an assistive diagnostic tool, not as an autonomous system. Full article