Search Results (125)

Aluminum foam materials have gained significant attention over the past decade, particularly in the automotive industry, due to their excellent stiffness-to-weight ratio and superior energy absorption capabilities. In this study, a multiscale numerical material model was developed to accurately and efficiently simulate the vibrational behavior of aluminum foams. The foam specimens were categorized into four density classes based on their measured mass and calculated volume. Two specimens were selected to conduct CT (computerized tomography) scans and quantify the volume of air in their density class. Based on the CT measurements, a representative volume element (RVE) was built using ANSYS Material Designer (MD). The newly obtained material was employed in conducting normal mode numerical simulations. The resonance frequencies and response amplitudes were compared with physical experiments and showed correlation within 3%. These findings underscore the efficacy of using CT scans in foam to develop material models and accurately predict structural behavior. By conducting comprehensive investigations and numerical simulations, we established a correlation between physical tests and simulation results, highlighting the reliability of the developed models. Full article

A 63-year-old woman with rheumatoid arthritis and Hashimoto’s thyroiditis was admitted to the emergency room, because of left leg pain associated with spontaneous subcutaneous hematomas, for 15 days. Their symptoms also occurred after the discontinuation of aspirin, which the patient had taken for a previous case of ocular papillitis. Laboratory tests showed anemia, a normal platelet count, but a prolonged activated partial thromboplastin time (aPTT) ratio; a computerized tomography scan of the left lower limb detected a recent hematoma in the left lateral rectus muscle, and subcutaneous soft tissue edema also involving the knee, without vascular involvement. Coagulation tests were performed showing normal levels of Lupus Anticoagulant, very low-factor FVIII activity (2.2%), normal FIX, FXI, and FXII activity, and the detection of FVIII inhibitors by a Bethesda assay (7.6 U). A diagnosis of acquired hemophilia A (AHA) was made, and hemostatic and immunosuppressive treatment was immediately started (activated prothrombin complex concentrates and methylprednisolone). Malignancies and infections were excluded. An autoantibodies panel confirmed the positivity to rheumatoid factor and anti-cyclic citrullinated peptide antibodies. In treatment, the patient did not present any new bruises, with aPTT normalizing, FVIII increasing, and inhibitors reducing until disappearance. A close follow-up continued every 1–2 week after discharge, with hemostatic treatment discontinuation and methylprednisolone decalage. Underlying autoimmune conditions induced this rare, autoimmune and life-threating disorder. Full article

Background and Objectives: The effects of COVID-19 disease can manifest and cause eye complications, one of which is diplopia. Diplopia is a medical condition that makes one object appear like two images. People may also experience diplopia after receiving the COVID-19 vaccine, after contracting COVID-19, or following a COVID-19 infection. Materials and Methods: This review aims to summarize the cases of COVID-19 that can cause diplopia and its treatment in the past 5 years. The literature search databases used for this review were PubMed and Scopus. The keywords used were “diplopia,” “COVID-19,” and “treatment.” Sixteen articles were reviewed after screening and applying the inclusion criteria. Results: The results show that over the past 5 years, cases of diplopia related to COVID-19 have occurred in America, Europe, Asia, and Africa. Most studies are case reports, and the total number of patients was 26, with an age range of 14 to 81. Conclusions: The diplopia cases recovered within 1 day to 8 months. Patients who experienced diplopia after receiving the COVID-19 vaccine, during COVID-19 infection, or after COVID-19 infection exhibited different symptoms. Nasopharyngeal swabs, magnetic resonance imaging (MRI), computerized tomography (CT) scans, visual acuity tests, slit lamp biomicroscope examinations, eye movement tests, funduscopic examinations, and blood tests were the most commonly performed tests. Corticosteroids such as prednisone, methylprednisolone, and prednisolone were the most commonly used drugs to treat diplopia. In addition to corticosteroids, antibiotics, antivirals, antiplatelets, and vitamins were also given. An eye patch was considered to alleviate the diplopia. Full article

The integration of Deep Learning and Symbolic Artificial Intelligence (AI) offers a promising hybrid framework for enhancing diagnostic accuracy and explainability in critical applications such as COVID-19 detection using computerized tomography (CT) scans. This study proposes a novel hybrid AI model that leverages the strengths of both approaches: the automated feature extraction and classification capabilities of Deep Learning and the logical reasoning and interpretability of Symbolic AI. Key components of the model include the adaptive deformable module, which improves spatial feature extraction by addressing variations in lung anatomy, and the attention-based encoder, which enhances feature saliency by focusing on critical regions within CT scans. Experimental validation using performance metrics such as F1-score, accuracy, precision, and recall demonstrates the model’s significant improvement over baseline configurations, achieving near-perfect accuracy (99.16%) and F1-score (0.9916). This hybrid AI framework not only achieves state-of-the-art diagnostic performance but also ensures interpretability through its symbolic reasoning layer, facilitating its adoption in healthcare settings. The findings underscore the potential of combining advanced machine learning techniques with symbolic approaches to create robust and transparent AI systems for critical medical applications. Full article

The prevalence of cardiac amyloidosis (CA), especially as a cause of heart failure, has significantly increased in recent years. Early detection and accurate assessment of the disease burden are crucial for initiating timely treatment and ensuring precise prognosis. CA primarily results from the infiltration of the myocardium by either immunoglobulin light chain fibrils (AL) or transthyretin fibrils (ATTR), leading to restrictive cardiomyopathy and eventual death if untreated. Over the past decade, advancements in diagnostic imaging and heightened clinical awareness have revealed a substantial presence of CA, particularly ATTR, among the elderly. These diagnostic improvements encompass echocardiography, cardiac computerized tomography scans, magnetic resonance imaging, and radionuclide scintigraphy with bone-avid tracers. Concurrently, significant progress has been made in therapeutic options, with new disease-modifying treatments now available that can dramatically alter the disease trajectory and improve survival rates when administered early. However, despite these advancements, there remains an urgent need for the early and accurate detection of CA to ensure that patients can fully benefit from these emerging therapies. Full article

Ultra-high-performance concrete (UHPC) is considered one of the future building materials due to its excellent performance. UHPC with good thermal conductivity has potential high-value applications in large-scale bridges and nuclear facilities. As a by-product of the coal gasification process, coal gasification slag (CGS) can replace sand in traditional UHPC. In this paper, based on the preparation of UHPC by CGS, silicon carbide (SiC) was added to improve the thermal conductivity of specimens. The application of CGS and SiC as alternatives to quartz sand with varying mix ratios in UHPC was studied. The impact of the substitution ratios of CGS and SiC on fluidity, mechanical properties, and thermal performance was analyzed. The compressive strength and splitting tensile strength of five different kinds of specimens were tested at 7 d, 14 d, and 28 d. The compressive strength and mass loss rate of specimens with five different ratios were also determined under five different temperature conditions (110 °C, 200 °C, 300 °C, 400 °C, and 500 °C). The results show that the maximum compressive strength of 28 d can reach 159.5 MPa and the splitting strength is 15.30 MPa. The addition of SiC can improve the thermal conductivity and thermal stability of concrete. The compressive strength of all specimens is improved after high-temperature treatment. When substitution rate of SiC reaches 100%, the compressive strength of the specimens is up to 182.2 MPa. With the increase in temperature, the concrete burst phenomenon occurs above 300 °C. It is observed that the high-temperature burst resistance of the specimens with low strength is better than that of the specimens with high strength. Two specimens were scanned with Industrial Computerized Tomography (ICT) and the microstructures of the specimens were compared. It was found that the samples with higher SiC substitution rates had more minor total pore defects and larger pores. Full article

Measurements of tooth size for estimating inter-arch tooth size discrepancies and inter-tooth distances, essential for orthodontic diagnosis and treatment, are primarily done using traditional methods involving plaster models and calipers. These methods are time-consuming and labor-intensive, requiring multiple steps. With advances in cone-beam computerized tomography (CBCT) and intraoral scanning technology, these processes can now be automated through computer analyses. This study proposes a multi-step computational method for measuring mesiodistal tooth widths and inter-tooth distances, applicable to both CBCT and scan images of plaster models. The first step involves 3D segmentation of the upper and lower teeth using CBCT, combining results from sagittal and panoramic views. For intraoral scans, teeth are segmented from the gums. The second step identifies the teeth based on an adaptively estimated jaw midline using maximum intensity projection. The third step uses a decentralized convolutional neural network to calculate key points representing the parameters. The proposed method was validated against manual measurements by orthodontists using plaster models, achieving an intraclass correlation coefficient of 0.967 and a mean absolute error of less than 1 mm for all tooth types. An analysis of variance test confirmed the statistical consistency between the method’s measurements and those of human experts. Full article

As a novel pavement wear layer material, the micromechanical mechanisms of High-toughness Ultra-thin Friction Course (HUFC) have not been fully elucidated. This paper presents a new method for the three-dimensional micromechanical simulation of high-toughness asphalt mixtures based on a viscoelastic parameter calibration model. X-ray Computerized Tomography (CT) was employed to scan samples of high-toughness asphalt mixtures to obtain detailed information on the internal structure (aggregate, fine aggregate matrix FAM and voids), and a three-dimensional micromechanical model was constructed based on the real-scale distribution of these components. Aggregates in the high-toughness asphalt mixture were modeled as elastic bodies, while FAM was treated as a viscoelastic material characterized by the Burgers model. Using the Boltzmann linear superposition principle and Laplace transform theory, the viscoelastic properties of FAM were converted into Prony parameters recognizable by finite element software, and the viscoelastic parameters were calibrated. Micromechanical simulations were conducted for three different gradings of high-toughness asphalt mixtures, and the results show that the predicted deformation closely matched the measured deformation. This method accurately reflects the deformation characteristics of different gradings of high-toughness asphalt mixtures, overcoming the limitations of traditional numerical simulations based on homogeneous material models. It represents an advancement and refinement of micromechanical simulation methods for high-toughness asphalt mixtures. Full article

Background: The natural history and prognosis of mesenteric panniculitis (MP) are not well-described. Despite referral for colonoscopy being common for this indication, colonoscopy findings in MP patients have not been reported. Therefore, we aimed to describe upper and lower gastrointestinal (GI) endoscopy findings in patients with mesenteric panniculitis, compared to matched controls, to investigate their clinical outcomes including incidence of malignancy and mortality. Methods: Retrospective case–control study was conducted, and included patients who were diagnosed with mesenteric panniculitis according to Coulier radiologic criteria on abdominal computerized tomography between 1/2005 and 12/2019, and followed to 12/2021. The case group was compared to a matched control group without MP on abdominal CT. Clinical data and the upper and lower endoscopies’ reports were reviewed in both groups. We excluded patients who, beyond diagnosis of MP, were also diagnosed with current malignancy, significant intra-abdominal morbidity or inflammatory bowel disease. Results: The initial set of 376 patients with MP, after exclusion, included 187 patients. A total of 56.1% were male, with a mean age 60 ± 15 years. Of them, 74 (39%) patients underwent follow-up CT scans, which demonstrated, in 66 (89.2%) patients, a stable MP without any aggravation. Colonoscopy was performed in 89 MP patients, and 98/187 controls. No significant difference in the colonoscopies’ findings was found between the two groups. Gastroscopy was performed in 84 MP and 79 controls. No case of gastric cancer was found. No statistically significant difference was found in the rate of gastroscopy findings. By the end of the follow-up period, malignancy was diagnosed in four patients of the MP group. None were colon cancer. The mortality rate in the MP group was 3.2%, without a significant difference compared to the controls. None were MP related. Conclusions: MP identified on abdominal CT is not associated with pathologic endoscopy findings or future diagnosis of colon cancer, and also has no impact on mortality rate. Since repeating abdominal CT did not reveal any disease progression, the necessity of follow-up imaging for MP should be carefully reconsidered. Full article

Geometrical models of the airways offer a comprehensive perspective on the complex interplay between lung structure and function. Originating from mathematical frameworks, these models have evolved to include detailed lung imagery, a crucial enhancement that aids in the early detection of morphological changes in the airways, which are often the first indicators of diseases. The accurate representation of airway geometry is crucial in research areas such as biomechanical modeling, acoustics, and particle deposition prediction. This review chronicles the evolution of these models, from their inception in the 1960s based on ideal mathematical constructs, to the introduction of advanced imaging techniques like computerized tomography (CT) and, to a lesser degree, magnetic resonance imaging (MRI). The advent of these techniques, coupled with the surge in data processing capabilities, has revolutionized the anatomical modeling of the bronchial tree. The limitations and challenges in both mathematical and image-based modeling are discussed, along with their applications. The foundation of image-based modeling is discussed, and recent segmentation strategies from CT and MRI scans and their clinical implications are also examined. By providing a chronological review of these models, this work offers insights into the evolution and potential future of airway geometry modeling, setting the stage for advancements in diagnosing and treating lung diseases. This review offers a novel perspective by highlighting how advancements in imaging techniques and data processing capabilities have significantly enhanced the accuracy and applicability of airway geometry models in both clinical and research settings. These advancements provide unique opportunities for developing patient-specific models. Full article

Obesity is a chronic relapsing disease and a major public health concern due to its high prevalence and associated complications. Paradoxically, several studies have found that obesity might positively impact the prognosis of patients with certain existing chronic diseases, while some individuals with normal BMI may develop obesity-related complications. This phenomenon might be explained by differences in body composition, such as visceral adipose tissue (VAT), total body fat (TBF), and fat-free mass (FFM). Indirect measures of body composition such as body circumferences, skinfold thicknesses, and bioelectrical impedance analysis (BIA) devices are useful clinically and in epidemiological studies but are often difficult to perform, time-consuming, or inaccurate. Biomedical imaging methods, i.e., computerized tomography scanners (CT scan), dual-energy X-ray absorptiometry (DEXA), and magnetic resonance imaging (MRI), provide accurate assessments but are expensive and not readily available. Recent advancements in 3D optical image technology offer an innovative way to assess body circumferences and body composition, though most machines are costly and not widely available. Two-dimensional optical image technology might offer an interesting alternative, but its accuracy needs validation. This review aims to evaluate the efficacy of 2D and 3D automated body scan devices in assessing body circumferences and body composition. Full article

Elbow computerized tomography (CT) scans have been widely applied for describing elbow morphology. To enhance the objectivity and efficiency of clinical diagnosis, an automatic method to recognize, segment, and reconstruct elbow joint bones is proposed in this study. The method involves three steps: initially, the humerus, ulna, and radius are automatically recognized based on the anatomical features of the elbow joint, and the prompt boxes are generated. Subsequently, elbow MedSAM is obtained through transfer learning, which accurately segments the CT images by integrating the prompt boxes. After that, hole-filling and object reclassification steps are executed to refine the mask. Finally, three-dimensional (3D) reconstruction is conducted seamlessly using the marching cube algorithm. To validate the reliability and accuracy of the method, the images were compared to the masks labeled by senior surgeons. Quantitative evaluation of segmentation results revealed median intersection over union (IoU) values of 0.963, 0.959, and 0.950 for the humerus, ulna, and radius, respectively. Additionally, the reconstructed surface errors were measured at 1.127, 1.523, and 2.062 mm, respectively. Consequently, the automatic elbow reconstruction method demonstrates promising capabilities in clinical diagnosis, preoperative planning, and intraoperative navigation for elbow joint diseases. Full article

(1) Background: Recent digital workflows are being developed for full-arch rehabilitations supported by implants with immediate function. The purpose of this case series is to describe a new digital workflow for the All-on-4 concept. (2) Methods: The patients were rehabilitated using the All-on-4 concept with a digital workflow including computerized tomography scanning, intra-oral scanning, and CAD-CAM production of the temporary prosthesis, with the 3D printing of stackable guides (base guide, implant guide, and prosthetic guide). The passive fit of the prostheses and the time to perform the rehabilitations were evaluated. (3) Results: The digital workflow allowed for predictable bone reduction, the insertion of implants with immediate function, and the connection of an implant-supported prosthesis with immediate loading. The time registered to perform the full-arch rehabilitations (implant insertion, abutment connection, prosthesis connection) was below 2 hours and 30 min. No passive fit issues were noted. (4) Conclusions: within the limitation of this case series, the digital workflow applied to the All-on-4 concept using stackable base-, implant-, and prosthetic guides constitutes a potential alternative with decreased time for the procedure without prejudice of the outcome. Full article

Every year, almost 4 million patients received medical care for knee osteoarthritis. Osteoarthritis involves progressive deterioration or degenerative changes in the cartilage, leading to inflammation and pain as the bones and ligaments are affected. To enhance treatment and surgical outcomes, various studies analyzing the biomechanics of the human skeletal system by fabricating simulated bones, particularly those reflecting the characteristics of patients with knee osteoarthritis, are underway. In this study, we fabricated replicated bones that mirror the bone characteristics of patients with knee osteoarthritis and developed a skeletal model that mimics the actual movement of the knee. To create patient-specific replicated bones, models were extracted from computerized tomography (CT) scans of knee osteoarthritis patients. Utilizing 3D printing technology, we replicated the femur and tibia, which bear the weight of the body and support movement, and manufactured cartilage capable of absorbing and dispersing the impact of knee joint loads using flexible polymers. Furthermore, to implement knee movement in the skeletal model, we developed artificial muscles based on shape memory alloys (SMAs) and used them to mimic the rolling, sliding, and spinning motions of knee flexion. The knee movement was investigated by changing the SMA spring’s position, the number of coils, and the applied voltage. Additionally, we developed a knee-joint-mimicking system to analyze the movement of the femur. The proposed artificial-skeletal-model-based knee-joint-mimicking system appears to be applicable for analyzing skeletal models of knee patients and developing surgical simulation equipment for artificial joint replacement surgery. Full article

The study of pig bones, due to their similarity with human tissues, has facilitated the development of technological tools that help in the diagnosis of diseases and injuries affecting the skeletal system. Radiomic techniques involving medical image segmentation, along with finite element analysis, enable the detailed study of bone damage, loss of density, and mechanical functionality, which is a significant advancement in personalized medicine. This study involves conducting experimental tests on L3–L6 pig vertebrae under axial loading conditions. The mechanical properties of these vertebrae are analyzed, and the maximum loads they can sustain within the elastic range are determined. Additionally, three-dimensional models are generated by segmenting computerized axial tomography (CAT) scans of the vertebrae. Digital shadows of the vertebrae are constructed by assigning an anisotropic material model to the segmented geometries. Then, finite element analysis is performed to evaluate the elastic characteristics, stress, and displacement. The findings from the experimental data are then compared to the numerical model, revealing a strong correlation with differences of less than 0.8% in elastic modulus and 1.53% in displacement. The proposed methodology offers valuable support in achieving more accurate medical outcomes, employing models that serve as a diagnostic reference. Moreover, accurate bone modeling using finite element analysis provides valuable information to understand how implants interact with the surrounding bone tissue. This information is useful in guiding the design and optimization of implants, enabling the creation of safer, more durable, and biocompatible medical devices that promote optimal osseointegration and healing in the patient. Full article