Search

Objective To study the possibility of using pediatric endocrowns to restore the second primary molar using three-dimensional (3D) finite element analysis. Design A 3D finite element model was built for a pediatric mandibular molar, starting with laser scanning a naturally extracted tooth. The access cavity had an elliptic shape with 6 mm width, 4 mm height, and 2 mm depth with a wall taper angle of 5 degrees.Two materials (Zr and E-max) were tested for the endocrown and two cementing materials (glass ionomer and resin cement) with 20 to 40 μm thickness. Twelve case studies were reported within this research as the applied load of 330 N was tested with three angulations vertical, oblique at 45 degrees, and laterally. Results Twelve linear static stress analyses were performed. The resultant stresses and deformations' distribution patterns did not alter much, and values were within the threshold of physiological tolerance. Deformations were negligibly changed with changing endocrown and cement materials. In contrast, endocrown stresses indicated zirconia endocrown would have a long lifetime, while E-max one will have a relatively short lifetime. Conclusions Analysis results indicated that bone was negligibly affected by changing endocrowns and cementing materials. Both tested endocrown materials can be used safely. Zirconia endocrowns may have a much longer lifetime than E-max.

OBJECTIVE: To calculate the risk for postoperative complications and mortality after corrective surgery of adult spinal deformity (ASD) patients using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator (SRC). METHODS: Patients aged ≥18 years undergoing corrective surgery for ASD were identified. Current procedural terminology (CPT) codes of 22800, 22802, 22804, 22808, 22801, 22812, 22818, 22819, 22843, 22844, 22846, 22847, 22842, and 22845 were assessed if the patient had an International Classification of Diseases Ninth Revision (ICD-9) scoliosis diagnosis (737.00–737.9). Calculated perioperative complication risk averages via the ACS-NSQIP surgical calculator were compared with observed complication rates. Outcomes assessed were as follows: serious complication, any complication, pneumonia, cardiac complication, surgical site infection, urinary tract infection, venous thromboembolism, renal failure, readmission, return to operating room, death, discharge to nursing or rehabilitation, sepsis, and total length of hospital stay. Predictive performance of the calculator was analyzed by computation of the Brier score. A Brier score is the sum of squared differences between the binary outcome and the predicted risk and ranges from 0 to a maximum Brier score = (mean observed outcome)*(1-[mean observed outcome]). Values closer to 0 are suggestive of better predictive performance. Length of stay (LOS) was assessed with a Bland-Altman plot, which plots the average of observed LOS on the x axis and the difference between the observed and predicted LOS on the y axis. RESULTS: A total of 9143 ASD patients (58.9 years, 56% females, 29.2 kg/m(2)) were identified; 36.9% of procedures involved decompression and 100% involved fusion. The means for individual patient characteristics entered into the online risk calculator interface were as follows: functional status (independent: 94.9%, partially dependent: 4.4%, totally dependent: 0.70%), 1.6% emergent cases, wound class (clean: 94.7%, clean/contaminated: 0.8%, contaminated: 0.5%, dirty/infected: 1.4%), American Society of Anesthesiologists class (I: 2.7%, II: 40.7%, III: 52.1%, IV: 4.6%, V: 0%), 5.1% steroid use for chronic condition, 0.04% ascites within 30 days prior to surgery, 1.73% systemic sepsis within 48 hours of surgery, 0.40% ventilator dependent, 3.2% disseminated cancer, 15.6% diabetes mellitus, 52.8% use of hypertensive medications, 0.3% congestive heart failure , 3% dyspnea, 21.4% history of smoking within 1 year, 4.3% chronic obstructive pulmonary disease, 0.7% dialysis, and 0.1% acute renal failure. Predictive of any 30-day postoperative complications ranged from 2.8 to 18.5% across CPT codes, where the actual rate in the cohort was 11.4%, and demonstrated good predictive performance via Brier score (0.000002, maximum: 0.101). The predicted and observed percentages for each of the 13 outcomes were assessed and their associated Brier scores and Brier maximums were calculated. Mean difference between observed and predicted LOS was 2.375 days (95% CI 9.895–5.145). CONCLUSIONS: The ACS-NSQIP SRC predicts surgical risk in patients undergoing ASD corrective surgery. This tool can be used as a resource in preoperative optimization by deformity surgeons. LEVEL OF EVIDENCE: 3.

This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and transfer learning in the context of medical imaging. Medical imaging plays a critical role in the diagnosis and treatment of diseases, and CNN-based models have demonstrated significant improvements in image analysis and classification tasks. Transfer learning, which involves reusing pre-trained CNN models, has also shown promise in addressing challenges related to small datasets and limited computational resources. This paper reviews the advantages of CNN and transfer learning in medical imaging, including improved accuracy, reduced time and resource requirements, and the ability to address class imbalances. It also discusses challenges, such as the need for large and diverse datasets, and the limited interpretability of deep learning models. What factors contribute to the success of these networks? How are they fashioned, exactly? What motivated them to build the structures that they did? Finally, the paper presents current and future research directions and opportunities, including the development of specialized architectures and the exploration of new modalities and applications for medical imaging using CNN and transfer learning techniques. Overall, the paper highlights the significant potential of CNN and transfer learning in the field of medical imaging, while also acknowledging the need for continued research and development to overcome existing challenges and limitations.

The health system in today’s real world is significant but difficult and overcrowded. These hurdles can be diminished using improved health record management and blockchain technology. These technologies can handle medical data to provide security by monitoring and maintaining patient records. The processing of medical data and patient records is essential to analyze the earlier prescribed medicines and to understand the severity of diseases. Blockchain technology can improve the security, performance, and transparency of sharing the medical records of the current healthcare system. This paper proposed a novel framework for personal health record (PHR) management using IBM cloud data lake and blockchain platform for an effective healthcare management process. The problem in the blockchain-based healthcare management system can be minimized with the utilization of the proposed technique. Significantly, the traditional blockchain system usually decreases the latency. Therefore, the proposed technique focuses on improving latency and throughput. The result of the proposed system is calculated based on various matrices, such as F1 Score, Recall, and Confusion matrices. Therefore, the proposed work scored high accuracy and provided better results than existing techniques.

Deep learning is one of the machine learning approach which has shown promising results and performance as compare to traditional algorithms of machine learning in terms of high dimensional data of MRI brain image. In this article the application of deep learning in medical field is addressed. A thorough review of various algorithms of deep learning for diagnosis of Alzheimer’s disease is done, in which this disease is a progressive brain disorder that destroy the brain memory gradually, it is a common disease in older adults which is caused by dementia. It has been obtained in most research papers that the most widely used and represented algorithm is Convolutional Neural Networks (CNN) when it deals with brain image analysis. After study of various related papers for diagnosing of AD, we have come to this point and suggested that the AD prediction at earlier stages can be increased by using an advance deep learning techniques in different dataset (ADNI, OASIS) combining to one.

Deep learning is one of the machine learning approach which has shown promising results and performance as compare to traditional algorithms of machine learning in terms of high dimensional data of MRI brain image. In this article the application of deep learning in medical field is addressed. A thorough review of various algorithms of deep learning for diagnosis of Alzheimer’s disease is done, in which this disease is a progressive brain disorder that destroy the brain memory gradually, it is a common disease in older adults which is caused by dementia. It has been obtained in most research papers that the most widely used and represented algorithm is Convolutional Neural Networks (CNN) when it deals with brain image analysis. After study of various related papers for diagnosing of AD, we have come to this point and suggested that the AD prediction at earlier stages can be increased by using an advance deep learning techniques in different dataset (ADNI, OASIS) combining to one.

The novel Coronavirus disease has increased rapidly in the Wuhan city of China in December 2019. This fatal virus has spread across the whole world like a fire in different stages and affecting millions of population and thousands of deaths worldwide. Therefore, it is essential to classify the infected people, so that they can take the precaution in the earlier stages. Also, due to the increasing cases spread of Coronavirus, there are only limited numbers of polymerase change reaction kits available in the hospitals for testing Coronavirus patients. That why it is extremely important to develop artificial intelligence-based automatic diagnostic tools to classify the Coronavirus outbreak. The objective of this paper is to know the novel disease epidemiology, major prevention from spreading of Coronavirus Severe Acute Respiratory Syndrome, and to assess the machine and deep learning-based architectures performance that is proposed in the present year for classification of Coronavirus images such as, X-Ray and computed tomography. Specifically, advanced deep learning-based algorithms known as the Convolutional neural network, which plays a great effect on extracting highly essential features, mostly in terms of medical images. This technique, with using CT and X-Ray image scans, has been adopted in most of the recently published articles on the Coronavirus with remarkable results. Furthermore, according to this paper, this can be noted and said that deep learning technology has potential clinical applications.

Nowadays, the uses of renewable energy resources are growing rapidly. Several renewable resources such as hydro, wind, solar and tidal are commonly used all over the world. Following this trend, the implementation of large area solar arrays is considered to be necessary. Several design methodologies and approaches have been accessible in order to obtain maximum efficiency of solar panel supporting structure. In this study, Finite Element Method (FEM) was established to investigate the impact of various wind loads on the structural reliability and strength of solar panel supporting structures. The wind speeds of 20 m/s, 25 m/s, 30 m/s, 35 m/s and 40 m/s were used for the analysis of solar panel supporting structure. Wind loads were also calculated by mathematical approach. The results show that solar panel structure was significantly affected by wind loads applied on the surface of solar PV module. The results obtained from the FEM analysis that total deformation and maximum equivalent stresses were increased by increasing the wind loads. The maximum value of equivalent stress can be found at joint sections of solar panel supporting structure and total deformation is higher at centre of the structure as well as at base of the structure. The maximum and minimum value of equivalent stress 62.866 Mpa and 15.75 Mpa can be found at 40 m/s and 20 m/s respectively. Similarly, maximum and minimum value of total deformation 0.3631 mm and 0.0911 mm can be found at 40 m/s and 20 m/s respectively. The knowledge of stress distribution and deformation play a vital role for structure designs, stability and strength of hydro solar panel supporting structure. &nbsp