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Osteolysis is one of the most prominent reasons of revision surgeries in total joint arthroplasty. This biological phenomenon is induced by wear particles and corrosion products that stimulate inflammatory biological response of surrounding tissues. The eventual responses of osteolysis are the activation of macrophages leading to bone resorption and prosthesis failure. Various factors are involved in the initiation of osteolysis from biological issues, design, material specifications, and model of the prosthesis to the health condition of the patient. Nevertheless, the factors leading to osteolysis are sometimes preventable. Changes in implant design and polyethylene manufacturing are striving to improve overall wear. Osteolysis is clinically asymptomatic and can be diagnosed and analyzed during follow-up sessions through various imaging modalities and methods, such as serial radiographic, CT scan, MRI, and image processing-based methods, especially with the use of artificial neural network algorithms. Deep learning algorithms with a variety of neural network structures such as CNN, U-Net, and Seg-UNet have proved to be efficient algorithms for medical image processing specifically in the field of orthopedics for the detection and segmentation of tumors. These deep learning algorithms can effectively detect and analyze osteolytic lesions well in advance during follow-up sessions in order to administer proper treatments before reaching a critical point. Osteolysis can be treated surgically or nonsurgically with medications. However, revision surgeries are the only solution for the progressive osteolysis. In this literature review, the underlying causes, mechanisms, and treatments of osteolysis are discussed with the main focus on the possible computer-based methods and algorithms that can be effectively employed for the detection of osteolysis. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy c -means clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8 ± 0.3 mm, and volume overlap error (VOE) of 5.6 ± 1.2 %. [ABSTRACT FROM AUTHOR]

The effects of different parameters including nanoparticles mass fraction and temperature were investigated on rheological behavior and surface tension of iron(II) oxide/light crude oil nanofluid. Iron(II) oxide was dispersed in light crude oil by using ultrasonic processor. TEM images was provided in order to assess the size and morphology of iron(II) oxide nanoparticles. In addition, DLS analysis and Zeta potential test were performed on nanofluid for estimation of nanoparticles size distribution within the basefluid and stability of nanoparticles, respectively. The results of this study showed that for iron(II) oxide/light crude oil nanofluid the value of surface tension reach to its minimum value at the condition where nanoparticles mass fraction was chosen to be 2.0 wt% and temperature was set on 70 °C. These results showed that for iron(II) oxide/light crude oil nanofluid the rheological behavior of nanofluid is non-Newtonian at temperature of 40 °C and the suspension behave as a rheoplexy fluid in which by increasing the shear rate higher dynamic viscosity of nanofluid observed. Furthermore, nanofluid behaves as a Newtonian fluid for the temperature of higher than 55 °C. Finally, a comprehensive correlation was obtained for estimation of relative dynamic viscosity of nanofluid by hybrid group method of data handling (GMDH)-type neural network method. The correlation presented in this study shows that for the relative dynamic viscosity of iron(II) oxide/light crude oil as a function of nanoparticles mass fraction and temperature, the amount of the total deviation of calculated data from experimental values is less than 10%. • A comprehensive method of hybrid GMDH-type neural network. • Predict surface tension, shear stress, viscosity & sensitivity analysis. • Empirical data of iron (II) oxide nanoparticles in light crude oil mixture. [ABSTRACT FROM AUTHOR]