Xin Wang^*, Shuang Liu, Chang-Cai Zhou

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 221-234, 2023, DOI:10.32604/iasc.2023.036766

Abstract Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Currently, studies on the detection of knee OA mainly focus on X-ray images, but X-ray images are insensitive to the changes in knee OA in the early stage. Since magnetic resonance (MR) imaging can observe the early features of knee OA, the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered. Firstly, the knee MR images are preprocessed before training, including a region of interest clipping, slice selection, and data augmentation. Then the data set was divided… More >

QIAO ZHOU^1,2,3, JIAN LIU^2,4, LING XIN⁴, YANYAN FANG^2,4, LEI WAN^2,4, DAN HUANG^2,4, JINCHEN GUO¹, JIANTING WEN^2,4

BIOCELL, Vol.47, No.4, pp. 761-772, 2023, DOI:10.32604/biocell.2023.026698

Abstract The pathophysiology of osteoarthritis (OA) is multifactorial, with the primary risk factors being obesity, age, environmental variables, and genetic predisposition. The available evidence suggests that genetic diversity does not adequately account for all clinical characteristics and heterogeneity of OA. Genetics has emerged as a nascent and crucial area of research in OA. The epigenetic module presents a potential link between genetic and environmental risk factors and the susceptibility and pathogenesis of OA. As a critical epigenetic alteration, DNA methylation has been shown to have an important role in the etiology of OA and is a viable biomarker for predicting disease… More >

Sang-min Lee^*, Namgi Kim

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1981-1994, 2023, DOI:10.32604/cmc.2023.035245

Abstract Artificial intelligence technologies are being studied to provide scientific evidence in the medical field and developed for use as diagnostic tools. This study focused on deep learning models to classify degenerative arthritis into Kellgren–Lawrence grades. Specifically, degenerative arthritis was assessed by X-ray radiographic images and classified into five classes. Subsequently, the use of various deep learning models was investigated for automating the degenerative arthritis classification process. Although research on the classification of osteoarthritis using deep learning has been conducted in previous studies, only local models have been used, and an ensemble of deep learning models has never been applied to… More >

R. Kanthavel^1,*, R. Dhaya², Kanagaraj Venusamy³

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5783-5798, 2022, DOI:10.32604/cmc.2022.023745

Abstract Knee Osteoarthritis (OA) is a joint disease that is commonly observed in people around the world. Osteoarthritis commonly affects patients who are obese and those above the age of 60. A valid knee image was generated by Computed Tomography (CT). In this work, efficient segmentation of CT images using Elephant Herding Optimization (EHO) optimization is implemented. The initial stage employs, the CT image normalization and the normalized image is incited to image enhancement through histogram correlation. Consequently, the enhanced image is segmented by utilizing Niblack and Bernsen algorithm. The (EHO) optimized outcome is evaluated in two steps. The initial step… More >

R. Kanthavel^1,*, R. Dhaya²

Computer Systems Science and Engineering, Vol.42, No.1, pp. 257-269, 2022, DOI:10.32604/csse.2022.021606

Abstract Osteoarthritis is the most common class of arthritis that involves tears down the soft cartilage between the joints of the knee. The regeneration of this cartilage tissue is not possible, and thus physicians typically suggest therapeutic measures to prevent further deterioration over time. Normally, bringing about joint replacement is a remedial course of action. Expose itself in joint pain recognized with a normal X-ray. Deep learning plays a vital role in predicting the early stages of osteoarthritis by using the MRI pictures of muscles of the knee muscle. It can be used to accurately measure the shape and texture of… More >

RAFAEL SCAF DE MOLON^1,2,*, ERICA DORIGATTI DE AVILA², JONI AUGUSTO CIRELLI², JOAO PAULO STEFFENS³

BIOCELL, Vol.46, No.3, pp. 633-638, 2022, DOI:10.32604/biocell.2022.018031

Abstract The periodontium comprises all structures surrounding the teeth, including gingiva, root cementum, periodontal ligament and alveolar bone. Those tissues aim to protect and support the teeth and are challenged by a residing microbiota that leads to subclinical inflammation even in physiological conditions. Periodontitis, a prevalent multicausal inflammatory and destructive disease, develops as a result from complex host-parasite interactions. This unique physiologic and pathologic scenario enables the development of research methods which allows conclusions beyond the simple understanding of periodontal homeostasis. The aim of this viewpoint was to explore potential contributions of periodontal research to a wide array of basic science… More >

Mohamed Yacin Sikkandar^1,*, S. Sabarunisha Begum², Abdulaziz A. Alkathiry³, Mashhor Shlwan N. Alotaibi¹, Md Dilsad Manzar⁴

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4279-4291, 2022, DOI:10.32604/cmc.2022.020571

Abstract Knee Osteoarthritis (KOA) is a degenerative knee joint disease caused by ‘wear and tear’ of ligaments between the femur and tibial bones. Clinically, KOA is classified into four grades ranging from 1 to 4 based on the degradation of the ligament in between these two bones and causes suffering from impaired movement. Identifying this space between bones through the anterior view of a knee X-ray image is solely subjective and challenging. Automatic classification of this process helps in the selection of suitable treatment processes and customized knee implants. In this research, a new automatic classification of KOA images based on… More >

Sujeet More¹, Jimmy Singla¹, Oh-Young Song^2,*, Usman Tariq³, Sharaf Malebary⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3127-3143, 2021, DOI:10.32604/cmc.2021.018230

Abstract Medical Resonance Imaging (MRI) is a noninvasive, nonradioactive, and meticulous diagnostic modality capability in the field of medical imaging. However, the efficiency of MR image reconstruction is affected by its bulky image sets and slow process implementation. Therefore, to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network (SANR_CNN) for eliminating noise and improving the MR image reconstruction quality. The proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality, and SARN algorithm is used for building a dictionary learning technique… More >

ZHIPING WEI^1,2, YAJUN LIU¹, MEIWEN YANG³, MENGDI LI¹, KEXIN LI¹, LUXI ZHENG¹, HUIQIONG GUO¹, FENFANG HONG^4,*, SHULONG YANG^1,*

BIOCELL, Vol.45, No.4, pp. 1005-1012, 2021, DOI:10.32604/biocell.2021.014581

Abstract Background and Objective: LTB4 has been shown to be involved in rheumatoid arthritis (RA) pathogenesis. The effect of Dioscin(Dio) on the LTB4 pathway of RA have not been reported yet. This study aimed at further exploring whether Dioscin’s effects on TNF-α induced collagen-induced arthritis (CIA) rat fibroblast-like synoviocytes (FLS) connected with the LTB4 and its receptor pathway. Materials & Methods: In this experiment, control group, TNF-α group, and different concentrations of Dioscin groups were established. Cell viability was evaluated using MTT assay. The levels of LTB4 in the samples of above groups were measured using ELISA. The mRNA expression levels… More >

Mai Ramadan Ibraheem¹, Jilan Adel², Alaa Eldin Balbaa³, Shaker El-Sappagh⁴, Tamer Abuhmed^5,*, Mohammed Elmogy⁶

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 393-409, 2021, DOI:10.32604/cmc.2021.014446

Abstract Surface electromyogram (sEMG) processing and classification can assist neurophysiological standardization and evaluation and provide habitational detection. The timing of muscle activation is critical in determining various medical conditions when looking at sEMG signals. Understanding muscle activation timing allows identification of muscle locations and feature validation for precise modeling. This work aims to develop a predictive model to investigate and interpret Patellofemoral (PF) osteoarthritis based on features extracted from the sEMG signal using pattern classification. To this end, sEMG signals were acquired from five core muscles over about 200 reads from healthy adult patients while they were going upstairs. Onset, offset,… More >