Abdul Haseeb¹, Muhammad Attique Khan^1,*, Faheem Shehzad¹, Majed Alhaisoni², Junaid Ali Khan¹, Taerang Kim³, Jae-Hyuk Cha³

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2397-2415, 2023, DOI:10.32604/csse.2023.040529

Abstract X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost. However, the manual categorization of knee joint disorders is time-consuming, requires an expert person, and is costly. This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm. Two pre-trained deep learning models (Efficientnet-b0 and Densenet201) have been employed for the training and feature extraction. Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images. In the next step, fusion is performed using a canonical… More >

Abdul Qadir¹, Rabbia Mahum¹, Suliman Aladhadh^2,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1365-1384, 2023, DOI:10.32604/csse.2023.037033

Abstract A considerable portion of the population now experiences osteoarthritis of the knee, spine, and hip due to lifestyle changes. Therefore, early treatment, recognition and prevention are essential to reduce damage; nevertheless, this time-consuming activity necessitates a variety of tests and in-depth analysis by physicians. To overcome the existing challenges in the early detection of Knee Osteoarthritis (KOA), an effective automated technique, prompt recognition, and correct categorization are required. This work suggests a method based on an improved deep learning algorithm that makes use of data from the knee images after segmentation to detect KOA and its severity using the Kellgren-Lawrence… More >

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 >

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 >

Jingying Lu¹, Datao Xu¹, Wenjing Quan^1,2, Julien S. Baker³, Yaodong Gu^1,*

Molecular & Cellular Biomechanics, Vol.19, No.2, pp. 61-75, 2022, DOI:10.32604/mcb.2022.019978

Abstract Objectives: Although overuse running injury risks for the ankle and knee are high, the effect of different shoe designs on Achilles tendon force (ATF) and Patellofemoral joint contact force (PTF) loading rates are unclear. Therefore, the primary objective of this study was to compare the ATF at the ankle and the PTF and Patellofemoral joint stress force (PP) at the knee using different running shoe designs (forefoot shoes vs. normal shoes). Methods: Fourteen healthy recreational male runners were recruited to run over a force plate under two shoe conditions (forefoot shoes vs. normal shoes). Sagittal plane ankle and knee kinematics… More >

Gang He^*

Molecular & Cellular Biomechanics, Vol.19, No.1, pp. 41-50, 2022, DOI:10.32604/mcb.2022.017044

Abstract This study aims to analyze the biomechanical difference between the two legs of male badminton players when they land on one leg, thereby providing some guidance for preventing sports injury. Ten male badminton players were selected as the subjects. They did the single-leg landing movement successfully three times. The kinematic data were obtained by the Vicon infrared high-speed motion capture system. The kinetic data were obtained by the KISTLER three-dimensional forcing measuring platform. The data were processed and analyzed. The center of gravity of the right leg on the X and Y axes were 0.25 ± 0.05 and 0.21 ± 0.04 m, respectively, which were… 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 >

Ali Murtaza¹, Muhammad Usman Qadir¹, Muhammad Awais Khan¹, Izhar ul Haq^1,*, Kamran Shah¹, Nizar Akhtar²

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1887-1899, 2022, DOI:10.32604/iasc.2022.020006

Abstract Transfemoral prostheses have evolved from mechanical devices to microprocessor-based, electronically controlled knee joints, allowing amputees to regain control of their limbs. For improved amputee experience at varying ambulation rates, these devices provide controlled damping throughout the swing and stance phases of the gait cycle. Commercially available microprocessor-based prosthetic knee (MPK) joints use linear controllers, heuristic-based methods, and finite state machine based algorithms to track the refence gait cycle. However, since the amputee experiences a variety of non-linearities during ambulation, such as uneven terrains, walking backwards and climbing stairs, therefore, traditional controllers produces error, abnormal movements, unstable control system and require… More >

Jing Sun^1,3,*, Youting Lin², Yangyang Fan⁴, Samantha Ferguson³, Nicholas Buys³, Minyan Sun²

International Journal of Mental Health Promotion, Vol.23, No.3, pp. 417-427, 2021, DOI:10.32604/IJMHP.2021.013098

Abstract Knee instability as a symptom of ligament injury usually only receives attention when it causes pain or impacts patients’ mobility in China. In this study both the physical and psychosocial impact of an innovative approach to treatment, Half-cut Wood Training, was examined. Twenty individuals with knee instability who received Halfcut Wood Training (Intervention group) and twenty two individuals with knee instability who did not receive Half-cut Wood Training (as Control group) participated in the study. The electric WIFI based HBF-306 was used to collect the anthropometry and biomedical data. Symptom severity was assessed by a doctor and through response to… More >

Belayat Hossain^a, Takatoshi Morooka^b, Makiko Okuno^b, Manabu Nii^a, Shinichi Yoshiya^b, Syoji Kobashi^a

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 105-115, 2019, DOI:10.31209/2018.100000034

Abstract This work aimed to predict postoperative knee functions of a new patient prior to total knee arthroplasty (TKA) surgery using machine learning, because such prediction is essential for surgical planning and for patients to better understand the TKA outcome. However, the main difficulty is to determine the relationships among individual varieties of preoperative and postoperative knee kinematics. The problem was solved by constructing predictive models from the knee kinematics data of 35 osteoarthritis patients, operated by posterior stabilized implant, based on generalized linear regression (GLR) analysis. Two prediction methods (without and with principal component analysis followed by GLR) along with… More >