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Knee Pain Running: Causes & How To Treat It
By Rabbia Mahum 1 , Saeed Ur Rehman 2 , Talha Meraj 2 , Hafiz Tayyab Rauf 3, * , Aun Irtaza 1 , Ahmed M. El-Sherbeeny 4 and Mohammed A. El-Meligy 4
In the recent era, various diseases have severely affected the lifestyle of individuals, especially adults. Among these, bone diseases, including Knee Osteoarthritis (KOA), have a great impact on quality of life. KOA is a knee joint problem mainly produced due to decreased Articular Cartilage between femur and tibia bones, producing severe joint pain, effusion, joint movement constraints and gait anomalies. To address these issues, this study presents a novel KOA detection at early stages using deep learning-based feature extraction and classification. Firstly, the input X-ray images are preprocessed, and then the Region of Interest (ROI) is extracted through segmentation. Secondly, features are extracted from preprocessed X-ray images containing knee joint space width using hybrid feature descriptors such as Convolutional Neural Network (CNN) through Local Binary Patterns (LBP) and CNN using Histogram of oriented gradient (HOG). Low-level features are computed by HOG, while texture features are computed employing the LBP descriptor. Lastly, multi-class classifiers, that is, Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbour (KNN), are used for the classification of KOA according to the Kellgren–Lawrence (KL) system. The Kellgren–Lawrence system consists of Grade I, Grade II, Grade III, and Grade IV. Experimental evaluation is performed on various combinations of the proposed framework. The experimental results show that the HOG features descriptor provides approximately 97% accuracy for the early detection and classification of KOA for all four grades of KL.
Osteoarthritis (OA) is a severe disease in joints, especially in the knees, due to loss of cartilage. It appears with age, and it is present mostly in the elderly population. Overweight is also among the various causes of the prevalence of OA [1, 2]. The Knee joint consists of two major bones, the femur and the tibia. Between these bones, a thick material called cartilage is present. This cartilage helps with the flexible and frictionless movement of the knee. Cartilage volume may decrease due to aging or accidental loss [3]. Due to decreased cartilage volume, tibiofemoral bones produce friction during movement, leading to knee osteoarthritis (KOA). Articular cartilage is composed of a chondrocyte that helps the underlying bone by load distribution, and it works for a lifetime [4].
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Kellgren–Lawrence (KL) is a grading system that describes the various stages of OA. This system is based on the radiographic classification of KOA. It is found to be the most authoritative system of classification. It consists of Grade I, Grade II, Grade III, and Grade IV [5]. Early symptoms that indicate KOA in patients are knee pain, swelling, surface roughness, gait abnormalities, morning pain, and so forth. From these factors, doctors detect the presence of the disease. Although KOA is detected below the age of forty years, the average age of the patients has been reported to be above forty-five years [6]. According to a recent study, 80% of people over the age of 65 have radiographic KOA in the USA [5]. It is expected that the ratio will increase in the future. Another study has stated that KOA affects more than 21 million people in the USA [7]. In Indonesia, 65% of total arthritis cases are knee osteoarthritis [8]. In Asia, it is also increasing day by day. According to a recent study conducted in Pakistan, 28% of the urban and 25% of the rural population is affected by knee osteoarthritis [9]. Clinically, along with medication, KOA is cured by exercise, weight loss, walking aids, heat and ice treatment, and physiotherapy as non-invasive methods and acupuncture, intra-articular injection, and surgical procedures as invasive methods of treatment [10].
Image processing is a computer-aided technique that is used for KOA detection. Various modalities, such as radiography, MRI, gait analysis, bioelectric impedance signals, and so forth, are used for the detection of KOA [11, 12]. X-rays/radiographic images help to detect knee osteophytes and joint width space narrowing, while MRI is helpful for cartilage thickness detection, surface area, and roughness. In contrast, bioelectric impedance signals are a powerful tool for the detection of KOA. As it is a non-invasive technique, it is low cost and easy to operate. It involves the recording of electrical signals around the knee. Later, these signals are used for the analysis and detection of KOA [13]. Radiography is a simple and cheap procedure for the detection of KOA. Through it, we can see the joint space width easily. It is used almost everywhere in the world as it is a cheap modality. However, it has a limitation in that we cannot see the details of the image, and it does not provide any information for the early detection of KOA [13]. The MRI technique is more advanced than radiography in the detection of the morphological features of the knee. It provides an in-depth image of the structure and formation of the knee. We can obtain useful information using image processing techniques on MR images. However, it is costlier than Radiography and can be more useful [13, 14]. The image processing techniques, such as segmentation, thresholding, masking, edge detection, contrast enhancement, and so forth, are applied for obtaining the required data from the images.
Various machine learning and deep learning techniques have been used for the detection of KOA using images of radiography [2, 3, 15, 16, 17, 18]. Deep learning algorithms are usefulness in various domains such as for mission-critical applications [19, 20], semantic segmentation [21], medical, that is, real-time cardiovascular Magnetic Resonance [22], and ecosystems change analysis [23]. Deep learning algorithms perform very well in the medical field. However, deep learning techniques did not perform well for KOA classification using radiographic images. Although these algorithms performed well for binary classification among OA and non-OA images with an accuracy of 92% but for multi-classification the accuracy was 66.7% [15].
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Therefore, this study proposes a novel technique for KOA detection according to the KL grading system. The technique uses a hybrid approach for feature extraction, and classification is performed with three different multi-class algorithms—SVM, KNN, and Random Forest. The result for the KNN classifier is better than that of the others.
The remaining sections of papers are organized as follows: Section 1 refers to the Introduction, Section 2 refers to the Literature Review, Section 3 refers to the Proposed Methodology, and Section 4 and Section 5 refer to the Experimental evaluation and the Conclusion.
OA is a common joint disorder. It appears with aging and also due to wear and tear on joints. Overweight persons have an increased risk of OA in different joints [1]. Osteoarthritis causes the degradation of articular cartilage, which is a flexible coating between the knee bones. OA causes mechanical abnormalities of the knee and hips. In this method, gait analysis is performed to predict joint deterioration [24]. Joint mechanics and function are based on the efficient working of menisci. These menisci enable load balancing at tibia-femoral bones. It also facilitates articular cartilage by reducing the load on it. The lubrication and distribution of synovial fluids are also regulated and affected by menisci [25]. There are two types of material from which knee bone is made; one type of material is known as Cancellous or Trabecular (Spongy) bone, and the other is known as Cortical (compact) bone [26]. The bone has different shapes; some bones are long, some are short, some are flat, and other bone shapes are irregular [27]. They have presented a Layered graph approach for optimal segmentation. It can be applied on single and multiple interacting surfaces [28].
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Mosaicplasty is a self-cartilage transplantation method. In the case of knee cartilage damage, it is one of the remedies. It requires 3D image precision [29]. Osteoarthritis and rheumatoid arthritis are other widespread diseases that are inclined to cause effusion. Even situations, such as gout or the formation of tumors and cysts, can trigger fluid keeping in and around the knee. A fully automated segmentation technique is used. This technique uses MR images and is applied for the detection of osteoarthritis of the knee [14, 30]. Image processing techniques, such as histogram quantization,
Therefore, this study proposes a novel technique for KOA detection according to the KL grading system. The technique uses a hybrid approach for feature extraction, and classification is performed with three different multi-class algorithms—SVM, KNN, and Random Forest. The result for the KNN classifier is better than that of the others.
The remaining sections of papers are organized as follows: Section 1 refers to the Introduction, Section 2 refers to the Literature Review, Section 3 refers to the Proposed Methodology, and Section 4 and Section 5 refer to the Experimental evaluation and the Conclusion.
OA is a common joint disorder. It appears with aging and also due to wear and tear on joints. Overweight persons have an increased risk of OA in different joints [1]. Osteoarthritis causes the degradation of articular cartilage, which is a flexible coating between the knee bones. OA causes mechanical abnormalities of the knee and hips. In this method, gait analysis is performed to predict joint deterioration [24]. Joint mechanics and function are based on the efficient working of menisci. These menisci enable load balancing at tibia-femoral bones. It also facilitates articular cartilage by reducing the load on it. The lubrication and distribution of synovial fluids are also regulated and affected by menisci [25]. There are two types of material from which knee bone is made; one type of material is known as Cancellous or Trabecular (Spongy) bone, and the other is known as Cortical (compact) bone [26]. The bone has different shapes; some bones are long, some are short, some are flat, and other bone shapes are irregular [27]. They have presented a Layered graph approach for optimal segmentation. It can be applied on single and multiple interacting surfaces [28].
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Mosaicplasty is a self-cartilage transplantation method. In the case of knee cartilage damage, it is one of the remedies. It requires 3D image precision [29]. Osteoarthritis and rheumatoid arthritis are other widespread diseases that are inclined to cause effusion. Even situations, such as gout or the formation of tumors and cysts, can trigger fluid keeping in and around the knee. A fully automated segmentation technique is used. This technique uses MR images and is applied for the detection of osteoarthritis of the knee [14, 30]. Image processing techniques, such as histogram quantization,
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