Przemysław Krakowski

The meniscus is critical for load distribution and joint stability; tears disrupt biomechanics and accelerate cartilage degeneration, prompting a shift from meniscectomy toward preservation and repair. We review three core techniques – inside-out, outside-in, and all-inside – with stepwise descriptions, indications, advantages/limitations, devices, and complication profiles. Inside-out offers precise, strong fixation but requires an accessory incision and neurovascular precautions; outside-in is reproducible and cost-efficient for anterior horn tears; and all-inside reduces soft-tissue dissection and operative time, improving access to posterior/body regions. No single meniscal repair technique demonstrates clear superiority in healing or failure rates; outcomes depend on tear characteristics and patient factors. Although modern all-suture all-inside devices show favorable biomechanics, their higher cost and unclear cost-effectiveness limit definitive clinical advantage. Technical pearls include optimized portal placement, peripheral bites, protection of neurovascular structures, and avoidance of soft-tissue bridges. Concomitant ACL reconstruction enhances healing – likely via improved stability and marrow-derived biologic augmentation. Region-specific guidance is provided using medial and lateral anatomical zoning, aligning suture patterns (inside-out, outside-in, and all-inside) to tear location to preserve biomechanics and reduce complications.

Osteoarthritis (OA) is a severe and progressive joint disease that usually affects elderly people. The consequence of this disease in its advanced stage is the need for total knee arthroplasty (TKA). Over the years, there has been a constant increase in the number of TKA procedures, with a predicted increase to 1.26 million procedures by 2030. Diagnostics are based on conventional radiography, although advanced techniques such as radiostereometry, SPECT/CT and PET/CT, which enable early detection of micromigration, are gaining increasing recognition. Vibroarthrography (VAG) is a proposed supplement to diagnostics, enabling the assessment of the characteristics of vibrations and friction of joint surfaces, thus supporting the process of early detection of endoprosthesis instability. The combination of conventional and alternative diagnostic methods, including vibroarthrography, may improve the detection of early TKA loosening. This may also result in increased implant durability. The aim of this article is to review the current state of knowledge on the classification and analysis of endoprosthesis loosening mechanisms. In addition, classic and modern methods of detecting and monitoring loosening are discussed, with particular emphasis on vibroarthrography as a potential tool for early diagnosis.

This study proposes a hybrid analytical framework for detecting chondromalacia using vibroacoustic (VAG) signals from patients with knee osteoarthritis (OA) and healthy controls (HCs). The methodology combines nonlinear signal decomposition, feature extraction, and deep learning classification. Raw VAG signals, recorded with a custom multi-sensor system during open (OKC) and closed (CKC) kinetic chain knee flexion–extension, underwent preprocessing (denoising, segmentation, normalization). Ensemble Empirical Mode Decomposition (EEMD) was used to isolate Intrinsic Mode Functions (IMFs), and Detrended Fluctuation Analysis (DFA) computed local (α₁) and global (α₂) scaling exponents as well as breakpoint location. Frequency–energy features of IMFs were statistically assessed and selected via Neighborhood Component Analysis (NCA) for support vector machine (SVM) classification. Additionally, reconstructed α₁/α₂-based signals and raw signals were converted into continuous wavelet transform (CWT) scalograms, classified with convolutional neural networks (CNNs) at two resolutions. The SVM approach achieved the best performance in CKC conditions (accuracy 0.87, AUC 0.91). CNN classification on CWT scalograms also demonstrated robust OA/HC discrimination with acceptable computational times at higher resolutions. Results suggest that combining multiscale decomposition, nonlinear fluctuation analysis, and deep learning enables accurate, non-invasive detection of cartilage degeneration, with potential for early knee pathology diagnosis.

Articular cartilage (AC) plays an important role in the biomechanics of synovial joints. Its task is to enable smooth movement and transfer of mechanical loads with minimised friction. AC is characterised by unique mechanical properties resulting from its complex structure, in which the dominant components are type II collagen, proteoglycans and water. Healthy articular cartilage shows elasticity in compression, viscoelastic properties, and the ability to relax stresses under the influence of cyclic loads. In response to different loading modes, it shows anisotropic and non-uniform behaviour, which translates into its cushioning and protective function for the subchondral bone. Significant changes occur in the structure and mechanical properties of cartilage with age as a result of mechanical overload or degenerative diseases, such as osteoarthritis. This results in a deterioration of the cushioning and mechanical function, which leads to progressive degradation of joint tissues. Understanding the mechanical properties of AC is crucial for developing effective diagnostic methods. Analysis of changes in mechanical properties contributes to the early detection of pathological changes. The aim of this paper is to review the current state of knowledge regarding the structure and biomechanical properties of articular cartilage, and to analyse conventional and alternative diagnostic methods in the context of their suitability for assessing the state of AC, particularly in the early stages of degenerative processes.

This study focuses on the diagnostic analysis of cartilage damage in the knee joint based on acoustic signals generated by the joint. The research utilizes a combination of advanced signal processing techniques, specifically empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA), alongside convolutional neural networks (CNNs) for classification and detection tasks. Acoustic signals, often reflecting the mechanical behavior of the joint during movement, serve as a non-invasive diagnostic tool for assessing the cartilage condition. EEMD is applied to decompose the signals into intrinsic mode functions (IMFs), which are then analyzed using DFA to quantify the scaling properties and detect irregularities indicative of cartilage damage. The separation of individual frequency components allows for multi-scale analysis of the signals, with each of the functions resulting from the analysis reflecting local variations in the amplitude and frequency over time and allowing for effective removal of noise present in the signal. The CNN model is trained on features extracted from these signals to accurately classify different stages of cartilage degeneration. The proposed method demonstrates the potential for early detection of knee joint pathology, providing a valuable tool for preventive healthcare and reducing the need for invasive diagnostic procedures. The results suggest that the combination of EEMD-DFA for feature extraction and CNN for classification offers a promising approach for the non-invasive assessment of cartilage damage.

The ageing population and the resulting number of physical and health problems are now a major social and economic challenge around the world. Osteoarthritis is a common disease among older people. It can affect any joint, but it most often affects the knee, hip, and hand joints. Osteoarthritis of the knee joint significantly affects everyday life, limiting daily activities. Patients affected by this disease face many ailments, such as pain, stiffness, and a reduced of range of joint motion. In order to implement quick and effective treatment and prevent the development of the disease, accurate and early diagnosis is important. This will contribute to prolonging the health of the joints. Available methods for diagnosing osteoarthritis include conventional radiography, MRI, and ultrasound, but these methods are not suitable for screening. Over the years, there have been proposals to use vibroarthrography as a new, cheap, and noninvasive screening method for cartilage damage. The paper reviews recent studies on vibroarthrography as a diagnostic method for knee osteoarthritis. The aim of the study is to organise the current knowledge regarding the diagnosis of osteoarthritis of the knee joint and vibroarthrography as a proposal for a new diagnostic method.

Nowadays, the world is struggling with the problems of an aging society. With the increasing share of older people in the population, degenerative joint diseases are a growing problem. The result of progressive degenerative changes in joints is primarily the deterioration of the patients' quality...

Osteoarthritis (OA) is one of the most common causes of disability around the globe, especially in aging populations. The main symptoms of OA are pain and loss of motion and function of the affected joint. Hyaline cartilage has limited ability for regeneration due to its avascularity, lack of nerve endings, and very slow metabolism. Total joint replacement (TJR) has to date been used as the treatment of end-stage disease. Various joint-sparing alternatives, including conservative and surgical treatment, have been proposed in the literature; however, no treatment to date has been fully successful in restoring hyaline cartilage. The mechanical and frictional properties of the cartilage are of paramount importance in terms of cartilage resistance to continuous loading. OA causes numerous changes in the macro- and microstructure of cartilage, affecting its mechanical properties. Increased friction and reduced load-bearing capability of the cartilage accelerate further degradation of tissue by exerting increased loads on the healthy surrounding tissues. Cartilage repair techniques aim to restore function and reduce pain in the affected joint. Numerous studies have investigated the biological aspects of OA progression and cartilage repair techniques. However, the mechanical properties of cartilage repair techniques are of vital importance and must be addressed too. This review, therefore, addresses the mechanical and frictional properties of articular cartilage and its changes during OA, and it summarizes the mechanical outcomes of cartilage repair techniques.

Osteoarthritis is one of the leading causes of disability around the globe. Up to this date there is no definite cure for cartilage lesions. Only fast and accurate diagnosis enables prolonging joint survivor time. Available diagnostic methods have disadvantages such as high price, radiation, need for experienced radiologists or low availability in some regions. The present study evaluates the use of vibroarthorgraphy as a method of cartilage lesion detection. 47 patients with diagnosed cartilage lesions, and 51 healthy control group patients have been enrolled in this study. The cartilage in the study group was evaluated intraoperatively by experienced orthopaedic surgeon. Signal acquisition was performed in open and closed kinematic chain based on 10 knee joint movements from 0-90 degrees. By using EEMD-DFA algorithms, reducing classifier inputs using ANOVA and then classifying using artificial neural networks (ANN), a classification accuracy of almost 93% was achieved. A sensitivity of 0.93 and a specificity of 0.93 with an AUC of 0.942 were obtained for the multilayer perceptron network. These results allow to apply this testing protocol in a clinical setting in the future.