Background and Aim: Researches had shown that the high levels of shear and compression stresses that appear in the articular cartilage after meniscectomy are partly responsible for cartilage pathologies, such as osteoarthrosis . In this study, we probe to determine the stress distribution of the medial and lateral meniscus and to choose the appropriate region of meniscectomy.

Materials and Methods: Biomechanical 3D finite element model of the knee joint was generated from CT-scan images. Mimics modeled the bony structure of knee and Solidworks developed the medial and lateral meniscus.

Results: Under an axial femoral compressive load, the maximal contact stress in the articular cartilage after meniscectomy was about twice that of a healthy joint. The maximal contact pressures took place in the posterior region of the medial meniscus, with average values of 1.622 MPa and in the anterior horn of the lateral menisci with 1.159 MPa.

Results: Critical regions determine the allowed region of menisectomy for surgeon. Not only stress distribution doesn`t change by increasing of body weight or meniscectomy, but also the rate of stress increase after meniscectomy.

Background and Aim: Knee joint injuries are the most common injuries in routine life and stirring sports. The most common injuries in knee joint are meniscus injuries, anterior cruciate ligament rupture and kind of tears of above structures. Diagnosis of meniscus tear is generally clinically and by magnetic resonance imaging (MRI: Magnetic Resonance Imaging). In this study, meniscuc tear was recognized by recorded the knee vibration signals (VAG: Vibroarthrography).

Materials and Methods: Forty subjects (20 normal and 20 abnormal) with meniscus tear were selected and recorded the signals by electrostethoscope, 3 times in 15 sec. Testimonial form was taken from all of participants. 

Results: After recording, the signals were processed and reduced the noise by singular value decomposition algorithm (SVD: Singular Value Decomposition), four parameters of these signals were extracted in energy and frequency domain. These were included energy parameter (EP: Energy Parameter), energy spread parameter (ESP: Energy Spread Prameter), frequency parameter (FP: Frequency Parameter) and frequency spread parameter (FSP: Frequency Spread Parameter). Mean and standard deviation of each feature were considered and analyzed eight features of the signals. Statistical analyzes showed the P-Value less than 0.05 ( ) for each feature. Three methods for data classification were used. ) Mean and standard deviation of the parameters were obtained as below: Multi Layer Perceptron (MLP: Multi Layer Perceptron), Support Vector Machine (SVM: Support Vector Machine) and K- Nearest Neighbor (KNN: K-Nearest Neighbor) with ( ), ( ) and ( . K-nearest neighbor method (K=5) has the highest percentage of accuracy.

Conclusion: Knee signals processing (VAG signals) is a suitable and non-invasive method for diagnosis of meniscus tear which can save the time and reduce the costs.

Keywords: Articular pathology, Meniscus tear, VAG signals, Singular Value Decomposition (SVD), Time- frequency distribution.