A way in determination of patellar position: Ligamentum patellae angle and a neural network application

MODELING OF PATELLA HEIGHT WITH DISTAL FEMUR AND PROXIMAL TIBIA REFERENCE POINTS WITH ARTIFICIAL NEURAL NETWORK

The patellofemoral joint is one of the parts of the knee extension mechanism that plays a role in the stability of the knee by enlarging the force arm of the quadriceps muscle and changing the direction of the muscle strength. For the entire knee joint to perform its task painlessly and functionally, the positions and strength of the muscles, the strength of the ligaments, and their reaction to movement must be compatible. The Insall–Salvati (Ins-Sal) index is useful for showing changes in patellar height produced by repositioning the tibial plateau, in other words, showing changes in patellar tendon length. Patella height is an important value to be taken into account in knee prosthesis surgery, tibial osteotomy, and anterior cruciate ligament reconstruction. The morphometric relationship between the reference measurements of the distal femur and proximal tibia and the position of the patella will be useful in determining the natural anatomy. In this study, we aimed to determine the relationship between patella height and distal femur and proximal tibia reference areas by using the artificial neural network method as an alternative approach method. In order to assess the performance of the estimation of the Ins-Sal index, the four ANN model with six input combinations which included age, gender and the reference measurements for the right and left sides have been constructed and tested. The MSE and [Formula: see text] values are calculated for every four models for the training and test phase. The results show that the proposed approach for modeling of relation between reference measurements and the Ins-Sal index is a powerful approach.

Auxiliary Diagnostic Method for Patellofemoral Pain Syndrome Based on One-Dimensional Convolutional Neural Network

Early accurate diagnosis of patellofemoral pain syndrome (PFPS) is important to prevent the further development of the disease. However, traditional diagnostic methods for PFPS mostly rely on the subjective experience of doctors and subjective feelings of the patient, which do not have an accurate-unified standard, and the clinical accuracy is not high. With the development of artificial intelligence technology, artificial neural networks are increasingly applied in medical treatment to assist doctors in diagnosis, but selecting a suitable neural network model must be considered. In this paper, an intelligent diagnostic method for PFPS was proposed on the basis of a one-dimensional convolutional neural network (1D CNN), which used surface electromyography (sEMG) signals and lower limb joint angles as inputs, and discussed the model from three aspects, namely, accuracy, interpretability, and practicability. This article utilized the running and walking data of 41 subjects at their selected speed, including 26 PFPS patients (16 females and 10 males) and 16 painless controls (8 females and 7 males). In the proposed method, the knee flexion angle, hip flexion angle, ankle dorsiflexion angle, and sEMG signals of the seven muscles around the knee of three different data sets (walking data set, running data set, and walking and running mixed data set) were used as input of the 1D CNN. Focal loss function was introduced to the network to solve the problem of imbalance between positive and negative samples in the data set and make the network focus on learning the difficult-to-predict samples. Meanwhile, the attention mechanism was added to the network to observe the dimension feature that the network pays more attention to, thereby increasing the interpretability of the model. Finally, the depth features extracted by 1D CNN were combined with the traditional gender features to improve the accuracy of the model. After verification, the 1D CNN had the best performance on the running data set (accuracy = 92.4%, sensitivity = 97%, specificity = 84%). Compared with other methods, this method could provide new ideas for the development of models that assisted doctors in diagnosing PFPS without using complex biomechanical modeling and with high objective accuracy.