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

Abstract

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.

Assessing the efficacy and safety of Juan Bi Tang for dialysis-related myofascial pain in the fistula arm: Study protocol for a randomized cross-over trial

“…The strengths of this study include: (1) This study is the first pilot randomized cross-over trial to evaluate the feasibility of JBT for the treatment of dialysis-related myofascial pain. (2) The design of the trial is based on TCM syndromes because dialysis-relayed myofascial pain can be inferred to inadequate qi and blood around the fistula arm in patients receiving hemodialysis.…”

“…Myofascial pain syndrome (MPS) is characterized by localized pain (1), paresthesia, exquisite tenderness, restricted range of motion, and hypersensitivity at specific anatomic sites, which are termed taut bands with active myofascial trigger points (MTrPs) (2)(3)(4). According to the International Association for the Study of Pain and the American Academy of Pain Medicine, the essential criteria for the diagnosis of MPS are hypersensitive spots that cause local pain and symptoms that can be recreated by palpation (5).…”

A fused biometrics information graph convolutional neural network for effective classification of patellofemoral pain syndrome

“…In order to further confirm the performance of our method, we compare it against the following five methods: Extreme Learning Machines (ELM), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Back Propagation Neural Network (BP) and Long Short-Term Memory (LSTM) (Shi et al, 2019;Shi et al, 2021b). Among them, the kernel function of the SVM is linear, the C-value is set to 0.04.…”

“…models is set to 0.01 (Shi et al, 2021b). Each experiment was performed with a fivefold cross validation method and the experiment was repeated 50 times to work out the mean and the standard deviation.…”