Feasibility of a Support Vector Machine Classifier for Myofascial Pain Syndrome: Diagnostic Case-Control Study

Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research

OBJECTIVE

The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management.

METHODS

A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used.

RESULTS

After the screening process, 60 studies were reviewed, highlighting AI's effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP.

CONCLUSION

The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures.

SIGNIFICANCE

This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization.

Ultrasound-Based Radiomics for the Classification of Henoch-Schönlein Purpura Nephritis in Children

Henoch-Schönlein purpura nephritis (HSPN) is one of the most common kidney diseases in children. The current diagnosis and classification of HSPN depend on pathological biopsy, which is seriously limited by its invasive and high-risk nature. The aim of the study was to explore the potential of radiomics model for evaluating the histopathological classification of HSPN based on the ultrasound (US) images. A total of 440 patients with Henoch-Schönlein purpura nephritis proved by biopsy were analyzed retrospectively. They were grouped according to two histopathological categories: those without glomerular crescent formation (ISKDC grades I-II) and those with glomerular crescent formation (ISKDC grades III-V). The patients were randomly assigned to either a training cohort (n = 308) or a validation cohort (n = 132) with a ratio of 7:3. The sonologist manually drew the regions of interest (ROI) on the ultrasound images of the right kidney including the cortex and medulla. Then, the ultrasound radiomics features were extracted using the Pyradiomics package. The dimensions of radiomics features were reduced by Spearman correlation coefficients and least absolute shrinkage and selection operator (LASSO) method. Finally, three radiomics models using k-nearest neighbor (KNN), logistic regression (LR), and support vector machine (SVM) were established, respectively. The predictive performance of such classifiers was assessed with receiver operating characteristic (ROC) curve. 105 radiomics features were extracted from derived US images of each patient and 14 features were ultimately selected for the machine learning analysis. Three machine learning models including k-nearest neighbor (KNN), logistic regression (LR), and support vector machine (SVM) were established for HSPN classification. Of the three classifiers, the SVM classifier performed the best in the validation cohort [area under the curve (AUC) =0.870 (95% CI, 0.795-0.944), sensitivity = 0.706, specificity = 0.950]. The US-based radiomics had good predictive value for HSPN classification, which can be served as a noninvasive tool to evaluate the severity of renal pathology and crescentic formation in children with HSPN.

Myofascial Trigger Point Identification in B-Mode Ultrasound: Texture Analysis Versus a Convolutional Neural Network Approach

OBJECTIVE

Myofascial pain syndrome (MPS) is one of the most common causes of chronic pain and affects a large portion of patients seen in specialty pain centers as well as primary care clinics. Diagnosis of MPS relies heavily on a clinician's ability to identify the presence of a myofascial trigger point (MTrP). Ultrasound can help, but requires the user to be experienced in ultrasound. Thus, this study investigates the use of texture features and deep learning strategies for the automatic identification of muscle with MTrPs (i.e., active and latent MTrPs) from normal (i.e., no MTrP) muscle.

METHODS

Participants (n = 201) were recruited from Toronto Rehabilitation Institute, and ultrasound videos of their trapezius muscles were acquired. This new data set consists of 1344 images (248 active, 120 latent, 976 normal) collected from these videos. For texture analysis, several features were investigated with varying parameters (i.e., region of interest size, feature type and pixel pair relationships). Convolutional neural networks (CNN) were also applied to observe the performance of deep learning approaches. Performance was evaluated based on the classification accuracy, micro F1-score, sensitivity, specificity, positive predictive value and negative predictive value.

RESULTS

The best CNN approach was able to differentiate between muscles with and without MTrPs better than the best texture feature approach, with F1-scores of 0.7299 and 0.7135, respectively.

CONCLUSION

The results of this study reveal the challenges associated with MTrP identification and the potential and shortcomings of CNN and radiomics approaches in detail.

Diagnostic Applications of Ultrasonography in Myofascial Trigger Points: A Scoping Review and Critical Appraisal of Literature

Objective:

Myofascial trigger points (MTrPs) are pathognomonic of myofascial pain syndrome. The detection ability of MTrPs via ultrasonography is underreported and the characteristics of MTrPs are not sufficiently standardized. The objective was to summarize the characteristics and diagnostic abilities of ultrasonography for MTrP investigations.

Materials and Methods:

A multi-database, and bibliography hand-search was implemented. Studies of ≥10 patients, published after 1980, appraising ultrasonography as a diagnostic aid for myofascial pain syndrome were included. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was used to evaluate the diagnostic accuracy of the included studies.

Results:

Six cross-sectional studies met the inclusion criteria. The back and trapezius muscles were the most studied locations. The diagnostic studies were of low-medium risk of bias. The studies reported a large range of diagnostic metrics (accuracy 58%–100%, sensitivity 33%–91%, specificity 75%–100%, positive predictive value 91%–100%, negative predictive value 47%–97%, positive likelihood ratio 3.6, and negative likelihood ratio 0.12–0.67).

Conclusion:

This review found low-medium risk of bias evidence in support of ultrasonography for MTrP investigations. The clinical studies identified in the scoping review used gray-scale ultrasound equipment systems with a 5 to 14 MHz transducer to diagnose MTrPs and the local twitch response, and MTrPs were visualized mostly as hypoechoic nodules.

Use of automated artificial intelligence to predict the need for orthodontic extractions

Objective

To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records.

Methods

The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions.

Results

By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used.

Conclusions

The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.

Quantitative Ultrasound Texture Feature Changes With Conservative Treatment of the Trapezius Muscle in Female Patients With Myofascial Pain Syndrome

OBJECTIVE

We set out to assess whether quantitative ultrasound could be used to assess changes that occur after physical therapy in patients experiencing myofascial pain syndrome.

METHODS

We consecutively recruited female subjects experiencing myofascial pain syndrome of the neck and shoulder region and provided 10 sessions of conservative physical therapy. A control group was recruited for textural analyses. We measured change in pain ratings, range of motion, and ultrasound texture features before and after the intervention and after 3 mos.

RESULTS

We recruited 63 female myofascial pain syndrome subjects and 20 healthy controls. After treatment, the mean blob size (an ultrasound texture feature) value for each subject decreased from 30.84 ± 5.00 to 25.86 ± 5.67 on the right and decreased from 31.70 ± 5.51 to 28.08 ± 5.53 on the left (P < 0.0005). The blob count showed a significant increase only on the left side (P < 0.01). Corresponding to this were reductions in pain and disability scores after treatment and at 3 mos compared with retreatment (P < 0.0005 for all checkpoints). Cervical range of motion values were significantly increased only at 3 mos compared with pretreatment except for mean flexion range of motion.

CONCLUSIONS

Ultrasound texture feature of blob size and count changes correspond to routine clinical outcomes after conservative physical therapy of myofascial pain syndrome in female individuals.

Intelligent Algorithm-Based Picture Archiving and Communication System of MRI Images and Radiology Information System-Based Medical Informatization

Objective

The study aimed to explore the application value of picture archiving and communication system (PCAS) of MRI images based on radial basis function (RBF) neural network algorithm combined with the radiology information system (RIS).

Methods

551 patients who required MRI examination in a hospital from May 2016 to May 2021 were selected as research subjects. Patients were divided into two groups according to their own wishes. Those who were willing to use the RBF neural network algorithm-based PCAS of MRI images combined with RIS were set as the combined group, involving a total of 278 cases; those who were unwilling were set as the regular group, involving a total of 273 cases. The RBF neural network algorithm-based PCAS of MRI images combined with RIS was trained and tested for classification performance and then used for comparison analysis.

Result

The actual output (0.031259–0.038515) of all test samples was almost the same as the target output (0.000000) (P > 0.05). In the first 50,000 learnings, the iteration error of the RBF neural network dropped rapidly and finally stabilized at 0.038. The classification accuracy of the RBF neural network algorithm-based PCAS of MRI images combined with RIS for the head was 94.28%, that of abdomen was 97.22%, and it was 93.10% for knee joint, showing no statistically significant differences (P > 0.05), and the total classification accuracy was as high as 95%. The time spent in the examination in the combined group was about 2 hours, and that in the regular group was about 4 hours (P > 0.05). The satisfaction of the combined group (96.76%) was significantly higher than that of the control group (46.89%) (P > 0.05).

Conclusion

The RBF neural network has good classification performance for MRI images. To incorporate intelligent algorithms into the medical information system can optimize the system. RBF has good application prospects in the medical information system, and it is worthy of continuous exploration.

Assessment of Myofascial Trigger Points via Imaging: A Systematic Review

ABSTRACT

This study systematically reviewed the published literature on the objective characterization of myofascial pain syndrome and myofascial trigger points using imaging methods. PubMed, Embase, Ovid, and the Cochrane Library databases were used, whereas citation searching was conducted in Scopus. Citations were restricted to those published in English and in peer-reviewed journals between 2000 and 2021. Of 1762 abstracts screened, 69 articles underwent full-text review, and 33 were included. Imaging data assessing myofascial trigger points or myofascial pain syndrome were extracted, and important qualitative and quantitative information on general study methodologies, study populations, sample sizes, and myofascial trigger point/myofascial pain syndrome evaluation were tabulated. Methodological quality of eligible studies was assessed based on the Quality Assessment of Diagnostic Accuracy Studies criteria. Biomechanical properties and blood flow of active and latent myofascial trigger points assessed via imaging were found to be quantifiably distinct from those of healthy tissue. Although these studies show promise, more studies are needed. Future studies should focus on assessing diagnostic test accuracy and testing the reproducibility of results to establish the best performing methods. Increasing methodological consistency would further motivate implementing imaging methods in larger clinical studies. Considering the evidence on efficacy, cost, ease of use and time constraints, ultrasound-based methods are currently the imaging modalities of choice for myofascial pain syndrome/myofascial trigger point assessment.

Re-Examining Myofascial Pain Syndrome: Toward Biomarker Development and Mechanism-Based Diagnostic Criteria

PURPOSE OF REVIEW

We discuss the need for a mechanism-based diagnostic framework with a focus on the development of objective measures (e.g., biomarkers) that can potentially be added to the diagnostic criteria of the syndrome. Potential biomarkers are discussed in relation to current knowledge on the pathophysiology of myofascial pain syndrome (MPS), including alterations in redox status, inflammation, and the myofascial trigger point (MTrP) biochemical milieu, as well as imaging and neurophysiological outcomes. Finally, we discuss the long-term goal of conducting a Delphi survey, to assess the influence of putative MPS biomarkers on clinician opinion, in order to ultimately develop new criteria for the diagnosis of MPS.

RECENT FINDINGS

Myofascial pain syndrome (MPS) is a prevalent healthcare condition associated with muscle weakness, impaired mood, and reduced quality of life. MPS is characterized by the presence of myofascial trigger points (MTrPs): stiff and discrete nodules located within taut bands of skeletal muscle that are painful upon palpation. However, physical examination of MTrPs often yields inconsistent results, and there is no gold standard by which to diagnose MPS. The current MPS diagnostic paradigm has an inherent subjectivity and the absence of correlation with the underlying pathophysiology. Recent advancements in ultrasound imaging, systemic biomarkers, MTrP-specific biomarkers, and the assessment of dysfunction in the somatosensorial system may all contribute to improved diagnostic effectiveness of MPS.

Machine Learning Diagnostic Modeling for Classifying Fibromyalgia Using B-mode Ultrasound Images

Fibromyalgia (FM) diagnosis remains a challenge for clinicians due to a lack of objective diagnostic tools. One proposed solution is the use of quantitative ultrasound (US) techniques, such as image texture analysis, which has demonstrated discriminatory capabilities with other chronic pain conditions. From this, we propose the use of image texture variables to construct and compare two machine learning models (support vector machine [SVM] and logistic regression) for differentiating between the trapezius muscle in healthy and FM patients. US videos of the right and left trapezius muscle were acquired from healthy (n = 51) participants and those with FM (n = 57). The videos were converted into 64,800 skeletal muscle regions of interest (ROIs) using MATLAB. The ROIs were filtered by an algorithm using the complex wavelet structural similarity index (CW-SSIM), which removed ROIs that were similar. Thirty-one texture variables were extracted from the ROIs, which were then used in nested cross-validation to construct SVM and elastic net regularized logistic regression models. The generalized performance accuracy of both models was estimated and confirmed with a final validation on a holdout test set. The predicted generalized performance accuracy of the SVM and logistic regression models was computed to be 83.9 ± 2.6% and 65.8 ± 1.7%, respectively. The models achieved accuracies of 84.1%, and 66.0% on the final holdout test set, validating performance estimates. Although both machine learning models differentiate between healthy trapezius muscle and that of patients with FM, only the SVM model demonstrated clinically relevant performance levels.