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

Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%.

Fibromyalgia: A Review of the Pathophysiological Mechanisms and Multidisciplinary Treatment Strategies

Fibromyalgia is a syndrome characterized by chronic widespread musculoskeletal pain, which may or may not be associated with muscle or joint stiffness, accompanied by other symptoms such as fatigue, sleep disturbances, anxiety, and depression. It is a highly prevalent condition globally, being considered the third most common musculoskeletal disorder, following lower back pain and osteoarthritis. It is more prevalent in women than in men, and although it can occur at any age, it is more common between the ages of thirty and thirty-five. Although the pathophysiology and etiopathogenesis remain largely unknown, three underlying processes in fibromyalgia have been investigated. These include central sensitization, associated with an increase in the release of both excitatory and inhibitory neurotransmitters; peripheral sensitization, involving alterations in peripheral nociceptor signaling; and inflammatory and immune mechanisms that develop concurrently with the aforementioned processes. Furthermore, it has been determined that genetic, endocrine, psychological, and sleep disorders may influence the development of this pathology. The accurate diagnosis of fibromyalgia remains challenging as it lacks specific diagnostic biomarkers, which are still under investigation. Nonetheless, diagnostic approaches to the condition have evolved based on the use of scales and questionnaires for pain identification. The complexity associated with this pathology makes it difficult to establish a single effective treatment. Therefore, treatment is multidisciplinary, involving both pharmacological and non-pharmacological interventions aimed at alleviating symptoms. The non-pharmacological treatments outlined in this review are primarily related to physiotherapy interventions. The effectiveness of physical exercise, both on land and in water, as well as the application of electrotherapy combined with transcranial therapy and manual therapy has been highlighted. All of these interventions aim to improve the quality of life of patients highly affected by fibromyalgia.

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.

The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain

The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.

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.

A theoretical framework to improve the construct for chronic pain disorders using fibromyalgia as an example

Fibromyalgia (FM) is a frequent, complex condition of chronic musculoskeletal pain with no evidence for biological correlates. For this reason, despite many efforts from the medical community, its construct still appears ill defined. Promising candidate biomarkers are critically reviewed. A research agenda is proposed for developing a clearer construct of FM. The ideal theoretical framework is one of overcoming the illness–disease dichotomy and considering reciprocal interactions between biology and behaviour. This approach may foster research in other fields of pain medicine and of medicine in general.

Current advances and research in ultrasound imaging to the assessment and management of musculoskeletal disorders

Currently evidence-based practice has given scientific weight to the physical therapist profession; it is essential that all medical professional and physical therapists know the usefulness of new tools that optimize the effectiveness of their interventions and allow the growing of the scientific knowledge base. The use of ultrasound imaging (USI) by physiotherapists has evolved in recent years, consolidating as an increasingly standardized technique, low cost compared to other imaging techniques, quickly of execution, feasible and reliable tool. USI offers a wide range of opportunities in clinical practice as well as in different research areas. Therefore, ultrasound has been currently used as a diagnostic tool by physicians and in recent years there has been an expansion of the use of ultrasound equipment by non-physicians professionals such as physical therapist or physical trainers, who incorporates USI as a means of assessing musculoskeletal system architecture and composition, musculoskeletal changes in dysfunction, pain or injury conditions, as an interventional technique assisting echo-guided procedures or using the visual real-time information as a biofeedback in control motor approaches, as guiding tool in clinical decisions as well as to improve the understanding of tissue adaptations to exercise or movement. The purpose of this article is to review and provide an overview about the currently research of the USI applications and their benefits for the diagnosis and management in individuals with musculoskeletal conditions.