SC COBRE for Translational Research Improving Musculoskeletal Health (SC-TRIMH)

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Registration of Smart Needle Measurements with Functional Ultrasonography by Machine Learning for Quantitative Monitoring and Assessment of Myofascial Pain Summary This Team Science Development Project complements and extends the scope and objective of the on-going COBRE project entitled “South Carolina COBRE for Translational Research Improving Musculoskeletal Health (SC-TRIMH, P20GM121342). The goal of SC-TRIMH is to enhance and expand the Biomedical Research capacity at Clemson University and the State of South Carolina to promote outstanding multidisciplinary, collaborative, and translational research in bone and joint diseases. SC-TRIMH proposes to implement a novel scientific concept for translational research, i.e., Virtual Human Trials, through powerful computational modeling combined with quantitative functional validation and assessment to expedite the process from concept development to deliverable new therapeutics, interventions, and devices for musculoskeletal health. The specific aims of SC-TRIMH are to: 1) train and mentor an initial cadre of five targeted junior investigators; 2) develop and enhance key areas of research infrastructure through the development of novel cores; and 3) promote the long- term viability of SC-TRIMH through technology transfer and rigorous evaluation and improvement strategies. The scientific cores of SC-TRIMH include: 1) Multi-scale Computational Modeling Core; 2) Advanced Fabrication and Testing Core; and 3) Preclinical Assessment Core. These cores provide key technical support to the junior investigators to implement the new concept of Virtual Human Trials to advance musculoskeletal health and facilitate their competitiveness for national research awards. Chronic musculoskeletal pain is estimated to affect 10% - 20% of the general population and has been a major public health problem. Among patients presenting with chronic musculoskeletal pain, the prevalence of myofascial pain syndrome (MPS) can be as high as 85%. While MPS is believed to be related to the dysfunction of myofascial tissues, however, currently there are no quantitative biomarkers that can quantify myofascial tissue abnormalities in latent or active states of pain to differentiate them from the healthy state. Opioids are commonly used to treat myofascial pain disorders with limited effectiveness but detrimental consequences. To improve treatment of MPS and prevent opioid misuse, there is an urgent need to develop clinically effective quantitative MPS biomarkers for effective management of chronic myofascial pain. The goal of the proposed research is to develop an innovative machine learning (ML) generated biomarker for quantitative assessment and differentiation of abnormal myofascial tissues in latent and active myofascial pain stages from healthy tissues, to objectively monitor and assess the responses to myofascial pain treatment and management. The new quantitative biomarker is enabled by innovatively integrating, through ML models, the functional ultrasonography with novel microsensor-embedded smart needle measurements of the tissue. The hypothesis is that the sensitivity and specificity of the ultrasound imaging diagnostics can be significantly improved by adding accurate anchor point information (e.g., depth-resolved tissue stiffness and oxygenation via a smart needle) to functional ultrasonography such as elastography (muscle stiffness) and Doppler ultrasound (blood flow), through the developed ML models. The improved sensitivity and specificity will allow quantitative structural and functional measurements of the myofascial tissue for better diagnosis and management of MPS. The specific aims are: 1) develop smart needles for minimally invasive measurements of depth-resolved myofascial tissue stiffness and oxygenation and co-register the results with ultrasonography; and 2) develop quantitative metrics from multimodal functional ultrasonography and co-registered smart needle measurements of myofascial tissues. The Team Science Development project will be performed by an interdisciplinary team of engineers, data scientists and physicians with combined expertise in sensor, instrumentation, manufacturing, computing, machine learning, ultrasonography, sports medicine and pain management, and acupuncture. The collaborative team science approach allows us to apply the latest data science method and cutting-edge sensor technologies to addressing the fundamental challenges in pain management. The developed quantitative biomarker is expected to become a new tool for enhancing the diagnosis, assessment, and effective management of MPS via non-opioid therapies and non-pharmacologic treatments, and thus contributes to the NIH HEAL Initiative to 1) improve treatment and prevention of opioid misuse and opioid use disorder and 2) enhance pain management. The initial studies and teaming resulted from this Team Science Development project will lay a solid foundation for preparation and submission of research proposals.