Myofascial pain (MP) is a leading cause of chronic low back pain, yet its tissue-level drivers remain poorly defined and lack reliable image biomarkers. Existing studies focus predominantly on muscle while neglecting fascia, fat, and other soft tissues that play integral biomechanical roles. We developed an anatomically grounded explainable artificial intelligence (AI) framework, LAYER (Layer-wise Analysis for Yielding Explainable Relevance Tissue), that analyses six tissue layers in three-dimensional (3D) ultrasound and quantifies their contribution to MP prediction. By utilizing the largest multi-model 3D ultrasound cohort consisting of over 4,000 scans, LAYER reveals that non-muscle tissues contribute substantially to pain prediction. In B-mode imaging, the deep fascial membrane (DFM) showed the highest saliency (0.420), while in combined B-mode and shear-wave images, the collective saliency of non-muscle layers (0.316) nearly matches that of muscle (0.317), challenging the conventional muscle-centric paradigm in MP research and potentially affecting the therapy methods. LAYER establishes a quantitative, interpretable framework for linking layer-specific anatomy to pain physiology, uncovering new tissue targets and providing a generalizable approach for explainable analysis of soft-tissue imaging.

Objectives: This study aims to systematically review the literature on the computational processing of the language of pain, or pain narratives, whether generated by patients or physicians, identifying current trends and challenges. Methods: Following the PRISMA guidelines, a comprehensive literature search was conducted to select relevant studies on the computational processing of the language of pain and answer pre-defined research questions. Data extraction and synthesis were performed to categorize selected studies according to their primary purpose and outcome, patient and pain population, textual data, computational methodology, and outcome targets. Results: Physician-generated language of pain, specifically from clinical notes, was the most used data. Tasks included patient diagnosis and triaging, identification of pain mentions, treatment response prediction, biomedical entity extraction, correlation of linguistic features with clinical states, and lexico-semantic analysis of pain narratives. Only one study included previous linguistic knowledge on pain utterances in their experimental setup. Most studies targeted their outcomes for physicians, either directly as clinical tools or as indirect knowledge. The least targeted stage of clinical pain care was self-management, in which patients are most involved. Affective and sociocultural dimensions were the least studied domains. Only one study measured how physician performance on clinical tasks improved with the inclusion of the proposed algorithm. Discussion: This review found that future research should focus on analyzing patient-generated language of pain, developing patient-centered resources for self-management and patient-empowerment, exploring affective and sociocultural aspects of pain, and measuring improvements in physician performance when aided by the proposed tools.

You are free to share this article under the Attribution 4.0 International license. Cognitive behavioral therapy for chronic pain supported by artificial intelligence can yield the same results as programs delivered by therapists, a new study shows. Cognitive behavioral therapy (CBT) is an effective alternative to opioid painkillers for managing chronic pain. But getting patients to complete those programs is challenging, especially because psychotherapy often requires multiple sessions and mental health specialists are scarce. AI-supported therapy requires substantially less clinician time, making it more accessible to patients, the researchers report.

Machine learning and deep learning has shown very good results when applied to various computer vision applications such as detection of plant diseases in agriculture (Kamilaris & Prenafeta-Boldú, 2018), fault diagnosis in industrial engineering (Wen et al., 2018), brain tumor recognition from MR images (Chen et al., 2018a), segmentation of endoscopic images for gastric cancer (Hirasawa et al., 2018), or skin lesion recognition (Li & Shen, 2018) and even autonomous vehicles (Alam et al., 2019). As our daily life increasingly depends on sitting work and the opportunities for physical exercising (in the context of COVID-19 pandemic associated restrictions and lockdowns are diminished), many people are facing various medical conditions directly related to such sedentary lifestyles. One of the frequently mentioned problems is back pain, with bad sitting posture being one of the compounding factors to this problem (Grandjean & Hünting, 1977; Sharma & Majumdar, 2009). Inadequate postures adopted by office workers are one of the most significant risk factors of work-related musculoskeletal disorders. The direct consequence may be back pain, while indirectly it has been associated with cervical disease, myopia, cardiovascular diseases and premature mortality (Cagnie et al., 2006).

It began with a pulled muscle. Each day after school, as the sun sank dusky purple over the hills of my hometown, I'd run with my track teammates. Even on our easy days, I'd bound ahead, leaving them behind. It wasn't that I thought myself better than them--it's that when I ran fast, and focused on nothing but the cold air burning my lungs and my feet pounding, my normally anxious thoughts turned to white noise. I limped a little, and then tried running again: sharp, hot pain radiated down my thigh. Panic flooded me, as I imagined weeks without running: weeks without a predictable break from my own thoughts, weeks immersed in adolescent loneliness.

Kaia Health, a digital therapeutics startup which uses computer vision technology for real-time posture tracking via the smartphone camera to deliver human-hands-free physiotherapy, has closed a $26 million Series B funding round. The funding was led by Optum Ventures, Idinvest and capital300 with participation from existing investors Balderton Capital and Heartcore Capital, in addition to Symphony Ventures -- the latter in an "investment partnership" with world famous golfer, Rory McIlroy, who knows a thing or two about chronic pain. Back in January 2019, when Kaia announced a $10M Series A, its business ratio was split 80:20 Europe to US. Now, says co-founder and CEO Konstantin Mehl -- speaking to TechCrunch by Zoom chat from New York where he's recently relocated -- it's flipped the other way. Part of the new funding will thus go on building out its commercial team in the US -- now its main market.

Researchers from the Icahn School of Medicine at Mount Sinai developed an artificial intelligence model that can scan physicians' notes and distinguish between acute and chronic lower back pain, according to findings published in the Journal of Medical Internet Research. "Several studies have documented increases in medication prescriptions and visits to physicians, physical therapists, and chiropractors for lower back pain episodes," Ismail Nabeel, MD, MPH, associate professor of environmental medicine and public health at the Icahn School of Medicine at Mount Sinai, said in a press release. "This study is important because artificial intelligence can potentially more accurately distinguish whether the pain is acute or chronic, which would determine whether a patient should return to normal activities quickly or rest and schedule follow-up visits with a physician." "This study also has implications for diagnosis, treatment and billing purposes in other musculoskeletal conditions, such as the knee, elbow, and shoulder pain, where the medical codes also do not differentiate by pain level and acuity," he added. To examine the feasibility of a system that automatically distinguishes acute lower back pain based on free-text clinical notes, Nabeel and colleagues used a dataset of 17,409 clinical notes from various primary care practices in the Mount Sinai Health System.

About 80 percent of adults experience lower back pain in their lifetime; it is the most common cause of job-related disability. Many argue that prescribing opioids for lower back pain contributed to the opioid crisis; thus, determining the quality of lower back pain in clinical practice could provide an effective tool not only to improve the management of lower back pain but also to curb unnecessary opioid prescriptions. Acute and chronic lower back pain are different conditions with different treatments. However, they are coded in electronic health records with the same code and can be differentiated only by retrospective reviews of the patient's chart, which includes the review of clinical notes. The single code for two different conditions prevents appropriate billing and therapy recommendations, including different return-to-work scenarios.

According to the Centers for Disease Control and Prevention (CDC), an estimated 50 million adults in the U.S. suffered from chronic pain in 2016, and according to the Substance Abuse and Mental Health Services Administration (SAMHSA), an estimated 10.3 million people in the U.S. ages 12 and older misused opioids in 2018. As such, the National Institutes of Health (NIH) have announced the awarding of $945 million in research grants to tackle the national opioid crisis through NIH HEAL Initiative (Helping to End Addiction Long-term Initiative). The UC San Francisco Department of Radiology and Biomedical Imaging is pleased to announce that one such project is the Back Pain Consortium (BACPAC) Research Program of which Sharmila Majumdar, PhD, vice chair for Research, is a part of. At this time, chronic low back pain is one of the most common forms of chronic pain in adults, and current treatments are ineffective, leading to increased use of opioids. This research will also lay the foundation for NIH funded research at the newly established Center for Intelligent Imaging, using artificial intelligence fueled algorithms for fast image acquisition, data analysis, quantitative sensory assessments, brain imaging, and biomechanical evaluation of the spine.

Chronic diseases affect the lives of millions of people around the world. Over 16 million adults in the U.S., for instance, have been diagnosed with chronic obstructive pulmonary disease (COPD), an inflammation of the lungs that obstructs airflow. And researchers estimate that 80 percent of people in the U.S. will experience back pain several times in their lives. Making matters worse, the treatments tend to be expensive -- in 2010, the total annual cost of COPD and back pain in the U.S. was projected to be $50 billion and $240 billion, respectively. Kaia Health, a four-year-old firm founded by former Foodora CEO Konstantin Mehl and Manuel Thurner, claims its digital therapeutics solution -- apps that tap artificial intelligence (AI) and motion-tracking technology to help manage pain -- is not only cheaper than prescription drugs and in-person consultations, but more sustainable in the long term.