Search

Background: Artificial intelligence (AI) is engineered to emulate tasks that have historically required human interaction and intellect, including learning, pattern recognition, decision-making, and problem-solving. Although AI models like ChatGPT-4 have demonstrated satisfactory performance on medical licensing exams, suggesting a potential for supporting medical diagnostics and decision-making, no study of which we are aware has evaluated the ability of these tools to make treatment recommendations when given clinical vignettes and representative medical imaging of common orthopaedic conditions. As AI continues to advance, a thorough understanding of its strengths and limitations is necessary to inform safe and helpful integration into medical practice., Questions/purposes: (1) What is the concordance between ChatGPT-4-generated treatment recommendations for common orthopaedic conditions with both the American Academy of Orthopaedic Surgeons (AAOS) clinical practice guidelines (CPGs) and an orthopaedic attending physician's treatment plan? (2) In what specific areas do the ChatGPT-4-generated treatment recommendations diverge from the AAOS CPGs?, Methods: Ten common orthopaedic conditions with associated AAOS CPGs were identified: carpal tunnel syndrome, distal radius fracture, glenohumeral joint osteoarthritis, rotator cuff injury, clavicle fracture, hip fracture, hip osteoarthritis, knee osteoarthritis, ACL injury, and acute Achilles rupture. For each condition, the medical records of 10 deidentified patients managed at our facility were used to construct clinical vignettes that each had an isolated, single diagnosis with adequate clarity. The vignettes also encompassed a range of diagnostic severity to evaluate more thoroughly adherence to the treatment guidelines outlined by the AAOS. These clinical vignettes were presented alongside representative radiographic imaging. The model was prompted to provide a single treatment plan recommendation. Each treatment plan was compared with established AAOS CPGs and to the treatment plan documented by the attending orthopaedic surgeon treating the specific patient. Vignettes where ChatGPT-4 recommendations diverged from CPGs were reviewed to identify patterns of error and summarized., Results: ChatGPT-4 provided treatment recommendations in accordance with the AAOS CPGs in 90% (90 of 100) of clinical vignettes. Concordance between ChatGPT-generated plans and the plan recommended by the treating orthopaedic attending physician was 78% (78 of 100). One hundred percent (30 of 30) of ChatGPT-4 recommendations for fracture vignettes and hip and knee arthritis vignettes matched with CPG recommendations, whereas the model struggled most with recommendations for carpal tunnel syndrome (3 of 10 instances demonstrated discordance). ChatGPT-4 recommendations diverged from AAOS CPGs for three carpal tunnel syndrome vignettes; two ACL injury, rotator cuff injury, and glenohumeral joint osteoarthritis vignettes; as well as one acute Achilles rupture vignette. In these situations, ChatGPT-4 most often struggled to correctly interpret injury severity and progression, incorporate patient factors (such as lifestyle or comorbidities) into decision-making, and recognize a contraindication to surgery., Conclusion: ChatGPT-4 can generate accurate treatment plans aligned with CPGs but can also make mistakes when it is required to integrate multiple patient factors into decision-making and understand disease severity and progression. Physicians must critically assess the full clinical picture when using AI tools to support their decision-making., Clinical Relevance: ChatGPT-4 may be used as an on-demand diagnostic companion, but patient-centered decision-making should continue to remain in the hands of the physician., Competing Interests: Each author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request., (Copyright © 2024 by the Association of Bone and Joint Surgeons.)

Objective: To prospectively evaluate a deep learning-based denoising reconstruction (DLR) for improved resolution and image quality in musculoskeletal (MSK) magnetic resonance imaging (MRI)., Methods: Images from 137 contrast-weighted sequences in 40 MSK patients were evaluated. Each sequence was performed twice, first with the routine parameters and reconstructed with a routine reconstruction filter (REF), then with higher resolution and reconstructed with DLR, and with three conventional reconstruction filters (NL2, GA43, GA53). The five reconstructions (REF, DLR, NL2, GA43, and GA53) were de-identified, randomized, and blindly reviewed by three MSK radiologists using eight scoring criteria and a forced ranking. Quantitative SNR, CNR, and structure's full width at half maximum (FWHM) for resolution assessment were measured and compared. To account for repeated measures, Generalized Estimating Equations (GEE) with Bonferroni adjustment was used to compare the reader's scores, SNR, CNR, and FWHM between DLR vs. NL2, GA43, GA53, and REF., Results: Compared to the routine REF images, the resolution was improved by 47.61% with DLR from 0.39 ± 0.15 mm 2 to 0.20 ± 0.06 mm 2 (p < 0.001). Per-sequence average scan time was shortened by 7.93% with DLR from 165.58 ± 21.86 s to 152.45 ± 25.65 s (p < 0.001). Based on the average scores, DLR images were rated significantly higher in all image quality criteria and the forced ranking (p < 0.001)., Conclusion: This prospective clinical evaluation demonstrated that DLR allows approximately two times finer resolution and improved image quality compared to the standard-of-care images., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

This review examines the role of various imaging techniques in assessing vascular and musculoskeletal manifestations in Systemic Sclerosis (SSc). Imaging modalities, such as thermography, capillaroscopy, ultrasound, optical coherence tomography, laser speckle contrast analysis, radiography, computed tomography, and MRI, offer valuable insights into SSc-related complications. Findings suggest that these techniques aid in diagnosing conditions like Raynaud phenomenon, digital ulcers, calcinosis, acro-osteolysis, and hand contractures. However, each modality has its advantages and limitations, necessitating a multimodal approach for comprehensive evaluation and accurate diagnosis of SSc-related manifestations., Competing Interests: Disclosures The authors have nothing to disclose., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Point-of-care ultrasound may be used to assist in the diagnosis of skin, soft tissue, and musculoskeletal concerns in the emergency department. Frequently, linear or curvilinear probes are used to perform these studies and ultrasound may be used to assist in common emergency department procedures related to these conditions., Competing Interests: Disclosure The authors have no relevant conflicts of interest to disclose., (Copyright © 2024 Elsevier Inc. All rights reserved.)

MR-guided focused ultrasound (MRgFUS) has a wide range of musculoskeletal applications. Some indications are well validated, specifically the treatment of painful osseous metastases and osteoid osteoma. Others are only beginning to be studied, such as the treatment of painful facet, sacroiliac, and knee joints. MRgFUS of soft tissue lesions also shows promise, particularly in patients whom alternative modalities are not feasible or may result in significant morbidity. Ongoing and future research will illuminate the full potential for MRgFUS in the treatment of musculoskeletal conditions., Competing Interests: Disclosure The author has nothing to disclose., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Hydatid disease is a parasitic infection seen in endemic areas. Musculoskeletal hydatid disease is rarely reported., Purpose: To describe the magnetic resonance imaging (MRI) features of musculoskeletal hydatid disease and to highlight the specific findings in the diagnosis of hydatid cysts., Material and Methods: The MRI scans of 29 cases diagnosed as musculoskeletal hydatid disease between 2000 and 2022 were retrospectively analyzed. The localization, size, appearance (unilocular or multilocular), signal characteristics, rim sign, presence of internal septa and membrane, and gadolinium enhancement pattern of hydatid cysts were evaluated., Results: A total of 29 patients diagnosed with hydatid cyst were included in the study. Of the lesions, 18 were localized in bone and 11 were in soft tissue. The bone hydatid cysts on MRI showed heterogeneous low to intermediate signal intensity on T1-weighted images and high signal intensity on T2-weighted images in the medullary bone. In 15/18 patients, there was also cortical destruction and extension into the adjacent soft tissue planes. In 8/11 cases where the cyst was located in muscle tissue, the "cyst or cysts within a cyst" appearance was observed on MRI. The rim sign was observed in 7/11 cases and the "water lily" sign was noted in 2/11 cases., Conclusion: MRI provides valuable information for the diagnosis of hydatid disease with its distinctive imaging features. Knowledge of the different patterns of hydatid cysts on MRI may be helpful in the diagnosis of this disease., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

With a substantial growth in the use of musculoskeletal MRI, there has been a growing need to improve MRI workflow, and faster imaging has been suggested as one of the solutions for a more efficient examination process. Consequently, there have been considerable advances in accelerated MRI scanning methods. This article aims to review the basic principles and applications of accelerated musculoskeletal MRI techniques including widely used conventional acceleration methods, more advanced deep learning-based techniques, and new approaches to reduce scan time. Specifically, conventional accelerated MRI techniques, including parallel imaging, compressed sensing, and simultaneous multislice imaging, and deep learning-based accelerated MRI techniques, including undersampled MR image reconstruction, super-resolution imaging, artifact correction, and generation of unacquired contrast images, are discussed. Finally, new approaches to reduce scan time, including synthetic MRI, novel sequences, and new coil setups and designs, are also reviewed. We believe that a deep understanding of these fast MRI techniques and proper use of combined acceleration methods will synergistically improve scan time and MRI workflow in daily practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1., (© 2023 International Society for Magnetic Resonance in Medicine.)

Recent advances in computed tomography have resulted in new applications of CT scans in musculoskeletal imaging. Dual-energy CT technology involves the acquisition of data at high and low kilovolts, allowing differentiation and quantification of materials with different X-ray absorption. Newer CT scanners with a variety of post-processing options allow interesting applications of dual-energy CT in musculoskeletal and trauma imaging. This article provides an overview of the basic principles and physics of DECT. We review applications of DECT in the evaluation of the acute painful joint with suspicion of gout, metal artefact reduction in the prosthetic joint and in imaging of patients following major trauma. We present a review of literature and case examples to illustrate the strengths and limitations of this modality in the diagnosis of acute musculoskeletal conditions., (© 2024. Crown.)

Introduction: Musculoskeletal ultrasound (MSUS) is integral to routine clinical diagnostics for musculoskeletal and joint disorders. This study aims to establish and validate a sonography course tailored to undergraduate medical students acquiring MSUS-specific skills at a German university., Methods: A blended learning training concept, comprising 24 instruction sessions of 45 min each, was designed based on the current national guidelines of the German Society for Ultrasound in Medicine (DEGUM). This program was integrated into the clinical phase of the undergraduate students' medical education. The self-perceived improvement in competency and the effectiveness of the course design were evaluated using a a 7-point Likert scale questionnaire. Objective learning success was evaluated via a written test and a "Direct Observation of Practical Skills" practical exam. Control groups included medical students without MSUS training (control group 1) and doctors who had completed DEGUM-certified basic MSUS courses (control group 2). Both control groups completed the written test, while control group 2 also took the practical final exam. The study involved 146 participants: 56 were allocated to the study group, 44 to control group 1, and 46 to control group 2., Results: The study group rated their skills significantly higher after the course (p < 0.01). Participants expressed high satisfaction with the course design, the teaching materials, and the teachers. The study group's performance on the final written test was comparable to those of control group 2 (p = 0.06) and significantly superior to control group 1 (p < 0.001). Additionally, the study group's performance on the practical final exam was not significantly different from control group 2 (p = 0.28), with both groups achieving scores exceeding 80%., Conclusion: Both subjective and objective measures of learning suggest that an MSUS course designed for postgraduates can be effectively adapted for undergraduate medical students. Incorporating MSUS training into the clinical curriculum is recommended to enhance future medical professionals' educational experience and practical skills., (© 2024. The Author(s).)

Introduction: Magnetic Resonance Imaging (MRI) has revolutionized the diagnosis and treatment of musculoskeletal disorders. Parallel imaging (PI) and compressed sensing (CS) techniques reduce scan time, but higher acceleration factors decrease image quality. Artificial intelligence has enhanced MRI reconstructions by integrating deep learning algorithms. Therefore, the study aims to review the impact of Artificial intelligence-assisted compressed sensing (AI-CS) and acceleration factors on scan time and image quality in musculoskeletal MRI., Methods: Database searches were completed across PubMed, Scopus, CINAHL, Web of Science, Cochrane Library, and Embase to identify relevant articles focusing on the application of AI-CS in musculoskeletal MRI between 2022 and 2024. We utilized the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines to extract data from the selected studies., Results: Nine articles were included for the final review, with a total sample size of 730 participants. Of these, seven articles were rated as high, while two articles were considered to be of moderate quality. MRI examination with AI-CS showed scan time reduction of 18.9-38.8% for lumbar spine, 38-40% for shoulder, 54-75% for knee and 53-63% for ankle., Conclusions: AI-CS showed a significant reduction in scan time and improved image quality for 2D and 3D sequences in musculoskeletal MRI compared with PI and CS. Determining the optimal acceleration factor necessary to achieve images with higher image quality compared to traditional PI techniques is required before clinical implementation. Higher acceleration factors currently lead to reduced image scores, although advancements in AI-CS are expected to address the limitation., Implications of Practice: AI-CS in MRI improves patient care by shortening scan times, reducing patient discomfort and anxiety, and produces high quality images for accurate diagnosis., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Musculoskeletal infections in the ER are not an uncommon presentation. The clinical context is critical in determining the suspicion for infection and degree of tissue involvement which can involve all layers from the skin to bones. The location, extent, and severity of clinically suspected infection directly relate to the type of imaging performed. Uncomplicated cellulitis typically does not require any imaging. Localized and superficial infections can mostly be evaluated with ultrasound. If there is a diffuse site (an entire extremity) or suspected deeper involvement (muscle/deep fascia), then CT is accurate in diagnosing, widely available, and performed quickly. With potential osseous involvement, MRI is the gold standard for diagnosing acute osteomyelitis; however, it has the drawbacks of longer scan times, artifacts including patient motion, and limited availability., (© 2023. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Neglected tropical diseases (NTDs) encompass a group of diseases predominantly found in tropical regions, with origins dating back to their inclusion in the United Nations Millennium Development Goals in 2000. This initiative aimed to raise awareness and global funding to combat these diseases, which thrive in areas with limited sanitation, healthcare, and education. NTDs are caused by various pathogens such as bacteria, fungi, parasites, and viruses and affect over two billion individuals in resource-poor communities, leading to preventable deaths and devastating consequences. While the musculoskeletal system is only occasionally affected, the resulting chronic disabilities prevent individuals from working, posing a significant socioeconomic burden in this region of the world. Some NTDs exhibit distinct imaging features, and radiologists need to be aware of these characteristics to facilitate early treatment. In this review, we delve into musculoskeletal NTDs, focusing on clinical features and imaging findings, differential diagnosis, and clinical management., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Musculoskeletal infections consist of different clinical conditions that are commonly encountered in daily clinical settings. As clinical findings and even laboratory tests cannot always be specific, imaging plays a crucial role in the diagnosis and treatment of these cases. Musculoskeletal infections most commonly occur secondary to direct inoculation into the skin involuntarily affected by trauma, microorganism, foreign bodies, or in diabetic ulcers; direct infections can also occur from voluntary causes due to surgery, vaccinations, or other iatrogenic procedures. Hematogenous spread of infection from a remote focus can also be a cause for musculoskeletal infections. Risk factors for soft tissue and bone infections include immunosuppression, old age, corticosteroid use, systemic illnesses, malnutrition, obesity, and burns. Most literature discusses musculoskeletal infections according to the diagnostic tools or forms of infection seen in different soft tissue anatomical planes or bones. This review article aims to evaluate musculoskeletal infections that occur due to direct inoculation to the musculoskeletal tissues, by focusing on the traumatic mechanism with emphasis on the radiological findings., (© 2024. The Author(s).)

Musculoskeletal hydatid disease is rare and can be located anywhere but most commonly the bone and muscles of the spine, pelvis, then the lower limbs. Imaging is essential for its diagnosis, performing the pre-therapeutic assessment, guiding possible percutaneous treatments, and providing post-therapeutic follow-up. Musculoskeletal hydatidosis can take several forms that may suggest other infections and tumors or pseudotumors. MRI and CT are superior for its diagnosis but ultrasound and radiography remain the most accessible examinations in developing countries where this parasitosis is endemic. In this review, we provide an overview of this disease and describe its different imaging patterns in soft tissue and bone involvement that should be sought to support the diagnosis., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Saliency maps are popularly used to "explain" decisions made by modern machine learning models, including deep convolutional neural networks (DCNNs). While the resulting heatmaps purportedly indicate important image features, their "trustworthiness," i.e., utility and robustness, has not been evaluated for musculoskeletal imaging. The purpose of this study was to systematically evaluate the trustworthiness of saliency maps used in disease diagnosis on upper extremity X-ray images. The underlying DCNNs were trained using the Stanford MURA dataset. We studied four trustworthiness criteria-(1) localization accuracy of abnormalities, (2) repeatability, (3) reproducibility, and (4) sensitivity to underlying DCNN weights-across six different gradient-based saliency methods (Grad-CAM (GCAM), gradient explanation (GRAD), integrated gradients (IG), Smoothgrad (SG), smooth IG (SIG), and XRAI). Ground-truth was defined by the consensus of three fellowship-trained musculoskeletal radiologists who each placed bounding boxes around abnormalities on a holdout saliency test set. Compared to radiologists, all saliency methods showed inferior localization (AUPRCs: 0.438 (SG)-0.590 (XRAI); average radiologist AUPRC: 0.816), repeatability (IoUs: 0.427 (SG)-0.551 (IG); average radiologist IOU: 0.613), and reproducibility (IoUs: 0.250 (SG)-0.502 (XRAI); average radiologist IOU: 0.613) on abnormalities such as fractures, orthopedic hardware insertions, and arthritis. Five methods (GCAM, GRAD, IG, SG, XRAI) passed the sensitivity test. Ultimately, no saliency method met all four trustworthiness criteria; therefore, we recommend caution and rigorous evaluation of saliency maps prior to their clinical use., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Tuberculosis (TB) represents a major public health problem worldwide. Any tissue may be infected. Involvement of the musculoskeletal (MSK) system account for 1-3% of all tuberculous infections. MSK TB may manifest as tuberculous spondylitis, arthritis, osteomyelitis, and soft tissue infections. Although TB spondylitis may present with distinctive imaging features compared to pyogenic infections of the spine, the imaging semiology of extra-spinal TB infections is mostly nonspecific and may mimic other lesions. TB infections should therefore always be considered in the differential diagnosis, particularly in immunocompromised patients. The aim of this article is to review the imaging features of spinal and extra-spinal MSK TB. Magnetic resonance imaging is considered the modality of choice to make the diagnosis and to evaluate the extent of the disease., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Clinical/methodical Issue: Magnetic resonance imaging (MRI) is a central component of musculoskeletal imaging. However, long image acquisition times can pose practical barriers in clinical practice., Standard Radiological Methods: MRI is the established modality of choice in the diagnostic workup of injuries and diseases of the musculoskeletal system due to its high spatial resolution, excellent signal-to-noise ratio (SNR), and unparalleled soft tissue contrast., Methodological Innovations: Continuous advances in hardware and software technology over the last few decades have enabled four-fold acceleration of 2D turbo-spin-echo (TSE) without compromising image quality or diagnostic performance. The recent clinical introduction of deep learning (DL)-based image reconstruction algorithms helps to minimize further the interdependency between SNR, spatial resolution and image acquisition time and allows the use of higher acceleration factors., Performance: The combined use of advanced acceleration techniques and DL-based image reconstruction holds enormous potential to maximize efficiency, patient comfort, access, and value of musculoskeletal MRI while maintaining excellent diagnostic accuracy., Achievements: Accelerated MRI with DL-based image reconstruction has rapidly found its way into clinical practice and proven to be of added value. Furthermore, recent investigations suggest that the potential of this technology does not yet appear to be fully harvested., Practical Recommendations: Deep learning-reconstructed fast musculoskeletal MRI examinations can be reliably used for diagnostic work-up and follow-up of musculoskeletal pathologies in clinical practice., (© 2024. The Author(s).)

Along with clinical and laboratory findings, imaging has a significant role in the initial evaluation and treatment follow-up of a wide variety of infectious and non-infectious musculoskeletal (MSK) conditions. The imaging findings of many non-infectious MSK processes, such as inflammatory, metabolic, and degenerative arthropathies, complications of diabetes mellitus, osseous and soft tissue injuries, osteonecrosis, tumors, and tumor-like lesions may be nonspecific and often overlap with those found in MSK infections. In this article, the authors discuss the imaging findings of both frequent and rare mimickers of MSK infections, including potential distinguishing characteristics., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Nowadays, the use of advanced MRI sequences such as diffusion-weighted imaging or perfusion-weighted imaging in the field of musculoskeletal radiology remains limited compared to other anatomical regions and subspecialties. Several reasons underpin this, primarily technical challenges, and a longstanding reliance on conventional and morphological evaluations of soft tissue and bone lesions. Experienced radiologists often assert that these advanced sequences do not offer added diagnostic value, claiming that a morphological approach suffices. However, in our opinion, the role of these advanced MRI sequences extends beyond merely naming an MSK lesion. In this commentary, we elucidate how these sequences can aid radiologists in various scenarios, from determining patient prognosis and tracking treatment progress to enhancing clinical-radiological correlations or guiding less experienced radiologists in evaluating soft tissues or bone tumours., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

During the COVID-19 pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected millions of people worldwide, with acute respiratory distress syndrome (ARDS) being the most common severe condition of pulmonary involvement. Despite its involvement in the lungs, SARS-CoV-2 causes multiple extrapulmonary manifestations, including manifestations in the musculoskeletal system. Several cases involving bone, joint, muscle, neurovascular and soft tissues were reported shortly after pandemic onset. Even after the acute infection has resolved, many patients experience persistent symptoms and a decrease in quality of life, a condition known as post-COVID syndrome or long COVID. COVID-19 vaccines have been widely available since December 2020, preventing millions of deaths during the pandemic. However, adverse reactions, including those involving the musculoskeletal system, have been reported in the literature. Therefore, the primary goal of this article is to review the main imaging findings of SARS-CoV-2 involvement in the musculoskeletal system, including acute, subacute, chronic and postvaccination manifestations., (© 2023. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

An increasing number of indications are emerging for point-of-care ultrasound to diagnose and treat musculoskeletal complaints. Low cost, dynamic imaging, immediate results, and added advantage of portability have made ultrasound an important imaging modality in urgent care, emergency department (ED), and other musculoskeletal care clinics. This article reviews the use of musculoskeletal ultrasound to help evaluate articular and periarticular structures to evaluate for mechanical injuries and inflammatory arthritis., Competing Interests: Disclosure A.L. Day: Royalties from Springer International for Manual of Musculoskeletal Ultrasound Textbook. M.J. Kohler, Royalties from Springer International for Musculoskeletal Ultrasound in Rheumatology Review Textbook; Janssen/Johnson and Johnson-Medical Advisory Board; Setpoint Medical-Research PI; Novartis-Medical Advisory Board. E. Chew and L. Nazarian: No disclosures., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Ultrasound imaging is a safe diagnostic tool that has become ubiquitous, in part due to its portability, effectiveness, and safety record. The latter is aided be adherence to regulatory limits, safety guidelines, and the "as low as reasonably achievable" principle. Operators receive feedback on potential tissue heating and mechanical effects through output display standard that shows the thermal index and mechanical index. To prevent the spread of infections, ultrasound transducers require varying levels of disinfection based on procedure invasiveness, following detailed infection control protocols, even in resource-limited settings. Ergonomic sonography practices are emphasized to prevent work-related musculoskeletal disorders (WRMSD), with professional organizations encouraging the use of ergonomically designed equipment and proper body positioning during scans. Operators should understand and properly apply ergonomic practices. Administrators play a key role by providing ergonomic equipment, education on WRMSD prevention, and creating workplace policies., Competing Interests: Disclosure The authors have no relevant disclosures., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: The workload of musculoskeletal radiologists has come under pressure. Our objective was to estimate the reading times of common musculoskeletal MRI examinations., Methods: A total of 144 radiologists were asked to estimate reading times (including interpretation and reporting) for MRI of the shoulder, elbow, wrist, hip, knee, and ankle. Multivariate linear regression analyses were performed., Results: Reported median reading times with interquartile range (IQR) for the shoulder, elbow, wrist, hip, knee, and ankle were 10 (IQR 6-14), 10 (IQR 6-14), 11 (IQR 7.5-14.5), 10 (IQR 6.6-13.4), 8 (IQR 4.6-11.4), and 10 (IQR 6.5-13.5) min, respectively. Radiologists aged 35-44 years reported shorter reading times for the shoulder (β coefficient [β] = B-3.412, p = 0.041), hip (β = -3.596, p = 0.023), and knee (β = -3.541, p = 0.013) than radiologists aged 45-54 years. Radiologists not working in an academic/teaching hospital reported shorter reading times for the hip (β = -3.611, p = 0.025) and knee (β = -3.038, p = 0.035). Female radiologists indicated longer reading times for all joints (β of 2.592 to 5.186, p ≤ 0.034). Radiologists without musculoskeletal fellowship training indicated longer reading times for the shoulder (β = 4.604, p = 0.005), elbow (β = 3.989, p = 0.038), wrist (β = 4.543, p = 0.014), and hip (β = 2.380, p = 0.119). Radiologists with <5 years of post-residency experience indicated longer reading times for all joints (β of 5.355 to 6.984, p ≤ 0.045), and radiologists with 5-10 years of post-residency experience reported longer reading time for the knee (β = 3.660, p = 0.045) than those with >10 years of post-residency experience., Conclusions: There is substantial variation among radiologists in reported reading times for common musculoskeletal MRI examinations. Several radiologist-related determinants appear to be associated with reading speed, including age, gender, hospital type, training, and experience.

Introduction: Artificial intelligence techniques such as machine learning have made it possible to create neural networks for the recognition of MRI and X-ray images, which has improved the diagnosis and treatment of orthopedic diseases. The purpose of our review was to synthesize and analyze publications on the use of artificial intelligence in the diagnosis and treatment of diseases of the musculoskeletal system., Materials and Methods: Utilizing a systematic narrative review method, we evaluated 348 publications from 2019 to 2024, with 201 of these being openly accessible. These publications were sourced from the Scopus and PubMed databases, focusing on key terms such as "Machine Learning", "Orthopedic Diagnostics", "Virtual Reality", and "Diseases of the Musculoskeletal System". We selected 89 publications for detailed analysis to identify the primary AI methods employed in orthopedics and to assess their diagnostic and therapeutic efficacy. During the literature analysis, the main areas were determined: the main methods of artificial intelligence used in orthopedics and the results of their application for diagnosis and treatment., Results: The analysis of publications showed the effectiveness of the use of AI in the analysis of MRI, CT and X-ray images. Techniques used by AI, such as machine learning, deep learning, virtual reality, and their effectiveness in performing diagnostic and treatment procedures were considered., Conclusions: The use of artificial intelligence in the diagnosis and treatment of orthopedic diseases demonstrated an increase in diagnostic accuracy, which contributed improvement of treatment results.

Musculoskeletal (MSK) pain leads to significant healthcare utilization, decreased productivity, and disability globally. Due to its complex etiology, MSK pain is often chronic and challenging to manage effectively. Disparities in pain management-influenced by provider implicit biases and patient race, gender, age, and socioeconomic status-contribute to inconsistent outcomes. Interventional radiology (IR) provides innovative solutions for MSK pain through minimally invasive procedures, which can alleviate symptoms and reduce reliance on opioids. However, IR services may be underutilized, especially due to current treatment paradigms, referral patterns, and in areas with limited access to care. Artificial intelligence (AI) presents a promising avenue to address these inequities by analyzing large datasets to identify disparities in pain management, recognizing implicit biases, improving cultural competence, and enhancing pain assessment through multimodal data analysis. Additionally, patients who may benefit from an IR pain procedure for their MSK pain may then receive more information through their providers after being identified as a candidate by AI sifting through the electronic medical record. By leveraging AI, healthcare providers can potentially mitigate their biases while ensuring more equitable pain management and better overall outcomes for patients., Competing Interests: Declaration of competing interest The authors report no potential conflicts of interest., (Copyright © 2024. Published by Elsevier Inc.)

OBJECTIVE: To determine if maladaptive imaging beliefs correlated with, and predicted pain interference and physical function outcomes in people with musculoskeletal pain disorders. DESIGN: A prospective cohort study of patients with musculoskeletal disorders receiving outpatient physical therapy from April 2022 to August 2023. METHODS: Four questions about imaging were asked to assess maladaptive beliefs, the need to rule out serious conditions, guide treatment, determine diagnosis, and validate symptoms. Correlations with beliefs and outcomes were assessed using Kendall's tau rank and Spearman's rho correlation coefficients. Generalized linear models determined if these beliefs predicted outcomes at baseline and 6 weeks. RESULTS: The cohort included 152 participants (mean [standard deviation] age: 56.13 [15.13]; 32.2% male). Maladaptive imaging beliefs correlated positively with pain interference and negatively with physical function. The need to rule out serious conditions and validate symptoms correlated with pain interference (range: τb = 0.17, 0.20; P = .003, .0121) and physical function (range: ρ = -0.22, -0.22; P = .006, .008). All but 1 belief correlated with pain interference (range: τb = 0.19, 0.24; P <.001, .004) and physical function (range: ρ = -0.26, -0.21; P = .001, .009) at 6 weeks. Each additional belief slightly increased pain interference at 6 weeks ( β = 0.01; 95% CI: 0.001, 0.03; P = .04) and lowered physical function at both baseline ( β = -0.97; 95% CI: -1.66, -0.28; P = .01) and 6 weeks ( β = -0.76; 95% CI: -1.37, -0.15; P = .02). CONCLUSION: Maladaptive imaging beliefs were significantly (albeit weakly) correlated with pain and physical function. Each additional maladaptive imaging belief increased pain interference at 6 weeks and lowered physical function at baseline and 6 weeks. Beliefs about the necessity of imaging to properly manage musculoskeletal disorders may influence outcomes. J Orthop Sports Phys Ther 2024;54(9):1-10. Epub 5 July 2024. doi:10.2519/jospt.2024.12625 .

Pseudolesions in bone and muscle are encountered mostly incidentally in routine imaging studies, especially due to the recent advancements on many different imaging modalities. These lesions can be categorized into the following categories: normal variants; congenital; iatrogenic; degenerative; and postoperative. In this review, we discuss the many different radiological characteristics of musculoskeletal pseudolesions that appear on imaging, which can prevent non-essential additional studies., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Competing Interests: Disclosure The authors report no conflicts of interest in this work.

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Multidisciplinary collaboration and radiology-pathology correlation are key components to advancing our knowledge and understanding of musculoskeletal disease and improving clinical care. The International Skeletal Society was founded on this principle and in its 50-year history it has successfully cultivated a globally recognized and respected Annual Meeting and Refresher Course to foster interest and share expertise in musculoskeletal disorders. The Society's consistent efforts for outreach and intersociety connections with MSK societies around the world have further strengthened its heritage. This look back on the Society's first 50 years recounts how it all started, where it has been, and provides insights on its promising future, not only in its support for education and scientific discovery, but also in its enrichment of precision medicine., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Purpose: To systematically review the use of artificial intelligence (AI) in musculoskeletal (MSK) ultrasound (US) with an emphasis on AI algorithm categories and validation strategies., Material and Methods: An electronic literature search was conducted for articles published up to January 2024. Inclusion criteria were the use of AI in MSK US, involvement of humans, English language, and ethics committee approval., Results: Out of 269 identified papers, 16 studies published between 2020 and 2023 were included. The research was aimed at predicting diagnosis and/or segmentation in a total of 11 (69%) out of 16 studies. A total of 11 (69%) studies used deep learning (DL)-based algorithms, three (19%) studies employed conventional machine learning (ML)-based algorithms, and two (12%) studies employed both conventional ML- and DL-based algorithms. Six (38%) studies used cross-validation techniques with K-fold cross-validation being the most frequently employed (n = 4, 25%). Clinical validation with separate internal test datasets was reported in nine (56%) papers. No external clinical validation was reported., Conclusion: AI is a topic of increasing interest in MSK US research. In future studies, attention should be paid to the use of validation strategies, particularly regarding independent clinical validation performed on external datasets., (© 2024. The Author(s).)

Magnetic resonance imaging (MRI) is crucial for accurately diagnosing a wide spectrum of musculoskeletal conditions due to its superior soft tissue contrast resolution. However, the long acquisition times of traditional two-dimensional (2D) and three-dimensional (3D) fast and turbo spin-echo (TSE) pulse sequences can limit patient access and comfort. Recent technical advancements have introduced acceleration techniques that significantly reduce MRI times for musculoskeletal examinations. Key acceleration methods include parallel imaging (PI), simultaneous multi-slice acquisition (SMS), and compressed sensing (CS), enabling up to eightfold faster scans while maintaining image quality, resolution, and safety standards. These innovations now allow for 3- to 6-fold accelerated clinical musculoskeletal MRI exams, reducing scan times to 4 to 6 min for joints and spine imaging. Evolving deep learning-based image reconstruction promises even faster scans without compromising quality. Current research indicates that combining acceleration techniques, deep learning image reconstruction, and superresolution algorithms will eventually facilitate tenfold accelerated musculoskeletal MRI in routine clinical practice. Such rapid MRI protocols can drastically reduce scan times by 80-90% compared to conventional methods. Implementing these rapid imaging protocols does impact workflow, indirect costs, and workload for MRI technologists and radiologists, which requires careful management. However, the shift from conventional to accelerated, deep learning-based MRI enhances the value of musculoskeletal MRI by improving patient access and comfort and promoting sustainable imaging practices. This article offers a comprehensive overview of the technical aspects, benefits, and challenges of modern accelerated musculoskeletal MRI, guiding radiologists and researchers in this evolving field., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

This narrative review explores recent advancements and applications of modern low-field (≤ 1 Tesla) magnetic resonance imaging (MRI) in musculoskeletal radiology. Historically, high-field MRI systems (1.5 T and 3 T) have been the standard in clinical practice due to superior image resolution and signal-to-noise ratio. However, recent technological advancements in low-field MRI offer promising avenues for musculoskeletal imaging. General principles of low-field MRI systems are being introduced, highlighting their strengths and limitations compared to high-field counterparts. Emphasis is placed on advancements in hardware design, including novel magnet configurations, gradient systems, and radiofrequency coils, which have improved image quality and reduced susceptibility artifacts particularly in musculoskeletal imaging. Different clinical applications of modern low-field MRI in musculoskeletal radiology are being discussed. The diagnostic performance of low-field MRI in diagnosing various musculoskeletal pathologies, such as ligament and tendon injuries, osteoarthritis, and cartilage lesions, is being presented. Moreover, the discussion encompasses the cost-effectiveness and accessibility of low-field MRI systems, making them viable options for imaging centers with limited resources or specific patient populations. From a scientific standpoint, the amount of available data regarding musculoskeletal imaging at low-field strengths is limited and often several decades old. This review will give an insight to the existing literature and summarize our own experiences with a modern low-field MRI system over the last 3 years. In conclusion, the narrative review highlights the potential clinical utility, challenges, and future directions of modern low-field MRI, offering valuable insights for radiologists and healthcare professionals seeking to leverage these advancements in their practice., (© 2024. The Author(s).)

Purpose: The ultrasound specialists face a substantial issue with work-related musculoskeletal disorders (WMSDs), characterized by occupation-related pain, reaching an estimated frequency of up to 90.5%. The type and location of pain vary depending on the sonographers specialty and the specific anatomical areas being examined. Our study aimed to assess the prevalence and intensity of pain among Polish doctors from various specialties conducting ultrasound examinations., Materials and Methods: The study has been performed between July and December 2023, involving 90 participants (51.9% women) actively practicing ultrasound diagnostics. The data collection process utilized a structured questionnaire developed by the researchers for this study. The questionnaire covered personal factors, including gender and age, and work-related aspects like workplace, specialty, sonography experience, and the types of examinations conducted. Statistical analysis involved both descriptive statistics and correlation analysis., Results: The age distribution ranged from 26 to 74 years, with mean (SD) of 43.1 (12.2) years. The leading specialties among physicians were radiology (22.2%) and internal medicine (22.2%). Among all physicians, 65.6% reported pain during or after ultrasound scans, with a mean (SD) pain intensity of 6.17 (2) in a 1-10 scale. The analysis indicated no correlation between age and gender and the occurrence of discomfort., Conclusions: WMSDs pose a significant risk to Polish sonographers, especially those specializing in vascular surgery, general surgery and obstetrics and gynecology specialties. The study underscores a notable deficit, with only 10% of the personnel reporting training in ergonomics for ultrasound work., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Computed tomography (CT) is a common modality employed for musculoskeletal imaging. Conventional CT techniques are useful for the assessment of trauma in detection, characterization and surgical planning of complex fractures. CT arthrography can depict internal derangement lesions and impact medical decision making of orthopedic providers. In oncology, CT can have a role in the characterization of bone tumors and may elucidate soft tissue mineralization patterns. Several advances in CT technology have led to a variety of acquisition techniques with distinct clinical applications. These include four-dimensional CT, which allows examination of joints during motion; cone-beam CT, which allows examination during physiological weight-bearing conditions; dual-energy CT, which allows material decomposition useful in musculoskeletal deposition disorders (e.g., gout) and bone marrow edema detection; and photon-counting CT, which provides increased spatial resolution, decreased radiation, and material decomposition compared to standard multi-detector CT systems due to its ability to directly translate X-ray photon energies into electrical signals. Advanced acquisition techniques provide higher spatial resolution scans capable of enhanced bony microarchitecture and bone mineral density assessment. Together, these CT acquisition techniques will continue to play a substantial role in the practices of orthopedics, rheumatology, metabolic bone, oncology, and interventional radiology., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

In musculoskeletal imaging, CT is used in a wide range of indications, either alone or in a synergistic approach with MRI. While MRI is the preferred modality for the assessment of soft tissues and bone marrow, CT excels in the imaging of high-contrast structures, such as mineralized tissue. Additionally, the introduction of dual-energy CT in clinical practice two decades ago opened the door for spectral imaging applications. Recently, the advent of photon-counting detectors (PCDs) has further advanced the potential of CT, at least in theory. Compared to conventional energy-integrating detectors (EIDs), PCDs provide superior spatial resolution, reduced noise, and intrinsic spectral imaging capabilities. This review briefly describes the technical advantages of PCDs. For each technical feature, the corresponding applications in musculoskeletal imaging will be discussed, including high-spatial resolution imaging for the assessment of bone and crystal deposits, low-dose applications such as whole-body CT, as well as spectral imaging applications including the characterization of crystal deposits and imaging of metal hardware. Finally, we will highlight the potential of PCD-CT in emerging applications, underscoring the need for further preclinical and clinical validation to unleash its full clinical potential., (© 2024. The Author(s).)

Senescent cells play a vital role in the pathogenesis of musculoskeletal (MSK) diseases, such as chronic inflammatory joint disorders, rheumatoid arthritis (RA), and osteoarthritis (OA). Cellular senescence in articular joints represents a response of local cells to persistent stress that leads to cell-cycle arrest and enhanced production of inflammatory cytokines, which in turn perpetuates joint damage and leads to significant morbidities in afflicted patients. It has been recently discovered that clearance of senescent cells by novel "senolytic" therapies can attenuate the chronic inflammatory microenvironment of RA and OA, preventing further disease progression and supporting healing processes. To identify patients who might benefit from these new senolytic therapies and monitor therapy response, there is an unmet need to identify and map senescent cells in articular joints and related musculoskeletal tissues. To fill this gap, new imaging biomarkers are being developed to detect and characterize senescent cells in human joints and musculoskeletal tissues. This review article will provide an overview of these efforts. New imaging biomarkers for senescence cells are expected to significantly improve the specificity of state-of-the-art imaging technologies for diagnosing musculoskeletal disorders., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Magnetic resonance-guided focused ultrasound (MRgFUS) is a noninvasive, incisionless, radiation-free technology used to ablate tissue deep within the body. This technique has gained increased popularity following FDA approval for treatment of pain related to bone metastases and limited approval for treatment of osteoid osteoma. MRgFUS delivers superior visualization of soft tissue targets in unlimited imaging planes and precision in targeting and delivery of thermal dose which is all provided during real-time monitoring using MR thermometry. This paper provides an overview of the common musculoskeletal applications of MRgFUS along with updates on clinical outcomes and discussion of future applications., (© 2024. The Author(s).)

Musculoskeletal (MSK) disorders are associated with large impacts on patient's pain and quality of life. Conventional morphological imaging of tissue structure is limited in its ability to detect pain generators, early MSK disease, and rapidly assess treatment efficacy. Positron emission tomography (PET), which offers unique capabilities to evaluate molecular and metabolic processes, can provide novel information about early pathophysiologic changes that occur before structural or even microstructural changes can be detected. This sensitivity not only makes it a powerful tool for detection and characterization of disease, but also a tool able to rapidly assess the efficacy of therapies. These benefits have garnered more attention to PET imaging of MSK disorders in recent years. In this narrative review, we discuss several applications of multimodal PET imaging in non-oncologic MSK diseases including arthritis, osteoporosis, and sources of pain and inflammation. We also describe technical considerations and recent advancements in technology and radiotracers as well as areas of emerging interest for future applications of multimodal PET imaging of MSK conditions. Overall, we present evidence that the incorporation of PET through multimodal imaging offers an exciting addition to the field of MSK radiology and will likely prove valuable in the transition to an era of precision medicine., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Competing Interests: Disclosure The authors report no conflicts of interest in this work.

Background Current terms used to describe the MRI findings for musculoskeletal infections are nonspecific and inconsistent. Purpose To develop and validate an MRI-based musculoskeletal infection classification and scoring system. Materials and Methods In this retrospective cross-sectional internal validation study, a Musculoskeletal Infection Reporting and Data System (MSKI-RADS) was designed. Adult patients with radiographs and MRI scans of suspected extremity infections with a known reference standard obtained between June 2015 and May 2019 were included. The scoring categories were as follows: 0, incomplete imaging; I, negative for infection; II, superficial soft-tissue infection; III, deeper soft-tissue infection; IV, possible osteomyelitis (OM); V, highly suggestive of OM and/or septic arthritis; VI, known OM; and NOS (not otherwise specified), nonspecific bone lesions. Interreader agreement for 20 radiologists from 13 institutions (intraclass correlation coefficient [ICC]) and true-positive rates of MSKI-RADS were calculated and the accuracy of final diagnoses rendered by the readers was compared using generalized estimating equations for clustered data. Results Among paired radiographs and MRI scans from 208 patients (133 male, 75 female; mean age, 55 years ± 13 [SD]), 20 were category I; 34, II; 35, III; 30, IV; 35, V; 18, VI; and 36, NOS. Moderate interreader agreement was observed among the 20 readers (ICC, 0.70; 95% CI: 0.66, 0.75). There was no evidence of correlation between reader experience and overall accuracy ( P = .94). The highest true-positive rate was for MSKI-RADS I and NOS at 88.7% (95% CI: 84.6, 91.7). The true-positive rate was 73% (95% CI: 63, 80) for MSKI-RADS V. Overall reader accuracy using MSKI-RADS across all patients was 65% ± 5, higher than final reader diagnoses at 55% ± 7 ( P < .001). Conclusion MSKI-RADS is a valid system for standardized terminology and recommended management of imaging findings of peripheral extremity infections across various musculoskeletal-fellowship-trained reader experience levels. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Schweitzer in this issue.

Purpose: Cone-Beam CT (CBCT) is well established in orofacial diagnostic imaging and is currently expanding into musculoskeletal applications. This systematic review sought to update the knowledge base on radiation dose comparisons between imaging modalities in MSK imaging and consider how research studies have reported dose measures., Methods: This review utilised a database search and an online literature tool. Studies with potential relevance were screened then before full text review, each performed by two independent reviewers, with a third independent reviewer available for conflicts. Data was extracted using a bespoke tool created within the literature tool., Results: 21 studies were included in the review which compared CBCT with MSCT (13), conventional radiography (1), or both (7). 19 studies concluded that CBCT provided a reduced radiation dose when compared with MSCT: the factor of reduction ranging from 1.71 to 50 with an average of 12. Studies comparing CBCT to DR found DR to have an average dose reduction of 4.55., Conclusions: The claims that CBCT produces a lower radiation dose than MSCT is borne out with most studies confirming doses less than half that of MSCT. Fewer studies include DR as a comparator but confirm that CBCT results in a higher effective dose on average, with scope for CBCT to provide an equivalent radiation dose. This review highlighted a need for consistency in methodology when conducting studies which compare radiation dose across different technologies. Potential solutions lie outside the scope of this review, likely requiring multi-discipline approach to ensure a cohesive outcome., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: To assess the feasibility of using large language models (LLMs), specifically ChatGPT-4, to generate concise and accurate layperson summaries of musculoskeletal radiology reports., Methods: Sixty radiology reports, comprising 20 MR shoulder, 20 MR knee, and 20 MR lumbar spine reports, were obtained via PACS. The reports were deidentified and then submitted to ChatGPT-4, with the prompt "Produce an organized and concise layperson summary of the findings of the following radiology report. Target a reading level of 8-9th grade and word count <300 words." Three (two primary and one later added for validation) independent readers evaluated the summaries for completeness and accuracy compared to the original reports. Summaries were rated on a scale of 1 to 3: 1) summaries that were incorrect or incomplete, potentially providing harmful or confusing information; 2) summaries that were mostly correct and complete, unlikely to cause confusion or harm; and 3) summaries that were entirely correct and complete., Results: All 60 responses met the criteria for word count and readability. Mean ratings for accuracy were 2.58 for reader 1, 2.71 for reader 2, and 2.77 for reader 3. Mean ratings for completeness were 2.87 for reader 1 and 2.73 for reader 2 and 2.87 for reader 3. For accuracy, reader 1 identified three summaries as a 1, reader 2 identified one, and reader 3 identified none. For the two primary readers, inter-reader agreement was low for accuracy (kappa 0.33) and completeness (kappa 0.29). There were no statistically significant changes in inter-reader agreement when the third reader's ratings were included in analysis., Conclusion: Overall ratings for accuracy and completeness of the AI-generated layperson report summaries were high with only a small minority likely to be confusing or inaccurate. This study illustrates the potential for leveraging generative AI, such as ChatGPT-4, to automate the production of patient-friendly summaries for musculoskeletal MR imaging., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Purpose: To evaluate the current evidence regarding the use of ultrasound elastography for assessing non-invasive, non-pharmacological interventions for eliciting changes in musculoskeletal stiffness., Methods: A systematic search of MEDLINE, CINAHL, EMBASE, and Web of Science databases was performed in accordance with Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Information on measurement and intervention procedures was extracted. Bias was assessed using Cochrane Risk of Bias or Risk of Bias In Non-randomised Studies of Interventions (ROBINS-I) tools for studies with true or quasi-experimental designs, respectively. Analyses were conducted for adequately powered subgroups based on intervention type, measurement site, and population assessed., Results: Twenty-one studies were included in the review. Overall risk of bias was low for true experimental studies and moderate for quasi-experimental studies. Subgroup analyses indicated a large overall effect for interventions involving manual physiotherapy and taping/splinting for reducing masseter muscle stiffness in patients with masticatory muscle disorders ( g  = 1.488, 95% CI = 0.320-2.655, p  = 0.013). Analyses for other intervention types and patient groups were underpowered., Conclusion: Ultrasound elastography demonstrates clinical applicability for assessing non-invasive, non-pharmacological interventions for musculoskeletal stiffness. However, the comparative efficacy of these interventions for modulating tissue stiffness remains inconclusive.