Evidence-based position paper on Physical and Rehabilitation Medicine (PRM) professional practice on telerehabilitation. The European PRM position (UEMS PRM Section).

INTRODUCTION: The evidence on the utility and effectiveness of rehabilitation interventions delivered via telerehabilitation is growing rapidly. Telerehabilitation is expected to have a key role in rehabilitation in the future. AIM: The aim of this evidence-based position paper (EBPP) is to improve PRM physicians' professional practice in telerehabilitation to be delivered to improve functioning and to reduce activity limitations and/or participation restrictions in individuals with a variety of disabling health conditions. METHODS: To produce recommendations for PRM physicians on telerehabilitation, a systematic review of the literature and a consensus procedure by means of a Delphi process have been performed involving the delegates of all European countries represented in the UEMS PRM Section. RESULTS: The systematic literature review is reported together with the 32 recommendations resulting from the Delphi procedure. CONCLUSIONS: It is recommended that PRM physicians deliver rehabilitation services remotely, via digital means or using communication technologies to eligible individuals, whenever required and feasible in a variety of health conditions in favor of the patient and his/her family, based on evidence of effectiveness and in compliance with relevant regulations. This EBPP represents the official position of the European Union through the UEMS PRM Section and designates the professional role of PRM physicians in telerehabilitation.

Muscle Cross-Sectional Area Segmentation in Transverse Ultrasound Images Using Vision Transformers.

Automatically measuring a muscle's cross-sectional area is an important application in clinical practice that has been studied extensively in recent years for its ability to assess muscle architecture. Additionally, an adequately segmented cross-sectional area can be used to estimate the echogenicity of the muscle, another valuable parameter correlated with muscle quality. This study assesses state-of-the-art convolutional neural networks and vision transformers for automating this task in a new, large, and diverse database. This database consists of 2005 transverse ultrasound images from four informative muscles for neuromuscular disorders, recorded from 210 subjects of different ages, pathological conditions, and sexes. Regarding the reported results, all of the evaluated deep learning models have achieved near-to-human-level performance. In particular, the manual vs. the automatic measurements of the cross-sectional area exhibit an average discrepancy of less than 38.15 mm2, a significant result demonstrating the feasibility of automating this task. Moreover, the difference in muscle echogenicity estimated from these two readings is only 0.88, another indicator of the proposed method's success. Furthermore, Bland−Altman analysis of the measurements exhibits no systematic errors since most differences fall between the 95% limits of agreements and the two readings have a 0.97 Pearson's correlation coefficient (p < 0.001, validation set) with ICC (2, 1) surpassing 0.97, showing the reliability of this approach. Finally, as a supplementary analysis, the texture of the muscle's visible cross-sectional area was examined using deep learning to investigate whether a classification between healthy subjects and patients with pathological conditions solely from the muscle texture is possible. Our preliminary results indicate that such a task is feasible, but further and more extensive studies are required for more conclusive results.

Automatic Extraction of Muscle Parameters with Attention UNet in Ultrasonography.

Automatically delineating the deep and superficial aponeurosis of the skeletal muscles from ultrasound images is important in many aspects of the clinical routine. In particular, finding muscle parameters, such as thickness, fascicle length or pennation angle, is a time-consuming clinical task requiring both human labour and specialised knowledge. In this study, a multi-step solution for automating these tasks is presented. A process to effortlessly extract the aponeurosis for automatically measuring the muscle thickness has been introduced as a first step. This process consists mainly of three parts. In the first part, the Attention UNet has been incorporated to automatically delineate the boundaries of the studied muscles. Afterwards, a specialised post-processing algorithm was utilised to improve (and correct) the segmentation results. Lastly, the calculation of the muscle thickness was performed. The proposed method has achieved similar to a human-level performance. In particular, the overall discrepancy between the automatic and the manual muscle thickness measurements was equal to 0.4 mm, a significant result that demonstrates the feasibility of automating this task. In the second step of the proposed methodology, the fascicle's length and pennation angle are extracted through an unsupervised pipeline. Initially, filtering is applied to the ultrasound images to further distinguish the tissues from the other muscle structures. Later, the well-known K-Means algorithm is used to isolate them successfully. As the last step, the dominant angle of the segmented muscle tissues is reported and compared with manual measurements. The proposed pipeline is showing very promising results in the evaluated dataset. Specifically, in the calculation of the pennation angle, the overall discrepancy between the automatic and the manual measurements was less than 2.22° (degrees), once more comparable with the human-level performance. Finally, regarding the fascicle length measurements, the results were divided based on the muscle properties. In the muscles where a large portion (or all) of the fascicles are located between the upper and lower aponeuroses, the proposed pipeline exhibits superb performance; otherwise, overall accuracy deteriorates due to errors caused by the trigonometric approximations needed for the length calculation.

The ultrasonographic measurement of muscle thickness in sarcopenia. A prediction study.

BACKGROUND: Sarcopenia is a common disease in the elderly. Although extensive research has been conducted on muscle mass and quality assessment tools, there are still certain drawbacks preventing their universal use. AIM: The aim of this study was the evaluation of the thickness of head, neck, upper and lower limb muscles measured with ultrasonography, as a potential predictory tool in sarcopenia. DESIGN: Prediction study. SETTING: The Outpatient Sarcopenia Clinic of the Rehabilitation Department of the University Hospital of Patras. POPULATION: Ninety-four individuals (27 men and 67 women) with a mean age of 75.6 years (SD=6.6), referred for sarcopenia screening, participated in this study. METHODS: The muscle thickness was measured with transverse and longitudinal ultrasound scans bilaterally. RESULTS: The thickness of the geniohyoid and medial head of gastrocnemius muscle in all ultrasound sections, and the thickness of the rectus femoris and vastus intermedius muscle, in specific sections, was found to be significantly decreased in patients with sarcopenia (P<0.05). The Receiver Operating Characteristic (ROC) curve analysis of the ultrasound muscle thickness measurements resulted in a significant association with sarcopenia. In the case of the geniohyoid muscle, the measured area under the ROC curve was found to be the highest (0.79). The optimal cut-off for the prediction of sarcopenia from the geniohyoid muscle was 0.65 cm with sensitivity equal to 75.0% and specificity equal to 66.7%. CONCLUSIONS: The results of this study have shown that the thickness of the neck and lower limb muscles measured ultrasonographically can be utilized in the prediction of sarcopenia with high sensitivity and specificity. CLINICAL REHABILITATION IMPACT: The prevalence of sarcopenia in the geriatric population and the rehabilitation wards is reported to be high. Therefore, an easy, fast, low cost and with no risk, widely available method such as ultrasonography could be an extremely valuable tool for the screening and follow-up of sarcopenia.

EURO-MUSCULUS/USPRM Global Report on Musculoskeletal Ultrasound Publications.

The developments in technology have improved access to the use of musculoskeletal ultrasound (MSUS) in different clinical settings. Accordingly, MSUS has been applied to a wide range of musculoskeletal problems including inflammatory and degenerative diseases, sport injuries, and regional pain syndromes both for clinical practice and research. In this report, the authors aimed to globally examine the publications on MSUS among different specialties, countries, and topics. Sixteen reviewers under the umbrella of the European Musculoskeletal Ultrasonography Society Group and the Ultrasound Study Group of International Society of Physical and Rehabilitation Medicine have evaluated approximately 15,000 publications on MSUS. The authors believe that the results of this comparative analysis may provide a holistic snapshot with regard to the utility of MSUS, not only for clinicians/academicians but also for the industry. Accordingly, while aiming to further increase their awareness, this article would possibly guide future investments as well.

Reliability of muscle thickness measurements in ultrasonography.

This study aims to clarify some of the issues associated with the reliable measurement of muscle thickness on ultrasonographic images of the musculoskeletal system, namely the repeatability of measurements in different time frames, the effect of body side selection, and the effect of scan orientation. Ultrasound scans were performed on muscles associated with essential daily activities: geniohyoid, masseter, anterior arm muscles, rectus femoris, vastus intermedius, tibialis anterior, and gastrocnemius. Measurements of the muscle thickness were performed and repeated after 1, 6, and 24 h, on both dominant and nondominant side, using both transverse and longitudinal scans. Thirteen healthy volunteers (eight males and five females, mean age = 24 years, SD = 2.86, range = 19-29) were included. The intraclass correlation coefficient (ICC) was calculated between the baseline and the 1-, 6-, and 24-h interval, using a two-way mixed model of absolute agreement. The ICC ranged from 0.295 for the longitudinal scan of the left masseter muscle in the 6-h interval to 0.991 for the longitudinal scan of the nondominant anterior arm muscles in the 24-h interval. The results indicate that there is variable reliability of the measurements depending on the muscle, time frame, body side, and scan orientation. Consequently, the choice of these parameters can affect the validity of the measurements. Further investigation on a larger scale is required to establish the preferred parameters for each anatomical site.

Fostering the highest educational standards in Physical and Rehabilitation Medicine: The European PRM board strategy for ensuring overall quality of rehabilitation education and care.

The harmonization of staff education is a key element for ensuring the highest standard of rehabilitation care across Europe. With this aim, the European Union of Medical Specialists (UEMS) has created a Common Training Framework, which consists of a common set of knowledge, skills and competencies for postgraduate medical training. As a body linked to the Physical and Rehabilitation (PRM) Section of the UEMS, the European PRM Board is committed to promoting the harmonization of PRM physicians qualifications. The European PRM Board accomplishes this mission, not only by determining the theoretical knowledge necessary for the practice of the PRM specialty, and the core competencies (training outcomes) to be achieved at the end of training, but also by ascertaining that a standard level of education is achieved and maintained by PRM physicians, through a medically driven system of certification. This paper provides an overview of the methodology and outcomes of the European PRM Board examination, while showing how the approach to PRM education should be considered as a reference point by scientific societies, higher education institutions, health policymakers, patients associations, and all the other bodies caring for high-quality rehabilitation provision to disabled people, at the national and European level.

Muscle Type and Gender Recognition Utilising High-Level Textural Representation in Musculoskeletal Ultrasonography.

Human assistive technology and computer-aided diagnosis is an emerging field in the area of medical imaging. Following the recent advances in this domain, a study for integrating machine learning techniques in musculoskeletal ultrasonography images was conducted. The goal of this attempt was to investigate how feature extraction techniques, that capture higher-level information, perform in identifying human characteristics. The potential success of these techniques could lead to significant improvement of the current assessment methods-as the gray-scale image analysis-for distinguishing healthy and pathologic conditions, that are heavily dependent on the image-acquisition system. The contribution of this work is threefold. First, a new privately held data set of 74 healthy patients was presented. This data set included musculoskeletal ultrasound images from four muscles of the human body, namely the biceps brachii, tibialis anterior, gastrocnemius medialis and rectus femoris, recorded in the transverse and longitudinal plane. Second, two classification tasks were performed, namely, gender and muscle-type recognition, to assess the performance of the proposed method for successfully identifying differences in the texture of the examined muscle sections. Third, a novel method used with great success in the computer vision domain was presented, allowing the extraction of a high-level feature representation, by encoding the distribution of locally invariant texture descriptors. On the muscle-type recognition our method achieved an 87.07% classification rate, and on the task of gender recognition it surpassed state-of-the-art textural representations, reported in the literature in almost all the examined muscle sections.

Evidence-based position paper on Physical and Rehabilitation Medicine professional practice for persons with acute and chronic pain. The European PRM position (UEMS PRM Section).

BACKGROUND: Pain is a frequent complaint from patients undergoing rehabilitation. It can be a major problem and can lead to several activity limitations and participation restrictions. For this reason, when the Professional Practice Committee (PPC) of the Physical and Rehabilitation Medicine (PRM) Section of the European Union of Medical Specialists (UEMS) decided to prepare evidence-based practice position papers (EBPPs) on the most relevant fields of PRM, a paper on the role of the PRM specialist on pain conditions was also included. The goals of this paper are to provide recommendations on the PRM physician's role in pain management; how to address this major problem and what is the best evidence-based approach for the PRM physician in acute and chronic pain conditions. This paper follows the methodology defined by the Professional Practice Committee of the UEMS-PRM Section. METHODS: A systematic literature search in PubMed was carried out and the results obtained from filtered papers were subjected to four Delphi rounds. RESULTS: Fifteen recommendations were obtained from the Consensus Process and systematic review and were approved by all of the delegates of the UEMS-PRM Section. It is recommended that PRM physicians focus on pain as a primary aim of their interventions, in whatever field they are applying their competencies. It is also recommended that the approach to pain focuses either on reducing the symptoms and improving functioning/reducing disability or recurrences and improving the health condition in the long term avoiding chronicity. CONCLUSIONS: Every PRM specialist encounters the problem of pain and some specialize in this field and their role is greater than that of the regular PRM doctor. Based on the evidence available, it is reasonable to determine the role of the physiatrist in managing pain.