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

Hamstring Muscle Quality Properties Using Texture Analysis of Ultrasound Images

The aim of this study was to examine the intra- and inter-muscular differences of the hamstring muscles using textural analysis of ultrasound (US) images, and the relationship between textural indicators with hamstring torque. Transverse US scans were obtained from 10 young males from four different measurement sites along the thigh of each individual hamstring muscle at rest. Maximum-knee-flexion isometric torque measurements were also obtained. Texture analysis was applied to US images, and five gray-level co-occurrence matrix (GLCM) features were quantified: entropy (ENT), angular second moment (ASM), inverse difference moment (IDM), contrast (CON) and correlation (COR). The intraclass correlation coefficients ranged from 0.77 to 0.99, and the standard error of measurement ranged from 0.06 to 10.05%, indicating high test-retest reliability. Analysis of the variance indicated significant differences between measurement sites and individual muscles, with the proximal measurement sites having greater values for ASM, IDM and COR and lower values for ENT and CON compared with the distal sites. Additionally, only the COR at the proximal measurement site exhibited a significant relationship (r = -0.66) with strength. The present study indicated significant differences among hamstrings and measurement locations with respect to the textural analysis and may provide a novel indicator of hamstring functional properties.

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.

Enhancing Orthopedic Surgery and Treatment Using Artificial Intelligence and Its Application in Health and Dietary Welfare

The current decade has seen an increased usage of high-end digital technologies like machine learning in the field of health care services which enable in supporting and performing different functions with less or no human interventions. The application of machine learning tools in the orthopedic area is gaining more popularity as it can support in analyzing the issues in a more comprehensive manner, provide accurate data, support in forecasting the pattern. It enables offering critical information for taking quick decisions by the medical practitioners in order to enhance the health and dietary care service delivery. The ML tools can support in collecting patient centric data related to orthopedic surgery and also estimate the postoperative complications, level of treatment modalities to be provided, and guide the medical practitioners in taking effective clinical device decisions. The ML approach also supports in providing prediction methods of implementing the ortho surgical outcomes. Furthermore, it can also guide in making better treatment procedures, forecast the patterns, and stream the health care management services for better patient recovery. This study implements a quantitative research approach which will support in sourcing the data from the respondents who are currently working as medical practitioners, orthopedic experts, and radiologists who use ML-based models in making critical decisions related to orthopedic surgery. The researchers chose nearly 149 respondents, and the information was analysed using the IBM SPSS package for gaining critical interpretation. The major analyses cover descriptive analysis, regression analysis, and analysis of variances.

Muscle Composition Analysis of Ultrasound Images: A Narrative Review of Texture Analysis

Skeletal muscle composition, often characterized by the degree of intramuscular adipose tissue, deteriorates with aging and disease and contributes to impairments in function and metabolism. Ultrasound can provide surrogate measures of muscle composition through measurement of echo intensity; however, there are several limitations associated with its analysis. More complex image processing features, broadly known as texture analysis, can also provide surrogates of muscle composition and may circumvent some of the limitations associated with muscle echo intensity. Here, texture features from the intensity histogram, gray-level co-occurrence matrix, run-length matrix, local binary pattern, blob analysis, texture anisotropy index and wavelet analysis are discussed. The purpose of this review was to provide a conceptual understanding of texture analysis as it pertains to muscle composition of ultrasound images.

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.