Reliability and concurrent validity of three-dimensional ultrasound for quantifying knee cartilage volume

Differences in ankle and knee muscle architecture and plantar pressure distribution among women with knee osteoarthritis

BACKGROUND

The aim of this study was to compare the plantar pressure distribution and knee and ankle muscle architecture in women with and without knee osteoarthritis (OA).

METHODS

Fifty women with knee OA (mean age = 52.11 ± 4.96 years, mean Body mass index (BMI) = 30.94 ± 4.23 kg/m2) and 50 healthy women as a control group (mean age = 50.93 ± 3.78 years, mean BMI = 29.06 ± 4.82 kg/m2) were included in the study. Ultrasonography was used to evaluate knee and ankle muscles architecture and femoral cartilage thickness. The plantar pressure distribution was evaluated using the Digital Biometry Scanning System and Milleri software (DIASU, Italy). Static foot posture was evaluated using the Foot Posture Index (FPI), and pain severity was assessed using the Visual Analog Scale.

RESULTS

The OA group exhibited lower muscle thickness in Rectus Femoris (RF) (p = 0.003), Vastus Medialis (VM) (p = 0.004), Vastus Lateralis (p = 0.023), and Peroneus Longus (p = 0.002), as well as lower Medial Gastrocnemius pennation angle (p = 0.049) and higher Fat thickness (FT) in RF (p = 0.033) and VM (p = 0.037) compared to the control group. The OA group showed thinner femoral cartilage thickness (p = 0.001) and higher pain severity (p = 0.001) than the control groups. FPI scores were higher (p = 0.001) in OA group compared to the control group. The plantar pressure distribution results indicated an increase in total surface (p = 0.027), total load (p = 0.002), medial load (p = 0.005), and lateral load (p = 0.002) on dominant side in OA group compared to the control group.

CONCLUSIONS

Knee and ankle muscle architecture, knee extensor muscle FT, and plantar pressure distribution in the dominant foot differed in individuals with knee OA compared to the control group.

Three-dimensional ultrasound to investigate synovitis in first carpometacarpal osteoarthritis: A feasibility study

BACKGROUND

Synovitis is one of the defining characteristics of osteoarthritis (OA) in the carpometacarpal (CMC1) joint of the thumb. Quantitative characterization of synovial volume is important for furthering our understanding of CMC1 OA disease progression, treatment response, and monitoring strategies. In previous studies, three-dimensional ultrasound (3-D US) has demonstrated the feasibility of being a point-of-care system for monitoring knee OA. However, 3-D US has not been tested on the smaller joints of the hand, which presents unique physiological and imaging challenges.

PURPOSE

To develop and validate a novel application of 3-D US to monitor soft-tissue characteristics of OA in a CMC1 OA patient population compared to the current gold standard, magnetic resonance imaging (MRI).

METHODS

A motorized submerged transducer moving assembly was designed for this device specifically for imaging the joints of the hands and wrist. The device used a linear 3-D scanning approach, where a 14L5 2-D transducer was translated over the region of interest. Two imaging phantoms were used to test the linear and volumetric measurement accuracy of the 3-D US device. To evaluate the accuracy of the reconstructed 3-D US geometry, a multilayer monofilament string-grid phantom (10 mm square grid) was scanned. To validate the volumetric measurement capabilities of the system, a simulated synovial tissue phantom with an embedded synovial effusion was fabricated and imaged. Ten CMC1 OA patients were imaged by our 3-D US and a 3.0 T MRI system to compare synovial volumes. The synovial volumes were manually segmented by two raters on the 2D slices of the 3D US reconstruction and MR images, to assess the accuracy and precision of the device for determining synovial tissue volumes. The Standard Error of Measurement and Minimal Detectable Change was used to assess the precision and sensitivity of the volume measurements. Paired sample t-tests were used to assess statistical significance. Additionally, rater reliability was assessed using Intra-Class Correlation (ICC) coefficients.

RESULTS

The largest percent difference observed between the known physical volume of synovial extrusion in the phantom and the volume measured by our 3D US was 1.1% (p-value = 0.03). The mean volume difference between the 3-D US and the gold standard MRI was 1.78% (p-value = 0.48). The 3-D US synovial tissue volume measurements had a Standard Error Measurement (SEm ) of 11.21 mm3 and a Minimal Detectible Change (MDC) of 31.06 mm3 , while the MRI synovial tissue volume measurements had an SEM of 16.82 mm3 and an MDC of 46.63 mm3 . Excellent inter- and intra-rater reliability (ICCs = 0.94-0.99) observed across all imaging modalities and raters.

CONCLUSION

Our results indicate the feasibility of applying 3-D US technology to provide accurate and precise CMC1 synovial tissue volume measurements, similar to MRI volume measurements. Lower MDC and SEm values for 3-D US volume measurements indicate that it is a precise measurement tool to assess synovial volume and that it is sensitive to variation between volume segmentations. The application of this imaging technique to monitor OA pathogenesis and treatment response over time at the patient's bedside should be thoroughly investigated in future studies.

Applications of artificial intelligence in musculoskeletal ultrasound: narrative review

Ultrasonography (US) has become a valuable imaging tool for the examination of the musculoskeletal system. It provides important diagnostic information and it can also be very useful in the assessment of disease activity and treatment response. US has gained widespread use in rheumatology practice because it provides real time and dynamic assessment, although it is dependent on the examiner's experience. The implementation of artificial intelligence (AI) techniques in the process of image recognition and interpretation has the potential to overcome certain limitations related to physician-dependent assessment, such as the variability in image acquisition. Multiple studies in the field of AI have explored how integrated machine learning algorithms could automate specific tissue recognition, diagnosis of joint and muscle pathology, and even grading of synovitis which is essential for monitoring disease activity. AI-based techniques applied in musculoskeletal US imaging focus on automated segmentation, image enhancement, detection and classification. AI-based US imaging can thus improve accuracy, time efficiency and offer a framework for standardization between different examinations. This paper will offer an overview of current research in the field of AI-based ultrasonography of the musculoskeletal system with focus on the applications of machine learning techniques in the examination of joints, muscles and peripheral nerves, which could potentially improve the performance of everyday clinical practice.

Automatic femoral articular cartilage segmentation using deep learning in three-dimensional ultrasound images of the knee

Objective

This study aimed to develop a deep learning-based approach to automatically segment the femoral articular cartilage (FAC) in 3D ultrasound (US) images of the knee to increase time efficiency and decrease rater variability.

Design

Our method involved deep learning predictions on 2DUS slices sampled in the transverse plane to view the cartilage of the femoral trochlea, followed by reconstruction into a 3D surface. A 2D U-Net was modified and trained using a dataset of 200 2DUS images resliced from 20 3DUS images. Segmentation accuracy was evaluated using a holdout dataset of 50 2DUS images resliced from 5 3DUS images. Absolute and signed error metrics were computed and FAC segmentation performance was compared between rater 1 and 2 manual segmentations.

Results

Our U-Net-based algorithm performed with mean 3D DSC, recall, precision, VPD, MSD, and HD of 73.1 ​± ​3.9%, 74.8 ​± ​6.1%, 72.0 ​± ​6.3%, 10.4 ​± ​6.0%, 0.3 ​± ​0.1 ​mm, and 1.6 ​± ​0.7 ​mm, respectively. Compared to the individual 2D predictions, our algorithm demonstrated a decrease in performance after 3D reconstruction, but these differences were not found to be statistically significant. The percent difference between the manually segmented volumes of the 2 raters was 3.4%, and rater 2 demonstrated the largest VPD with 14.2 ​± ​11.4 ​mm3 compared to 10.4 ​± ​6.0 ​mm3 for rater 1.

Conclusion

This study investigated the use of a modified U-Net algorithm to automatically segment the FAC in 3DUS knee images of healthy volunteers, demonstrating that this segmentation method would increase the efficiency of anterior femoral cartilage volume estimation and expedite the post-acquisition processing for 3D US images of the knee.

CT- and MRI-Based 3D Reconstruction of Knee Joint to Assess Cartilage and Bone

For the observation of human joint cartilage, X-ray, computed tomography (CT) or magnetic resonance imaging (MRI) are the main diagnostic tools to evaluate pathologies or traumas. The current work introduces a set of novel measurements and 3D features based on MRI and CT data of the knee joint, used to reconstruct bone and cartilages and to assess cartilage condition from a new perspective. Forty-seven subjects presenting a degenerative disease, a traumatic injury or no symptoms or trauma were recruited in this study and scanned using CT and MRI. Using medical imaging software, the bone and cartilage of the knee joint were segmented and 3D reconstructed. Several features such as cartilage density, volume and surface were extracted. Moreover, an investigation was carried out on the distribution of cartilage thickness and curvature analysis to identify new markers of cartilage condition. All the extracted features were used with advanced statistics tools and machine learning to test the ability of our model to predict cartilage conditions. This work is a first step towards the development of a new gold standard of cartilage assessment based on 3D measurements.

MRi of the knee compared to specialized radiography for measurements of articular cartilage height in knees with osteoarthritis

This study aims to evaluate and compare extremity-MRi with specialized radiography by measuring articular cartilage height in patients with knee osteoarthritis. A prospective study, including sixty patients. Measurements on MRi images, Rosenberg view, and coronal stress radiographs were performed. MRI was compared to specialized radiography. Measurements in the medial compartment showed negligible/weak correlation between MRi and Rosenber/varus stress. In the lateral compartment, MRi and the Rosenberg/valgus stress view were strongly correlated. We conclude that MRi cannot replace radiographs for the measurement of articular cartilage thickness. MRi should, however, be reserved for more unusual cases of atypical clinical findings.