Changes in tibial nerve stiffness during ankle dorsiflexion according to in-vivo analysis with shear wave elastography

Using ultrasound shear wave elastography to characterize peripheral nerve mechanics: a systematic review on the normative reference values in healthy individuals

Ultrasound shear wave elastography (SWE) is an emerging non-invasive imaging technique for peripheral nerve evaluation. Shear wave velocity (SWV), a surrogate measure of stiffness, holds promise as a biomarker for various peripheral nerve disorders. However, to maximize its clinical and biomechanical value, it is important to fully understand the factors that influence nerve SWV measurements. This systematic review aimed to identify the normal range of SWV for healthy sciatic and tibial nerves and to reveal the factors potentially affecting nerve SWV. An electronic search yielded 17 studies eligible for inclusion, involving 548 healthy individuals (age range, 17 to 72 years). Despite very good reliability metrics, the reported SWV values differed considerably across studies for the sciatic (1.9-9.9 m/s) and tibial (2.3-9.1 m/s) nerves. Factors such as measurement proximity to joint regions, limb postures inducing nerve axial stretching, and transducer alignment with nerve fiber orientation were associated with increased SWV. These findings suggest regional-specific nerve mechanical properties, non-linear elastic behaviour, and marked mechanical anisotropy. The impact of age and sex remains unclear and warrants further investigation. These results emphasize the importance of considering these factors when assessing and interpreting nerve SWE. While increased SWV has been linked to pathological changes affecting nerve tissue mechanics, the significant variability observed in healthy nerves highlights the need for standardized SWE assessment protocols. Developing guidelines for enhanced clinical utility and achieving a comprehensive understanding of the factors that influence nerve SWE assessments are critical in advancing the field.

In Vivo Effects of Joint Movement on Nerve Mechanical Properties Assessed with Shear-Wave Elastography: A Systematic Review and Meta-Analysis

Peripheral nerves are subjected to mechanical tension during limb movements and body postures. Nerve response to tensile stress can be assessed in vivo with shear-wave elastography (SWE). Greater tensile loads can lead to greater stiffness, which can be quantified using SWE. Therefore, this study aimed to conduct a systematic review and meta-analysis to perform an overview of the effect of joint movements on nerve mechanical properties in healthy nerves. The initial search (July 2023) yielded 501 records from six databases (PubMed, Embase, Scopus, Web of Science, Cochrane, and Science Direct). A total of 16 studies were included and assessed with a modified version of the Downs and Black checklist. Our results suggest an overall tendency for stiffness increase according to a pattern of neural tensioning. The main findings from the meta-analysis showed a significant increase in nerve stiffness for the median nerve with wrist extension (SMD [95%CI]: 3.16 [1.20, 5.12]), the ulnar nerve with elbow flexion (SMD [95%CI]: 2.91 [1.88, 3.95]), the sciatic nerve with ankle dorsiflexion (SMD [95%CI]: 1.13 [0.79, 1.47]), and the tibial nerve with both hip flexion (SMD [95%CI]: 2.14 [1.76, 2.51]) and ankle dorsiflexion (SMD [95%CI]: 1.52 [1.02, 2.02]). The effect of joint movement on nerve stiffness also depends on the nerve segment, the amount of movement of the joint mobilized, and the position of other joints comprised in the entirety of the nerve length. However, due to the limited number of studies, many aspects of nerve behavior together with the effect of using different ultrasound equipment or transducers for nerve stiffness evaluation still need to be fully investigated.

Automatic Identification of Ultrasound Images of the Tibial Nerve in Different Ankle Positions Using Deep Learning

Peripheral nerve tension is known to be related to the pathophysiology of neuropathy; however, assessing this tension is difficult in a clinical setting. In this study, we aimed to develop a deep learning algorithm for the automatic assessment of tibial nerve tension using B-mode ultrasound imaging. To develop the algorithm, we used 204 ultrasound images of the tibial nerve in three positions: the maximum dorsiflexion position and -10° and -20° plantar flexion from maximum dorsiflexion. The images were taken of 68 healthy volunteers who did not have any abnormalities in the lower limbs at the time of testing. The tibial nerve was manually segmented in all images, and 163 cases were automatically extracted as the training dataset using U-Net. Additionally, convolutional neural network (CNN)-based classification was performed to determine each ankle position. The automatic classification was validated using five-fold cross-validation from the testing data composed of 41 data points. The highest mean accuracy (0.92) was achieved using manual segmentation. The mean accuracy of the full auto-classification of the tibial nerve at each ankle position was more than 0.77 using five-fold cross-validation. Thus, the tension of the tibial nerve can be accurately assessed with different dorsiflexion angles using an ultrasound imaging analysis with U-Net and a CNN.