## 1 Introduction  
Crystal tunnel syndrome (CTS) is the most common entrapment neuropathy [1], occurring in 3.8% of the general population [2]. The condition manifests within the confines of the carpal tunnel, a narrow space through which the median nerve (MN) and nine flexor tendons pass. These tendons consist of the flexor pollicis longus (FPL), four flexor digitorum superficialis (FDS), and the four flexor digitorum profundus (FDP). Located within the carpal tunnel, these important anatomical structures are enveloped by a multilayered structure known as the subsynosuonic connective tissue (SSCT) [3, 4]. While this SSCT plays an essential role in cushioning and protecting these delicate nerve and tendon structures, it can become a source of concern in CTS patients. The histologic examination of the SSCT in patients with CTS reveals a recurrent pattern of non-inflammatory fibrosis of the SSCT in the carpal tunnel as a result of repetitive shear stress on the SSCT during finger and wrist movements [5, 6, 7, 8]. Over time, this constant mechanical stress leads to the thickening of the SSCT, which impairs the smooth sliding of the MN during tendon loading. As the SSCT swells, it exerts increased pressure within the already narrowed carpal tunnel, subsequently contributing to neuropathic symptoms. Traditionally, the gold standard for diagnosing CTS has relied on electrophysiologic testing, specifically including nerve conduction studies (NCS) and electromyography (EMG). However, in recent decades, ultrasonography (US) has emerged as a diagnostic tool for CTS. This shift in the diagnostic methodology can be attributed to the inherent advantages of US imaging, including its cost-effectiveness and widespread accessibility [9, 10]. [PERSON] et al. highlighted key ultrasonographic features in CTS patients, including an increased MN cross-sectional area, increased swelling ratio, and the prominent bowing of the palmer flexor retinaculum [11]. While static US imaging has provided valuable insights, the quest to understand CTS and its effects on MN dynamics has led to the exploration of a dynamic US evaluation in CTS patients. Several studies have reported the dynamic ultrasound evaluation of the MN in CTS patients and suggest that the mobility of MN is reduced in CTS [12; 13; 14]. Some investigators have addressed the dynamic assessment of the MN, with particular emphasis on longitudinal MN sliding [15; 16; 17; 18; 19] and transverse MN displacement [13; 15; 17; 20; 21; 22; 23; 24]. Various methods, such as cross-correlation or speckle tracking algorithms, have been used to study MN dynamics. However, the results of these dynamic MN analyses show some variability, which adds to the complexity of CTS diagnosis and understanding. Longitudinal MN sliding, for example, has yielded inconsistent results across studies. While the results of two studies of longitudinal MN sliding found no significant difference in MN sliding between the CTS group and the control group [15; 16], the other three studies reported that CTS patients had less MN sliding than the control group [17; 18; 19]. Similarly, the investigation of transverse MN displacement yielded mixed results. Two studies found no statistically significant differences between patients with CTS and the control group [17; 20], whereas the other two studies found that MN motion was indeed significantly reduced in the dorsal direction and in the radial direction in CTS patients [21; 23]. And another study reported that only the radioulnar axis showed significant differences [25]. In addition, it is worth noting that many of these dynamic analyses were performed by comparing static images or manually tracking the MN, which required considerable time and effort and suffered from poor reproducibility. In recent years, the field of computer science has witnessed remarkable advances, most notably the development of deep learning (DL) technologies using convolutional neural networks (CNN).