Wrist Joint

ObjectivesThe purpose of this study was to compare findings of ultrasound (US) with dual-energy CT (DECT) in patients presenting with suspected gouty hand and wrist arthritis.MethodsThis prospective study included 180 patients (136 men and 44 women, age range, 31- 94 years; mean age, 65.9 years) with an initial clinical diagnosis of acute gouty arthritis who underwent DECT and US examination. Intra- and extra-articular findings of each modality were tabulated and calculated with DECT as gold standard.ResultsThe final diagnosis of gout was positive in 97/180 patients (53.9%) by DECT, an alternative diagnosis confirmed in 83 patients. US showed a sensitivity of 70.1% (extra-articular: 42.5%, p ConclusionsSensitivity of US for diagnosis of gouty arthritis in hand and wrist is limited, particularly with respect to extra-articular urate deposition. The DCS is the most sensitive sign for the assessment of gouty hand and wrist arthritis by US.Key points• Sensitivity of US for diagnosis of gouty arthritis in hand and wrist is limited, particularly with respect to extra-articular gouty deposits. • The double contour sign is the most sensitive finding for the assessment of gouty hand and wrist arthritis by US. • Although the sensitivity of US for diagnosis of gouty hand and wrist arthritis is limited, it can be used as a first-line imaging modality in the presence of the DCS.

BackgroundAlthough the automatic diagnosis of fractures using artificial intelligence (AI) has recently been reported to be more accurate than those by orthopedics specialists, big data with at least 1000 images or more are required for deep learning of the convolutional neural network (CNN) to improve diagnostic accuracy. The aim of this study was to develop an AI system capable of diagnosing distal radius fractures with high accuracy even when learning with relatively small data by learning to use bi-planar X-rays images.MethodsVGG16, a learned image recognition model, was used as the CNN. It was modified into a network with two output layers to identify the fractures in plain X-ray images. We augmented 369 plain X-ray anteroposterior images and 360 lateral images of distal radius fractures, as well as 129 anteroposterior images and 125 lateral images of normal wrists to conduct training and diagnostic tests. Similarly, diagnostic tests for fractures of the styloid process of the ulna were conducted using 189 plain X-ray anteroposterior images of fractures and 302 images of the normal styloid process. The distal radius fracture is determined by entering an anteroposterior image of the wrist for testing into the trained AI. If it identifies a fracture, it is diagnosed as the same. However, if the anteroposterior image is determined as normal, the lateral image of the same patient is entered. If a fracture is identified, the final diagnosis is fracture; if the lateral image is identified as normal, the final diagnosis is normal.ResultsThe diagnostic accuracy of distal radius fractures and fractures of the styloid process of the ulna were 98.0 ± 1.6% and 91.1 ± 2.5%, respectively. The areas under the receiver operating characteristic curve were 0.991 {n = 540; 95% confidence interval (CI), 0.984-0.999} and 0.956 (n = 450; 95% CI 0.938-0.973).ConclusionsOur method resulted in a good diagnostic rate, even when using a relatively small amount of data.

A wide range of different classifications exist for distal radius fractures (DRF). Most of them are based on plane X-rays and do not give us any information on how to treat these fractures. A biomechanical understanding of the mechanical forces underlying each fracture type is important to treat each injury specifically and ensure the optimal choice for stabilization. The main cause of DRFs are forces acting on the carpus and the radius as well as the position of the wrist in relation to the radius. Reconstructing the mechanism of the injury gives insight into which structures are involved, such as ruptured ligaments, bone fragments as well as the dislocated osteoligamentous units. This article attempts to define certain key fragments, which seem crucial to reduce and stabilize each type of DRF. Once the definition is established, an ideal implant can be selected to sufficiently maintain reduction of these key fragments. Additionally, the perfect approach is selected. By applying the following principles, the surgeon may be assisted in choosing the ideal form of treatment approach and implant selection.

The injury of the scapholunate (SL) ligament is common in wrist traumas leading to pain and reduced wrist function. The wrist's unique joint design and possible underlying theories as the carpal row theory were subject to earlier investigations studying wrist kinematics. Nevertheless, a comprehensive understanding of how SL ligament injuries affect wrist biomechanics is still lacking. Through a quantitative analysis of carpal bone motion patterns, we evaluated the impact on wrist kinematics occurring after SL ligament injury. We conducted a study using computer tomography imaging to analyse wrist kinematics after SL ligament transection in 21 fresh-frozen anatomical specimens. The collected data were then transformed into 3D models, employing both standardized global and object coordinate systems. The study encompassed the evaluation of rotation and translation for each individual carpal bone, as well as the ulna, and all metacarpal bones in reference to the radius. The study showed a significant increase in rotation towards palmar (p < 0.01), particularly notable for the scaphoid, following transection of the SL ligament during palmar flexion. Ulnar deviation did not significantly affect rotation or translation, and radial deviation also showed no significant changes in rotation or translation. The study highlights the significance of the SL ligament in wrist kinematics, revealing that SL ligament tears lead to changes in wrist motion. While we observed significant rotational changes for the scaphoid, other carpal bones showed less pronounced alterations, emphasizing the complexity of wrist biomechanics.