Objective: The present quantitative study aimed to assess the three-dimensional (3-D) cartilage wear patterns of the first metacarpal and trapezium in the advanced stage of osteoarthritis (OA) and compare cartilage measurements with radiographic severity. Design: Using 19 cadaveric trapeziometacarpal (TMC) joints, 3-D cartilage surface models of the first metacarpal and trapezium were created with a laser scanner, and 3-D bone surface model counterparts were similarly created after dissolving the cartilage. These two models were superimposed, and the interval distance on the articular surface as the cartilage thickness was measured. All measurements were obtained in categorized anatomic regions on the articular surface of the respective bone, and we analyzed the 3-D wear patterns on the entire cartilage surface. Furthermore, we compared measurements of cartilage thickness with radiographic OA severity according to the Eaton grading system using Pearson correlation coefficients (r). Results: In the first metacarpal, the cartilage thickness declined volarly (the mean cartilage thickness of the volar region was 0.32 ± 0.16 mm, whereas that of the dorsal region was 0.53 ± 0.18 mm). Conversely, the cartilage evenly degenerated throughout the articular surface of the trapezium. Measurements of the categorized regions where cartilage thinning was remarkable exhibited statistical correlations with radiographic staging (r ¼ À0.48 to À0.72). Conclusions: Our findings indicate that cartilage wear patterns differ between the first metacarpal and trapezium in the late stage of OA. There is a need for further studies on cartilage degeneration leading to symptomatic OA in the TMC joint.
Background: In missed Monteggia fracture (MMF) cases, ulnar angulation and lengthening by osteotomy are required to reduce the dislocated radial head. This study aimed to clarify the abnormal discrepancy in length between the radius and ulna in MMF. We tested the hypothesis that the increase in the abnormal discrepancy in length between the radius and ulna relates with the duration of radial head dislocation. Methods: In total, 24 patients with MMF were studied and classified into 2 groups, according to the duration of radial head dislocation, including the early group (n=9, within 3 y) and the long-standing group (n=15, older than 3 y). The lengths of the radius (Lr) and ulna (Lu) were measured. The difference in length between the ulna and radius (DL=Lu−Lr) was calculated on both the affected (DLaff) and normal (DLnor) sides. DLnor−DLaff, which represented an abnormal discrepancy in both bones, was analyzed for correlation with the duration of radial head dislocation and the age at initial injury. Results: The affected and normal sides had no differences in the Lr of both the groups and in the Lu of the early group. However, in the long-standing group, Lu was significantly smaller in the affected side than in the normal side (P=0.001). In the long-standing group, DLaff was significantly smaller, owing to decreased length of the ulna, than DLnor (P=0.003). The DLnor−DLaff was positively correlated with the duration of radial head dislocation and was negatively correlated with the age at injury. Conclusions: In chronic MMF cases, the length of the ulna was shorter in the affected side than in the normal side. Therefore, ulnar lengthening is necessary to resolve this abnormal discrepancy and reduce the radial head. Because excessive ulnar lengthening has risks of postoperative complications, one of the surgical options is gradual ulnar lengthening or shortening osteotomy of the radius. Level of Evidence: Level III—Prognosis study.
The purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.
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