Asymmetric Post-Traumatic Knee Arthritis Is Closely Correlated With Both Severity and Time for Lower Limb Coronal Plane Malalignment.

OBJECTIVE: Mechanical alignment of the lower limbs has been suggested to cause abnormal uneven loading across the compartments at the knee, but its contribution to the initiation and progression of arthritis remains controversial. This study aimed to establish whether malalignment of the lower limb after trauma is associated with worsened arthritis scores in the theoretically overloaded compartment, and if arthritis scores continuously correlate with the degree of malalignment and time with deformity. DESIGN: After screening 1160 X-rays, 60 patients were identified with long-leg radiographs > 2 years after fracture. Measurement of mechanical axis deviation (MAD) divided into groups of varus malalignment (n = 16, >16 mm), valgus (n = 25, <0 mm), and normal alignment (n = 19). Alignment and bilateral knee compartmental arthritis scores were recorded by three clinicians, compared via analysis of variance and assessed with linear regression against time since injury using MAD as a covariate. RESULTS: In varus and valgus malalignment, there was a greater mean arthritis score in the "overloaded" compartment compared to the contralateral side, with varus medial Osteoarthritis Research Society International (OARSI) scores 5.17 ± 2.91 vs 3.50 ± 2.72 (P = 0.006) and Kellegren-Lawrence scores 2.65 ± 1.19 vs 1.79 ± 1.24 (P ≤ 0.001). In a linear regression model, OARSI arthritis score was significantly associated with absolute MAD (0.6/10 mm MAD, P < 0.001) and time (0.7/decade, P ≤ 0.001). CONCLUSIONS: Malalignment consistently results in more advanced arthritis scores in the overloaded compartment, most likely related to abnormal loading across the knee. Severity of arthritis using OARSI grading continuously correlates with degree of malalignment and time with deformity after post-traumatic malunion.

Automatic classification of distal radius fracture using a two-stage ensemble deep learning framework.

Distal radius fractures (DRFs) are one of the most common types of wrist fracture and can be subdivided into intra- and extra-articular fractures. Compared with extra-articular DRFs which spare the joint surface, intra-articular DRFs extend to the articular surface and can be more difficult to treat. Identification of articular involvement can provide valuable information about the characteristics of fracture patterns. In this study, a two-stage ensemble deep learning framework was proposed to differentiate intra- and extra-articular DRFs automatically on posteroanterior (PA) view wrist X-rays. The framework firstly detects the distal radius region of interest (ROI) using an ensemble model of YOLOv5 networks, which imitates the clinicians' search pattern of zooming in on relevant regions to assess abnormalities. Secondly, an ensemble model of EfficientNet-B3 networks classifies the fractures in the detected ROIs into intra- and extra-articular. The framework achieved an area under the receiver operating characteristic curve of 0.82, an accuracy of 0.81, a true positive rate of 0.83 and a false positive rate of 0.27 (specificity of 0.73) for differentiating intra- from extra-articular DRFs. This study has demonstrated the potential in automatic DRF characterization using deep learning on clinically acquired wrist radiographs and can serve as a baseline for further research in incorporating multi-view information for fracture classification.

The use of 4D-CT in assessing wrist kinematics and pathology: a narrative view.

The wrist is a complex joint involving many small bones and complicated kinematics. It has, therefore, been traditionally difficult to image and ascertain information about kinematics when making a diagnosis. Although MRI and fluoroscopy have been used, they both have limitations. Recently, there has been interest in the use of 4D-CT in imaging the wrist. This review examines the literature regarding the use of 4D-CT in imaging the wrist to assess kinematics and its ability to diagnose pathology. Some questions remain about the description of normal ranges, the most appropriate method of measuring intercarpal stability, the accuracy compared with established standards, and the place of 4D-CT in postoperative assessment. Cite this article: Bone Joint J 2019;101-B:1325-1330.