Since the outbreak of the ongoing pandemic of the novel coronavirus disease (COVID-19) in Wuhan, China, from December 2019, we have learned that multiple organs can be affected with the potential for various complications. Although myalgia is a frequent symptom in COVID-19 patients, no imaging findings of rhabdomyolysis have been featured in the literature. We report a case of presumed rhabdomyolysis in a 38-year-old male with COVID-19 based on the clinical presentation, laboratory results and radiological findings. By discussing the diagnostic rationale and reviewing the relevant literature we hope to advance the existing understanding of this disease and its effects on the musculoskeletal system.

Background: This study is aimed to determine the efficacy of X-Ray Microtomography (micro-CT) in predicting oxytocin (OT) treatment response in rabbit osteoporosis(OP) model. Methods: Sixty-five rabbits were randomly divided into three groups: control group, ovariectomy (OVX) -vehicle and OVX-oxytocin group. The controls underwent sham surgery. OVX-vehicle and OVX-oxytocin groups were subjected to bilateral OVX. The rabbits in OVX-oxytocin group were injected with oxytocin. In the 0th, 4th, 8th, 10th and 12th weeks post OVX operation, bone mineral density (BMD) and bone micro-architectural parameters were measured in three groups. Results: Bone mineral density (BMD), bone volume fraction (BV/TV), Trabecular Number (Tb.N), and Trabecular Thickness (Tb.Th) decreased, while Trabecular Spacing (Tb.Sp) and Structure Model Index (SMI) increased overtime in all the three groups. In OVX-oxytocin group, the bone deterioration tendency is slowing down compared with that of the OVX-vehicle group. The BMD of the OVX-oxytocin group was significantly lower than those in the OVX-vehicle group at 12th week (P = 0.017). BV/TV and Tb.Sp in OVX-oxytocin group changed significantly from 8th week (P = 0.043) and 12th week (P = 0.014), which is earlier than that of BMD and other bone micro-architectural parameters. Conclusion: BV/TV and Tb.Sp changed prior to BMD and other bone micro-architectural parameters with oxytocin intervention, which indicate that they are more sensitive markers for predicting early osteoporosis and treatment monitoring when using micro-CT to evaluate osteoporosis rabbit model.

Background: Patellofemoral anatomy has not been well characterized. Applying deep learning to automatically measure knee anatomy can provide a better understanding of anatomy, which can be a key factor in improving outcomes. Methods: 483 total patients with knee CT imaging (April 2017–May 2022) from 6 centers were selected from a cohort scheduled for knee arthroplasty and a cohort with healthy knee anatomy. A total of 7 patellofemoral landmarks were annotated on 14,652 images and approved by a senior musculoskeletal radiologist. A two-stage deep learning model was trained to predict landmark coordinates using a modified ResNet50 architecture initialized with self-supervised learning pretrained weights on RadImageNet. Landmark predictions were evaluated with mean absolute error, and derived patellofemoral measurements were analyzed with Bland–Altman plots. Statistical significance of measurements was assessed by paired t-tests. Results: Mean absolute error between predicted and ground truth landmark coordinates was 0.20/0.26 cm in the healthy/arthroplasty cohort. Four knee parameters were calculated, including transepicondylar axis length, transepicondylar-posterior femur axis angle, trochlear medial asymmetry, and sulcus angle. There were no statistically significant parameter differences (p > 0.05) between predicted and ground truth measurements in both cohorts, except for the healthy cohort sulcus angle. Conclusion: Our model accurately identifies key trochlear landmarks with ~0.20–0.26 cm accuracy and produces human-comparable measurements on both healthy and pathological knees. This work represents the first deep learning regression model for automated patellofemoral annotation trained on both physiologic and pathologic CT imaging at this scale. This novel model can enhance our ability to analyze the anatomy of the patellofemoral compartment at scale.