Decreased Compressional Sound Velocity Is an Indicator for Compromised Bone Stiffness in X-Linked Hypophosphatemic Rickets (XLH)

Objectives: To assess the diagnostic potential of bidirectional axial transmission (BDAT) ultrasound, and high-resolution peripheral quantitative computed tomography (HR-pQCT) in X-linked hypophosphatemia (XLH, OMIM #307800), a rare genetic disorder of phosphate metabolism caused by mutations in the PHEX gene. Methods: BDAT bone ultrasound was performed at the non-dominant distal radius (33% relative to distal head) and the central left tibia (50%) in eight XLH patients aged between 4.2 and 20.8 years and compared to twenty-nine healthy controls aged between 5.8 and 22.4 years. In eighteen controls, only radius measurements were performed. Four patients and four controls opted to participate in HR-pQCT scanning of the ultradistal radius and tibia. Results: Bone ultrasound was feasible in patients and controls as young as 4 years of age. The velocity of the first arriving signal (νFAS) in BDAT ultrasound was significantly lower in XLH patients compared to healthy controls: In the radius, mean νFAS of XLH patients and controls was 3599 ± 106 and 3866 ± 142 m/s, respectively (-6.9%; p < 0.001). In the tibia, it was 3578 ± 129 and 3762 ± 124 m/s, respectively (-4.9%; p = 0.006). HR-pQCT showed a higher trabecular thickness in the tibia of XLH patients (+16.7%; p = 0.021). Conclusions: Quantitative bone ultrasound revealed significant differences in cortical bone quality of young XLH patients as compared to controls. Regular monitoring of XLH patients by a radiation-free technology such as BDAT might provide valuable information on bone quality and contribute to the optimization of treatment. Further studies are needed to establish this affordable and time efficient method in the XLH patients

3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects.

BackgroundSemiquantitative assessment of MRI plays a central role in musculoskeletal research; however, in the clinical setting MRI reports often tend to be subjective and qualitative. Grading schemes utilized in research are not used because they are extraordinarily time-consuming and unfeasible in clinical practice.PurposeTo evaluate the ability of deep-learning models to detect and stage severity of meniscus and patellofemoral cartilage lesions in osteoarthritis and anterior cruciate ligament (ACL) subjects.Study typeRetrospective study aimed to evaluate a technical development.PopulationIn all, 1478 MRI studies, including subjects at various stages of osteoarthritis and after ACL injury and reconstruction.Field strength/sequence3T MRI, 3D FSE CUBE.AssessmentAutomatic segmentation of cartilage and meniscus using 2D U-Net, automatic detection, and severity staging of meniscus and cartilage lesion with a 3D convolutional neural network (3D-CNN).Statistical testsReceiver operating characteristic (ROC) curve, specificity and sensitivity, and class accuracy.ResultsSensitivity of 89.81% and specificity of 81.98% for meniscus lesion detection and sensitivity of 80.0% and specificity of 80.27% for cartilage were achieved. The best performances for staging lesion severity were obtained by including demographics factors, achieving accuracies of 80.74%, 78.02%, and 75.00% for normal, small, and complex large lesions, respectively.Data conclusionIn this study we provide a proof of concept of a fully automated deep-learning pipeline that can identify the presence of meniscal and patellar cartilage lesions. This pipeline has also shown potential in making more in-depth examinations of lesion subjects for multiclass prediction and severity staging.Level of evidence2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:400-410