Mathematical models predict the physiological dimensions of selected canine carpal joint structures across imaging modalities in healthy dogs

Abstract

Abstract Advanced imaging modalities, such as ultrasonography (USG) and magnetic resonance imaging (MRI), have markedly enhanced the diagnosis of canine musculoskeletal anatomy and disorders. The principal hypothesis tested is that body weight (BW) and thoracic limb measurements significantly influence the dimensions of CCJ of structures. The objectives are to determine the optimal conditions for visualizing the CCJ using USG and MRI and to develop predictive models based on BW and thoracic limb measurements. The study included 33 dogs, excluding chondrodystrophic breeds, with a focus on those having body condition scores of 4 and 5. The study utilized radiographic imaging, USG, MRI and mathematical modeling to explore the influence of BW and thoracic limb measurements on the dimensions of CCJ structures. Linear regression modeling was employed to forecast the dimensions of specific CCJ structures, with primary outcomes including the correlation and predictive accuracy of these measurements across all imaging modalities. Optimal visibility for both USG and MRI were achieved in dogs weighing 10 kg or more. Strong positive correlations were observed between imaging measurements and both BW and thoracic limb measurements (r > 0.9, P < 0.01), with significant correlations particularly noted in MRI measurements. BW was identified as a critical factor affecting imaging quality. Predictive modeling based on BW and limb measurements can accurately estimate the dimensions of selected CCJ structures. These models have the potential to improve diagnostic accuracy for CCJ disorders, suggesting enhancements in imaging protocols and diagnostic strategies, thereby contributing to better canine health and welfare.