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Abstract The femoral neck axis serves as a critical parameter in evaluating hip joint health, particularly in the pediatric population. Commonly used metrics for evaluating femoral torsion, such as the femoral neck-shaft and femoral anteversion angles, rely heavily on precise definitions of the position and orientation of the femoral neck axis. Current measurement methods employing radiographs and performing two-dimensional (2D) measurements on computed tomography (CT) scans are susceptible to errors due to their reliance on reader experience and the inherent limitations in 2D measurements. We hypothesized that utilizing volumetric data would mitigate these errors and enable more accurate and reproducible measurements of the femoral neck axis using the femoral anteversion and femoral neck-shaft angles. To test this hypothesis, we analyzed a historical collection of postmortem infant femoral and pelvic bones (28 hips) aged 0 to 6.5 months, with an average estimated age of 4.68 ± 1.80 months. Our findings revealed an average neck-shaft angle of 128.00 ± 4.92 deg and femoral anteversion angle of 35.56 ± 11.68 deg across all femurs, consistent with literature values. These measurements obtained from volumetric image data were found to be repeatable and reliable compared to conventional methods. Our study suggests that the proposed methodology offers a standardized approach for obtaining repeatable and reproducible measurements, thus potentially enhancing diagnostic accuracy and clinical decision-making in assessing hip developmental conditions in pediatric patients. 

Abstract Developmental dysplasia of the hip (DDH) is a condition in which the acetabular socket inadequately contains the femoral head (FH). If left untreated, DDH can result in degenerative changes in the hip joint. Several imaging techniques are used for DDH assessment. In radiographs, the acetabular index (ACIN), center-edge angle, Sharp's angle (SA), and migration percentage (MP) metrics are used to assess DDH. Determining these metrics is time-consuming and repetitive. This study uses a convolutional neural network (CNN) to identify radiographic measurements and improve traditional methods of identifying DDH. The dataset consisted of 60 subject radiographs rotated along the craniocaudal and mediolateral axes 25 times, generating 1500 images. A CNN detection algorithm was used to identify key radiographic metrics for the diagnosis of DDH. The algorithm was able to detect the metrics with reasonable accuracy in comparison to the manually computed metrics. The CNN performed well on images with high contrast margins between bone and soft tissues. In comparison, the CNN was not able to identify some critical points for metric calculation on a few images that had poor definition due to low contrast between bone and soft tissues. This study shows that CNNs can efficiently measure clinical parameters to assess DDH on radiographs with high contrast margins between bone and soft tissues with purposeful rotation away from an ideal image. Results from this study could help inform and broaden the existing bank of information on using CNNs for radiographic measurement and medical condition prediction. 

Inertial kinetics and kinematics have substantial influences on human biomechanical function. A new algorithm for Inertial Measurement Unit (IMU)-based motion tracking is presented in this work. The primary aims of this paper are to combine recent developments in improved biosensor technology with mainstream motion-tracking hardware to measure the overall performance of human movement based on joint axis-angle representations of limb rotation. This work describes an alternative approach to representing three-dimensional rotations using a normalized vector around which an identified joint angle defines the overall rotation, rather than a traditional Euler angle approach. Furthermore, IMUs allow for the direct measurement of joint angular velocities, offering the opportunity to increase the accuracy of instantaneous axis of rotation estimations. Although the axis-angle representation requires vector quotient algebra (quaternions) to define rotation, this approach may be preferred for many graphics, vision, and virtual reality software applications. The analytical method was validated with laboratory data gathered from an infant dummy leg’s flexion and extension knee movements and applied to a living subject’s upper limb movement. The results showed that the novel approach could reasonably handle a simple case and provide a detailed analysis of axis-angle migration. The described algorithm could play a notable role in the biomechanical analysis of human joints and offers a harbinger of IMU-based biosensors that may detect pathological patterns of joint disease and injury.