Treatment Study - Level IV.
Pectus carinatum (PC) presents itself as a protrusion on the chest wall of adolescent individuals. Current treatment for PC is performed with a Pectus carinatum orthosis (PCO) that applies a compressive force to the protrusion. While this treatment is accepted, the magnitude of compressive forces applied remains unknown leading to excessive or deficient compression. Although the need for this quantitative data is recognized, no studies reporting the data or methods are available. The purpose of this study was to design an accurate force measurement system (FMS) that could be incorporated into a PCO with minimal bulk. Components of the FMS were three-dimensional (3D)-printed and incorporated into an existing PCO design. The FMS was calibrated using a custom indenter that applied forces to the FMS in a controlled manner. Evaluation of the FMS on five human participants was also performed. A reliability measure of the FMS was calculated for analysis. The FMS was implemented into the PCO and able to withstand the applied forces. The calibration revealed an increase in load cell error with increased magnitude of applied force (mean error [SD] = 5.59 N [6.48 N]). Participants recruited to evaluate the FMS demonstrated reliable forces (R = 96%) with smaller standard deviations than those during the calibration. The FMS was shown capable of measuring PCO forces but requires further testing and improvement. This system is the foundational component in a wireless, minimalistic sensor system to provide real time force feedback to both the clinician and patient.
Background The trapeziometacarpal (TMC) joint is a mechanically complex joint and is commonly affected by musculoskeletal diseases such as osteoarthritis. Quantifying in vivo TMC joint biomechanics, such as joint angles, with traditional reflective marker-based methods can be difficult due to the joint’s location in the hand. Dynamic computed tomography (CT) can facilitate the quantification of TMC joint motion by continuously capturing three-dimensional volumes over time. However, post-processing of dynamic CT datasets can be time intensive and automated methods are needed to reduce processing times to allow for application to larger clinical studies. The purpose of this work is to introduce a fast, semi-automated pipeline to quantify joint angles from dynamic CT scans of the TMC joint and evaluate the associated error in joint angle and translation computation by means of a reproducibility and repeatability study. Methods Ten cadaveric hands were scanned with dynamic CT using a passive motion device to move thumbs in a radial abduction–adduction motion. Static CT scans and high-resolution peripheral quantitative CT scans were also acquired to generate high-resolution bone meshes. Abduction–adduction, flexion–extension, and axial rotation angles were computed using a joint coordinate system. Reproducibility and repeatability were assessed using intraclass correlation coefficients, Bland–Altman analysis, and root mean square errors. Target registration errors were computed to evaluate errors associated with image registration. Results We found good repeatability for flexion–extension, abduction–adduction, and axial rotation angles. Reproducibility was moderate for all three angles. Joint translations exhibited greater repeatability than reproducibility. Specimens with greater joint degeneration had lower repeatability and reproducibility. We found that the difference in resulting joint angles and translations were likely due to differences in segment coordinate system definition between multiple raters, rather than due to registration errors. Conclusions The proposed semi-automatic processing pipeline was fast, repeatable, and moderately reproducible when quantifying TMC joint angles and translations. This work provides a range of errors for TMC joint angles from dynamic CT scans using manually selected anatomical landmarks.
Background The trapeziometacarpal (TMC) joint is a mechanically complex joint and is commonly affected by musculoskeletal diseases such as osteoarthritis. Quantifying in vivo TMC joint biomechanics, such as joint angles, with traditional reflective marker-based methods can be difficult due to the joint’s location in the hand. Dynamic computed tomography (CT) can facilitate the quantification of TMC joint angles by continuously capturing three-dimensional volumes over time. However, post-processing of dynamic CT datasets can be time intensive and automated methods are needed to reduce processing times to allow for application to larger clinical studies. The purpose of this work is to introduce a fast, semi-automated pipeline to quantify joint angles from dynamic CT scans of the TMC joint and evaluate the associated error in joint angle computation by means of a reproducibility and repeatability study. Methods Ten cadaveric hands were scanned with dynamic CT using a passive motion device to move thumbs in a radial abduction-adduction motion. Static CT scans and high-resolution peripheral quantitative CT scans were also acquired to generate high-resolution bone meshes. Abduction-adduction, flexion-extension, and axial rotation angles were computed using a joint coordinate system. Reproducibility and repeatability were assessed using intraclass correlation coefficients, Bland-Altman analysis, and root mean square errors. Target registration errors were computed to evaluate errors associated with image registration. Results Repeatability was found to be moderate to excellent for flexion-extension and abduction-adduction, and poor to excellent for axial rotation. Reproducibility was poor to excellent for all three angles. Specimens with greater joint degeneration had lower repeatability and reproducibility. We found that the difference in resulting joint angles were likely due to differences in segment coordinate system definition between multiple raters, rather than due to registration errors. Conclusions The proposed semi-automatic processing pipeline was fast, repeatable, and moderately reproducible when quantifying TMC joint angles. This work provides a range of errors for TMC joint angles from dynamic CT scans using manually selected anatomical landmarks.
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