Background: Dynamic medical imaging can determine the cause of rotational impairment in the forearm. However, it has drawbacks depending on the image modality used, related to radiation dose, the need for specialized equipment, and the labor intensity involved in the analysis. Because the forearm rotation axis is static, we hypothesize that an axis based on bony landmarks is comparable to an axis calculated from dynamic imaging. Methods: Eight post-mortem human forearms were scanned using CT in seven rotational positions from maximum supination to maximum pronation. Three rotation axes were calculated: the landmark, average helical, and circle fit axes. The primary outcome is the difference between the axes expressed as the angle and the minimal distance between them. Secondary outcomes are the orientation errors when modeling pose using the three found axes. Findings: The mean difference between the landmark and average helical axes was 0.38 degrees and 0.51 mm. The mean difference between the landmark and circle fit axes was 0.40 degrees and 0.51 mm. When modeling the pose of the radius using one of the three axes, the difference between the modeled radius and the scanned radius was in each direction below 2 mm and 1 degree. Interpretation: The rotation axis of the forearm can be accurately calculated using automatically placed bony landmarks. These findings indicate that determining the forearm rotation axis does not require multiple static images or dynamic imaging. This knowledge should be applied to clinical data to assess its applicability in practice.

Background: For bone morphology and biomechanics analysis, landmarks are essential to define position, orientation, and shape. These landmarks define bone and joint coordinate systems and are widely used in these research fields. Currently, no method is known for automatically identifying landmarks on virtual 3D bone models of the radius and ulna. This paper proposes a knowledge-based method for locating landmarks and calculating a coordinate system for the radius, ulna, and combined forearm bones, which is essential for measuring forearm function. This method does not rely on pre-labeled data. Validation: The algorithm is validated by comparing the landmarks placed by the algorithm with the mean position of landmarks placed by a group of experts on cadaveric specimens regarding distance and orientation. Results: The median Euclidean distance differences between all the automated and reference landmarks range from 0.4 to 1.8 millimeters. The median angular differences of the coordinate system of the radius and ulna range from -1.4 to 0.6 degrees. The forearm coordinate system's median errors range from -0.2 to 2.0 degrees. The median error in calculating the rotational position of the radius relative to the ulna is 1.8 degrees. Conclusion: The automatic method's applicability depends on the use context and desired accuracy. However, the current method is a validated first step in the automatic analysis of the three-dimensional forearm anatomy.

Background: Elbow arthroscopy is a difficult surgical technique. Objective metrics can be used to improve safe and effective training in elbow arthroscopy. Force exerted on the elbow tissue during arthroscopy can be a measure of safe tissue manipulation. The purpose of this study was to determine the force magnitude and force direction used by experts during arthroscopic elbow navigation in cadaveric specimens and assess their applicability in elbow arthroscopy training. Methods: Two cadaveric elbows were mounted on a Force Measurement Table (FMT) that allowed 3-dimensional measurements (x-, y-, and z-plane) of the forces exerted on the elbow. Five experts in elbow arthroscopy performed arthroscopic navigation once in each of two cadaveric elbows, navigating through the posterior, posterolateral and anterior compartment in a standardized fashion with visualization of three to four anatomic landmarks per compartment. The total absolute force (F_abs) and force direction exerted (α and β) on the elbow during arthroscopy were recorded. α being the angle in the horizontal plane and β being the angle in the vertical plane. The 10th–90th percentiles of the data were used to set threshold levels for training. Results: The median F_abs was 24 N (19 N – 30 N), 27 N (20 N – 33 N) and 29 N (23 N – 32 N) for the posterior, posterolateral and anterior compartment, respectively. The median α was - 29° (- 55° – 5°), - 23° (- 56° – -1°) and 4° (- 22° – -18°) for the posterior, posterolateral and anterior compartment, respectively. The median β was - 71° (- 80° – -65°), - 76° (- 86° – -69°) and - 75° (- 81° – -71°) for the posterior, posterolateral and anterior compartment, respectively. Conclusion: Expert data on force magnitude and force direction exerted on the elbow during arthroscopic navigation in cadaveric specimens were collected. The proposed maximum allowable force of 30 N (smallest 90th percentile of F_abs) exerted on the elbow tissue, and the 10th–90th percentile range of the force directions (α and β) for each compartment may be used to provide objective feedback during arthroscopic skills training.