The aim of this study was to create artificial intelligence (AI) software with the purpose of providing a second opinion to physicians to support distal radius fracture (DRF) detection, and to compare the accuracy of fracture detection of physicians with and without software support. The dataset consisted of 26,121 anonymized anterior-posterior (AP) and lateral standard view radiographs of the wrist, with and without DRF. The convolutional neural network (CNN) model was trained to detect the presence of a DRF by comparing the radiographs containing a fracture to the inconspicuous ones. A total of 11 physicians (six surgeons in training and five hand surgeons) assessed 200 pairs of randomly selected digital radiographs of the wrist (AP and lateral) for the presence of a DRF. The same images were first evaluated without, and then with, the support of the CNN model, and the diagnostic accuracy of the two methods was compared.Aims
Methods
Cadaveric models of the shoulder evaluate discrete motion segments
using the glenohumeral joint in isolation over a defined trajectory.
The aim of this study was to design, manufacture and validate a
robotic system to accurately create three-dimensional movement of
the upper body and capture it using high-speed motion cameras. In particular, we intended to use the robotic system to simulate
the normal throwing motion in an intact cadaver. The robotic system
consists of a lower frame (to move the torso) and an upper frame
(to move an arm) using seven actuators. The actuators accurately
reproduced planned trajectories. The marker setup used for motion
capture was able to determine the six degrees of freedom of all
involved joints during the planned motion of the end effector.Objectives
Methods
