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Bone & Joint Research
Vol. 13, Issue 10 | Pages 588 - 595
17 Oct 2024
Breu R Avelar C Bertalan Z Grillari J Redl H Ljuhar R Quadlbauer S Hausner T

Aims. 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. Methods. 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. Results. At the time of the study, the CNN model showed an area under the receiver operating curve of 0.97. AI assistance improved the physician’s sensitivity (correct fracture detection) from 80% to 87%, and the specificity (correct fracture exclusion) from 91% to 95%. The overall error rate (combined false positive and false negative) was reduced from 14% without AI to 9% with AI. Conclusion. The use of a CNN model as a second opinion can improve the diagnostic accuracy of DRF detection in the study setting. Cite this article: Bone Joint Res 2024;13(10):588–595


Bone & Joint Research
Vol. 1, Issue 5 | Pages 78 - 85
1 May 2012
Entezari V Della Croce U DeAngelis JP Ramappa AJ Nazarian A Trechsel BL Dow WA Stanton SK Rosso C Müller A McKenzie B Vartanians V Cereatti A

Objectives

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.

Methods

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.