Aims

The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular fracture involvement, on plain radiographs.

Technological advancements in orthopaedic surgery have mainly focused on increasing precision during the operation however, there have been few developments in post-operative physiotherapy. We have developed a computer vision program using machine learning that can virtually measure the range of movement of a joint to track progress after surgery. This data can be used by physiotherapists to change patients’ exercise regimes with more objectively and help patients visualise the progress that they have made. In this study, we tested our program's reliability and validity to find a benchmark for future use on patients. We compared 150 shoulder joint angles, measured using a goniometer, and those calculated by our program called ArmTracking in a group of 10 participants (5 males and 5 females). Reliability was tested using adjusted R squared and validity was tested using 95% limits of agreement. Our clinically acceptable limit of agreement was ± 10° for ArmTracking to be used interchangeably with goniometry. ArmTracking showed excellent overall reliability of 97.1% when all shoulder movements were combined but there were lower scores for some movements like shoulder extension at 75.8%. There was moderate validity shown when all shoulder movements were combined at 9.6° overestimation and 18.3° underestimation. Computer vision programs have a great potential to be used in telerehabilitation to collect useful information as patients carry out prescribed exercises at home. However, they need to be trained well for precise joint detections to reduce the range of errors in readings