Résumé
Purpose Clinical evaluation of the shoulder range of motion (RoM) may vary significantly depending on the surgeon. We aim to validate an automatic shoulder RoM measurement system associating image acquisition by an RGB-D (red/green/blue-depth) video camera to an artificial intelligence (AI) algorithm. Methods Thirty healthy volunteers were included. A 3D RGB-D sensor that simultaneously generated a colour image and a depth map was used. Then, an open-access convolutional neural network algorithm that was programmed for shoulder recognition provided a 3D motion measure. Each volunteer adopted a randomized position successively. For each position, two observers made a visual (EyeREF) and goniometric measurement (GonioREF), blind to the automated software which was implemented by an orthopaedic surgeon. We evaluated the inter-tester intra-class correlation (ICC) between observers and the concordance correlation coefficient (CCC) between the three methods. Results For manual evaluations EyeREF and GonioREF, ICC remained constantly excellent for the widest motions in the vertical plane (i.e., abduction and flexion). It was very good for ER1 and IR2 and fairly good for adduction, extension, and ER2. Differences between the measurements' means of EyeREF and shoulder RoM was significant for all motions. Compared to GonioREF, shoulder RoM provided similar results for abduction, adduction, and flexion and EyeREF provided similar results for adduction, ER1, and ER2. The three methods showed an overall good to excellent CCC. The mean bias between the three methods remained under 10 & DEG; and clinically acceptable. Conclusion RGB-D/AI combination is reliable in measuring shoulder RoM in consultation, compared to classic goniometry and visual observation.