Exploring the Potential of AI-Assisted Technology in Joint Range-of-Motion Measurements: A Reliability Study - Gisoo Lee; Eric W. Tan (Medicina)
Background and Objectives: Measuring joint range of motion (ROM) is essential for diagnosing and treating musculoskeletal diseases. However, most clinical measurements are conducted using conventional devices, and their reliability may significantly depend on the tester. This study implemented an RGB-D (red/green/blue-depth) sensor-based artificial intelligence (AI) device to measure joint ROM and compared its reliability with that of a universal goniometer (UG). Materials and Methods: A single-center study was conducted from January 2022 to December 2022 on participants visiting the Chung-nam National University Hospital to compare the reliability of the RGB-D sensor-based AI device with that of the UG for measuring ROM. The ROM of the shoulder, hip, and lumbar spine joints was measured in 35 healthy participants in our hospital. The ROM was measured during active motion by the participants in the standing position. The ROM was measured twice consecutively using the RGB-D sensor-based AI device, and the mean values were obtained along with other values. A clinician also measured the ROM twice using a UG. Bland–Altman analysis was performed to evaluate the reliability of the measurements, which was assessed using intra-class correlation coefficient (ICC). An ICC value greater than 0.90 indicates excellent reliability. Results: Both methods achieved good-to-excellent intra-test reliability results (ICC > 0.75) for all the joints, with the reliability being slightly higher for the RGB-D sensor-based AI method than for the UG measurements. Moreover, for both methods, the inter-test reliability was higher than good (ICC > 0.75) for shoulder and lumbar joint ROM measurements but lower than good (ICC < 0.75) for hip ROM measurements. Conclusions: This study compared the efficacies of the RGB-D sensor-based AI method and UG in measuring ROM. In the future, this RGB-D sensor-based AI method should be technologically improved, and the measurement methods and protocols should be standardized.