Precision of AI-Based Lower Limb Assessment in Children and Adolescents with Open Growth Plates

Background/Objectives: X-rays are the most reliable and widely available imaging modality for diagnosing deformities of the lower extremities in paediatric orthopaedic patients. This raises the question of whether AI-generated analysis of key parameters is comparable to manual measurement. Methods: This retrospective, single-center observational study included patients with radiographically confirmed open growth plates at a Level One university hospital in Germany between 2005 and 2022. Using the quality criteria (full-leg weight-bearing radiograph with the patella centered, showing both legs with the upper ankle joint and pelvis, in a bipedal, hip-width stance), 325 X-rays were evaluated for leg length measurement and 269 X-rays for leg angle measurement. For each parameter, measurements were taken manually by a specialist and compared with the results from the AI software BoneMetrics developed by Gleamer (Paris, France). The age range of the patients at the time of examination was for length measurements between 3.4 and 17.8 years; 59.4% were male (mean 12.5 ± 3.0 years) and 40.6% were female (mean 10.8 ± 2.8 years). For angle measurements, the study included a cohort ranging in age from 10.1 to 17.7 years; 58% were male (mean age 13.6 ± 1.7 years) and 42% were female (mean age 12.5 ± 1.4). Results: For leg length measurements, the mean values of the AI-based measurements for the femur were 1.4% (5.6 mm) lower on both sides than those of the manual measurement; however, they were higher on both sides for the tibia (6.5%, 21.7 mm) and total leg-length (5.3%, 40.4 mm). The correlation of leg length measurements showed no differences between the right and left sides. For all parameters (femur, tibia, and total length), the measurement results from the two methods correlated excellently, with an intraclass correlation coefficient (ICC) > 0.75. The combined effect of excellent ICC values, coupled with systematic deviations in tibial- and total leg length suggests that high reliability does not necessarily mean interchangeability of the two methods. For leg angle measurements, the percentage differences ranged from 0 to a maximum of 1.3%. The joint line convergence angle (JLCA) was an exception on both sides, with very high percentage differences of 27.2% on the right and 50.5% on the left. The mechanical axial deviation (MAD) showed significantly higher values on both sides for the AI measurement compared to the manual measurement (p < 0.001). The percentage difference was 9.2% on the right and 5.4% on the left. The ICC showed excellent values of >0.75 for 7 out of 10 angles and good values of 0.71, but poor values of 0.08 and 0.146 for the left and right JLCA, respectively. Conclusions: Manual measurement remains vital for the diagnosis of axial deformities in children and adolescents. Given some promising results, it is expected that, through continuous training, AI will eventually be able to match the radiological values determined manually.