Reliability analysis of automated Cobb angle measurement using artificial intelligence models in scoliosis patients

The measurement of the Cobb angle is essential for diagnosing scoliosis and monitoring its pro-gression, especially in adolescent idiopathic scoliosis.This study evaluates the reliability and efficiency of DEEP:SPINE-AS-01 developed by DEEPNOID, a Korean Ministry of Food and Drug Safety certified AI software, in automated Cobb angle measurement compared to manual methods.Fifty-two radiographs were initially collected, and 48 met inclusion criteria for analysis.Two observers, one an experienced spine orthopedic specialist and the other a non-specialist, performed manual measurements, while the AI module provided automated assessments.Intra-and inter-observer reliability were analyzed using intraclass correlation coefficients.The AI demonstrated excellent agreement with manual measurements, with ICCs above 0.98 for inter-observer comparisons.The non-specialist observer required more time for manual measurements (mean 53 minutes) than the specialist (mean 36 minutes), highlighting the manual method's dependence on experi-ence.DEEP:SPINE-AS-01 reduced measurement variability and time, offering results in de-grees with high reproducibility.Despite minor endplate selection differences, the AI achieved robust agreement with human observers and standardized the measurement process.This study underscores DEEP:SPINE-AS-01 potential to enhance clinical workflow by auto-mating a traditionally time-intensive and error-prone process.Its accuracy, efficiency, and re-producibility suggest its value in routine scoliosis evaluation, reducing the burden on physicians and improving patient care.Future studies with larger datasets and diverse populations could further validate its clinical utility.