Evaluation of AI Performance in Spinal Radiographic Measurements Compared to Radiologists: A Study of Accuracy and Efficiency

This study aimed to evaluate the reliability of an AI-based software tool in measuring spinal parameters-Cobb angle, thoracic kyphosis, lumbar lordosis, and pelvic obliquity-compared to manual measurements by radiologists and to assess potential time savings. In this retrospective monocentric study, 56 patients who underwent full-spine weight-bearing X-rays were analyzed. Measurements were independently performed by an experienced radiologist, a radiology resident, and the AI software. A consensus between two senior experts established the ground truth. Lin's Concordance Correlation Coefficient (CCC), mean absolute error (MAE), ICC, and paired <i>t</i>-tests were used for statistical analysis. The AI software showed excellent agreement with human readers (CCC > 0.9) and demonstrated lower MAE than the resident in Cobb angle and lumbar lordosis measurements but slightly underperformed in thoracic kyphosis and pelvic obliquity. Importantly, the AI significantly reduced analysis time compared to both the experienced radiologist and the resident (<i>p</i> < 0.001). These findings suggest that the AI tool offers a reliable and time-efficient alternative to manual spinal measurements and may enhance accuracy for less experienced radiologists.