Abstract
Exploratory-descriptive study. Evaluate the influence of observer variability on the measurements of both thoracic kyphosis (TK) and lumbar lordosis (LL) obtained with anatomic and functional spinal segmentation methods. Parametric analysis for spinal surgery planning typically relies on anatomic parameters. However, incorporating functional parameters that consider the vertebrae orientation is important to minimizing surgical calculation errors. The authors developed parametric analysis software that integrates traditional and functional methodologies. The proposed method included functional thoracic kyphosis and functional lumbar lordosis calculated from the lines normal to the inflection points of the spine model. Using a synthetic lateral X-ray, the observer variability was computer-simulated generating 20 landmark sets that replicate the annotations of 20 observers. The analysis also included 10 clinical X-rays, annotated twice by 3 judges with a minimum 1-week interval. The spinal curvature angles were derived using the anatomic and functional methods. Statistical analysis were performed for comparison. For the synthetic X-ray, the proposed method presented significantly less variability: TK (<±2.5 degrees, P=0.00023) and LL (<±5 degrees, P=0.00012). For the clinical X-rays, the interobserver reliability analysis yielded higher intraclass correlation coefficients (ICC) for functional TK (ICC>0.97) and functional LL (ICC>0.87) than for TK (ICC<0.91) and LL (ICC<0.89). Statistically significant differences were observed for both TK (P=0.001) and LL (P=0.030). Under the traditional method, observer variability led to measurement differences surpassing ±19 degrees, whereas differences with the proposed method were within ±10 degrees for both parameters. The vertebral endplate is not the most suitable place to measure spinal sagittal curvatures. Small changes in landmark position significantly alter the measured Cobb angle. The proposed method offers a substantial advantage regarding the influence of observer variability, in addition to the more individualized analysis.
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