• Peter Bernstein, Johannes Metzler, Marlene Weinzierl, Carl Seifert, Wadim Kisel, and Markus Wacker.
• Department for Orthopaedics and Traumatology, University Comprehensive Spine Center, University Hospital Dresden, Fetscherstrasse 74, 01307, Dresden, Germany. bernspe@gmail.com.
• Eur Spine J. 2021 Mar 1; 30 (3): 676-685.

Introduction And ObjectiveAlthough being standard for scoliosis curve size estimation, COBB angle measurement is well known to be inaccurate, due to a high interobserver variance in end vertebra selection and end plate contour delineation. We propose a stepwise improvement by using a spline constructed from vertebra centroids to resemble spinal curve characteristics more closely. To enhance precision even further, a neural net was trained to detect the centroids automatically.Materials & MethodsVertebra centroids in AP spinal X-ray images of varying quality from 551 scoliosis patients were manually labeled by 4 investigators. With these inputs, splines were generated and the computed curve sizes were compared to the manually measured COBB angles and to the curve estimation obtained from the neural net.ResultsSplines achieved a higher interobserver correlation of 0.92-0.95 compared to manual COBB measurements (0.83-0.92) and showed 1.5-2 times less variance, depending on the anatomic region. This translates into an average of 1° of interobserver measurement deviation for spline-based curve estimation compared to 3°-8° for COBB measurements. The neural net was even more precise and achieved mean deviations below 0.5°.ConclusionIn conclusion, our data suggest an advantage of spline-based automated measuring systems, so further investigations are warranted to abandon manual COBB measurements.

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