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29-10-2024 | Scoliosis | Original Article

Superior performance of a center-point AI model over VFLDNet in automated cobb angle estimation for scoliosis assessment

Authors: Qingqing Lu, Lixin Ni, Zhehao Zhang, Lulin Zou, Lijun Guo, Yuning Pan

Published in: European Spine Journal

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Abstract

Purpose

Aims to establish the superiority of our proposed model over the state-of-the-art vertebra-focused landmark detection network (VFLDNet) in automating Cobb angle estimation from spinal radiographs.

Methods

Utilizing a private dataset for external validation, we compared the performance of our center-point detection-based vertebra localization and tilt estimation network (VLTENet) with the key-point detection-based VFLDNet. Both models’ Cobb angle predictions were rigorously evaluated against manual consensus score using metrics such as mean absolute error (MAE), correlation coefficient, intraclass correlation coefficient (ICC), Fleiss’ kappa, Bland-Altman analysis, and classification metrics [sensitivity (SN), specificity, accuracy] focusing on major curve estimation and scoliosis severity classification.

Results

A retrospective analysis of 118 cases with 342 Cobb angle measurements revealed that our model achieved a MAE of 2.15° for total Cobb angles and 1.89° for the major curve, significantly outperforming VFLDNet’s MAE of 2.80°and 2.57°, respectively. Both models demonstrated robust correlation and ICC, but our model excelled in classification consistency, particularly in predicting major curve magnitude (ours: kappa = 0.83; VFLDNet: kappa = 0.67). In subgroup analyses by scoliosis severity, our model consistently surpassed VFLDNet, displaying superior mean (SD) differences, narrower limits of agreement, and higher SN, specificity, and accuracy, most notably in moderate (ours: SN = 86.84%; VFLDNet: SN = 83.16%) to severe (ours: SN = 92.86%; VFLDNet: SN = 85.71%) scoliosis.

Conclusion

Our model emerges as the superior choice for automated Cobb angle estimation, particularly in assessing major curve and moderate to severe scoliosis, underscoring its potential to revolutionize clinical workflows and enhance patient care.
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Metadata
Title
Superior performance of a center-point AI model over VFLDNet in automated cobb angle estimation for scoliosis assessment
Authors
Qingqing Lu
Lixin Ni
Zhehao Zhang
Lulin Zou
Lijun Guo
Yuning Pan
Publication date
29-10-2024
Publisher
Springer Berlin Heidelberg
Published in
European Spine Journal
Print ISSN: 0940-6719
Electronic ISSN: 1432-0932
DOI
https://doi.org/10.1007/s00586-024-08538-6