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A deep learning tool for fully automated measurements of sagittal spinopelvic balance from X-ray images: performance evaluation

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Abstract

Purpose

The purpose of this study is to evaluate the performance of a novel deep learning (DL) tool for fully automated measurements of the sagittal spinopelvic balance from X-ray images of the spine in comparison with manual measurements.

Methods

Ninety-seven conventional upright sagittal X-ray images from 55 subjects were retrospectively included in this study. Measurements of the parameters of the sagittal spinopelvic balance, i.e., the sacral slope (SS), pelvic tilt (PT), spinal tilt (ST), pelvic incidence (PI) and spinosacral angle (SSA), were obtained manually by identifying specific anatomical landmarks using the SurgiMap Spine software and by the fully automated DL tool. Statistical analysis was performed in terms of the mean absolute difference (MAD), standard deviation (SD) and Pearson correlation, while the paired t test was used to search for statistically significant differences between manual and automated measurements.

Results

The differences between reference manual measurements and those obtained automatically by the DL tool were, respectively, for SS, PT, ST, PI and SSA, equal to 5.0° (3.4°), 2.7° (2.5°), 1.2° (1.2°), 5.5° (4.2°) and 5.0° (3.5°) in terms of MAD (SD), with a statistically significant corresponding Pearson correlation of 0.73, 0.90, 0.95, 0.81 and 0.71. No statistically significant differences were observed between the two types of measurement (p value always above 0.05).

Conclusion

The differences between measurements are in the range of the observer variability of manual measurements, indicating that the DL tool can provide clinically equivalent measurements in terms of accuracy but superior measurements in terms of cost-effectiveness, reliability and reproducibility.

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Acknowledgements

This work has been supported by Raylytic GmbH, Leipzig, Germany, partly by the Slovenian Research Agency under Grants P2-0232 and J2-7118 and partly by the German Research Foundation (DFG) under project number PU 510/2-1. The funding sources did not influence this investigation or affect the outcomes and results.

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Correspondence to Tomaž Vrtovec.

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Korez, R., Putzier, M. & Vrtovec, T. A deep learning tool for fully automated measurements of sagittal spinopelvic balance from X-ray images: performance evaluation. Eur Spine J 29, 2295–2305 (2020). https://doi.org/10.1007/s00586-020-06406-7

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