A deep learning tool for fully automated measurements of sagittal spinopelvic balance from X-ray images: performance eva
- PDF / 4,571,153 Bytes
- 11 Pages / 595.276 x 790.866 pts Page_size
- 61 Downloads / 183 Views
ORIGINAL ARTICLE
A deep learning tool for fully automated measurements of sagittal spinopelvic balance from X‑ray images: performance evaluation Robert Korez1 · Michael Putzier2 · Tomaž Vrtovec1 Received: 31 October 2019 / Revised: 10 March 2020 / Accepted: 30 March 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords Sagittal spinopelvic balance · Pelvic incidence · X-ray images · Computer-assisted tools · Deep learning
Introduction Radiological examination of the spine and pelvis is indispensable in the process of surgical and non-surgical treatment of different spinal disorders. The concept of sagittal spinopelvic balance has a key role in the etiopathogenesis of spinal deformities [1, 2] and has become widespread among radiologists and spine professionals, as it is necessary to consider the principles of sagittal imbalance and recognize its compensatory mechanisms for choosing an adequate treatment [3]. Sagittal balance is commonly evaluated by radiographic * Tomaž Vrtovec [email protected]‑lj.si 1
Laboratory of Imaging Technologies, Faculty of Electrical Engineering, University of Ljubljana, Tržaška Cesta 25, 1000 Ljubljana, Slovenia
Charité University Hospital, Charitéplatz 1, 10117 Berlin, Germany
2
measurements of geometrical relationships among specific ana
Data Loading...
