Kudaibergen Urinbayev
Research Subjects
Kudaibergen Urinbayev has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Fahad Abbas
Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany
1 shared publication - Jannis Löchel
Meoclinic Berlin, MEOSPINE, Friedrichstraße 71, 10117 Berlin, Germany
1 shared publication - Kirsten Labbus
Center for Musculoskeletal Surgery, Charité - University Medicine Berlin, Berlin, Germany
1 shared publication - Marcel Dreischarf
RAYLYTIC - medical data automation, Petersstr. 32-34, 04109, Leipzig, Germany
1 shared publication - Michael Putzier
Center for Musculoskeletal Surgery, Charité – Universitätsmedizin Berlin, Humboldt-Universität zu Berlin
1 shared publication - Priyanka Grover
Raylytic GmbH, Leipzig, Germany
1 shared publication
Latest Publications
- Open Access
- 17 January 2024
- Published by Springer Nature in European Spine Journal
- Vol. 33 (11) , 4119-4124
Deep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity
Aim: Deep learning (DL) algorithms can be used for automated analysis of medical imaging. The aim of this study was to assess the accuracy of an innovative, fully automated DL algorithm for analysis of sagittal balance in adult spinal deformity (ASD). Material and methods: Sagittal balance (sacral slope, pelvic tilt, pelvic incidence, lumbar lordosis and sagittal vertical axis) was evaluated in 141 preoperative and postoperative radiographs of patients with ASD. The DL, landmark-based measurements, were compared with the ground truth values from validated manual measurements. Results: The DL algorithm showed an excellent consistency with the ground truth measurements. The intra-class correlation coefficient between the DL and ground truth measurements was 0.71–0.99 for preoperative and 0.72–0.96 for postoperative measurements. The DL detection rate was 91.5% and 84% for preoperative and postoperative images, respectively. Conclusion: This is the first study evaluating a complete automated DL algorithm for analysis of sagittal balance with high accuracy for all evaluated parameters. The excellent accuracy in the challenging pathology of ASD with long construct instrumentation demonstrates the eligibility and possibility for implementation in clinical routine.
- 17 January 2024
- Published by Springer Nature in European Spine Journal
- Vol. 33 (11) , 4119-4124
Open AccessDeep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity
Aim: Deep learning (DL) algorithms can be used for automated analysis of medical imaging. The aim of this study was to assess the accuracy of an innovative, fully automated DL algorithm for analysis of sagittal balance in adult spinal deformity (ASD). Material and methods: Sagittal balance (sacral slope, pelvic tilt, pelvic incidence, lumbar lordosis and sagittal vertical axis) was evaluated in 141 preoperative and postoperative radiographs of patients with ASD. The DL, landmark-based measurements, were compared with the ground truth values from validated manual measurements. Results: The DL algorithm showed an excellent consistency with the ground truth measurements. The intra-class correlation coefficient between the DL and ground truth measurements was 0.71–0.99 for preoperative and 0.72–0.96 for postoperative measurements. The DL detection rate was 91.5% and 84% for preoperative and postoperative images, respectively. Conclusion: This is the first study evaluating a complete automated DL algorithm for analysis of sagittal balance with high accuracy for all evaluated parameters. The excellent accuracy in the challenging pathology of ASD with long construct instrumentation demonstrates the eligibility and possibility for implementation in clinical routine.