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Status: Bibliographieeintrag

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Verfasst von:Scherer, Moritz [VerfasserIn]   i
 Kausch, Lisa [VerfasserIn]   i
 Bajwa, Awais Akbar [VerfasserIn]   i
 Neumann, Jan-Oliver [VerfasserIn]   i
 Ishak, Basem [VerfasserIn]   i
 Naser, Paul [VerfasserIn]   i
 Vollmuth, Philipp [VerfasserIn]   i
 Kiening, Karl [VerfasserIn]   i
 Maier-Hein, Klaus H. [VerfasserIn]   i
 Unterberg, Andreas [VerfasserIn]   i
Titel:Automatic planning tools for lumbar pedicle screws
Titelzusatz:comparison and validation of planning accuracy for self-derived deep-learning-based and commercial atlas-based approaches
Verf.angabe:Moritz Scherer, Lisa Kausch, Akbar Bajwa, Jan-Oliver Neumann, Basem Ishak, Paul Naser, Philipp Vollmuth, Karl Kiening, Klaus Maier-Hein and Andreas Unterberg
E-Jahr:2023
Jahr: 2 April 2023
Umfang:14 S.
Illustrationen:Illustrationen
Fussnoten:Gesehen am 04.10.2023
Titel Quelle:Enthalten in: Journal of Clinical Medicine
Ort Quelle:Basel : MDPI, 2012
Jahr Quelle:2023
Band/Heft Quelle:12(2023), 7 vom: Apr., Artikel-ID 2646, Seite 1-14
ISSN Quelle:2077-0383
Abstract:Background: This ex vivo experimental study sought to compare screw planning accuracy of a self-derived deep-learning-based (DL) and a commercial atlas-based (ATL) tool and to assess robustness towards pathologic spinal anatomy. Methods: From a consecutive registry, 50 cases (256 screws in L1-L5) were randomly selected for experimental planning. Reference screws were manually planned by two independent raters. Additional planning sets were created using the automatic DL and ATL tools. Using Python, automatic planning was compared to the reference in 3D space by calculating minimal absolute distances (MAD) for screw head and tip points (mm) and angular deviation (degree). Results were evaluated for interrater variability of reference screws. Robustness was evaluated in subgroups stratified for alteration of spinal anatomy. Results: Planning was successful in all 256 screws using DL and in 208/256 (81%) using ATL. MAD to the reference for head and tip points and angular deviation was 3.93 ± 2.08 mm, 3.49 ± 1.80 mm and 4.46 ± 2.86° for DL and 7.77 ± 3.65 mm, 7.81 ± 4.75 mm and 6.70 ± 3.53° for ATL, respectively. Corresponding interrater variance for reference screws was 4.89 ± 2.04 mm, 4.36 ± 2.25 mm and 5.27 ± 3.20°, respectively. Planning accuracy was comparable to the manual reference for DL, while ATL produced significantly inferior results (p < 0.0001). DL was robust to altered spinal anatomy while planning failure was pronounced for ATL in 28/82 screws (34%) in the subgroup with severely altered spinal anatomy and alignment (p < 0.0001). Conclusions: Deep learning appears to be a promising approach to reliable automated screw planning, coping well with anatomic variations of the spine that severely limit the accuracy of ATL systems.
DOI:doi:10.3390/jcm12072646
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.3390/jcm12072646
 Volltext: https://www.mdpi.com/2077-0383/12/7/2646
 DOI: https://doi.org/10.3390/jcm12072646
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:atlas planning
 deep-learning
 machine-learning
 pedicle screw accuracy
 robotic surgery
 spinal instrumentation
 spinal navigation
K10plus-PPN:1860684963
Verknüpfungen:→ Zeitschrift

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