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Automatic Planning Tools for Lumbar Pedicle Screws: Comparison and Validation of Planning Accuracy for Self-Derived Deep-Learning-Based and Commercial Atlas-Based Approaches
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) we...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10094754/ https://www.ncbi.nlm.nih.gov/pubmed/37048730 http://dx.doi.org/10.3390/jcm12072646 |
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author | Scherer, Moritz Kausch, Lisa Bajwa, Akbar Neumann, Jan-Oliver Ishak, Basem Naser, Paul Vollmuth, Philipp Kiening, Karl Maier-Hein, Klaus Unterberg, Andreas |
author_facet | Scherer, Moritz Kausch, Lisa Bajwa, Akbar Neumann, Jan-Oliver Ishak, Basem Naser, Paul Vollmuth, Philipp Kiening, Karl Maier-Hein, Klaus Unterberg, Andreas |
author_sort | Scherer, Moritz |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10094754 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100947542023-04-13 Automatic Planning Tools for Lumbar Pedicle Screws: Comparison and Validation of Planning Accuracy for Self-Derived Deep-Learning-Based and Commercial Atlas-Based Approaches Scherer, Moritz Kausch, Lisa Bajwa, Akbar Neumann, Jan-Oliver Ishak, Basem Naser, Paul Vollmuth, Philipp Kiening, Karl Maier-Hein, Klaus Unterberg, Andreas J Clin Med Article 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. MDPI 2023-04-02 /pmc/articles/PMC10094754/ /pubmed/37048730 http://dx.doi.org/10.3390/jcm12072646 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Scherer, Moritz Kausch, Lisa Bajwa, Akbar Neumann, Jan-Oliver Ishak, Basem Naser, Paul Vollmuth, Philipp Kiening, Karl Maier-Hein, Klaus Unterberg, Andreas Automatic Planning Tools for Lumbar Pedicle Screws: Comparison and Validation of Planning Accuracy for Self-Derived Deep-Learning-Based and Commercial Atlas-Based Approaches |
title | Automatic Planning Tools for Lumbar Pedicle Screws: Comparison and Validation of Planning Accuracy for Self-Derived Deep-Learning-Based and Commercial Atlas-Based Approaches |
title_full | Automatic Planning Tools for Lumbar Pedicle Screws: Comparison and Validation of Planning Accuracy for Self-Derived Deep-Learning-Based and Commercial Atlas-Based Approaches |
title_fullStr | Automatic Planning Tools for Lumbar Pedicle Screws: Comparison and Validation of Planning Accuracy for Self-Derived Deep-Learning-Based and Commercial Atlas-Based Approaches |
title_full_unstemmed | Automatic Planning Tools for Lumbar Pedicle Screws: Comparison and Validation of Planning Accuracy for Self-Derived Deep-Learning-Based and Commercial Atlas-Based Approaches |
title_short | Automatic Planning Tools for Lumbar Pedicle Screws: Comparison and Validation of Planning Accuracy for Self-Derived Deep-Learning-Based and Commercial Atlas-Based Approaches |
title_sort | automatic planning tools for lumbar pedicle screws: comparison and validation of planning accuracy for self-derived deep-learning-based and commercial atlas-based approaches |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10094754/ https://www.ncbi.nlm.nih.gov/pubmed/37048730 http://dx.doi.org/10.3390/jcm12072646 |
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