Development and Validation of Deep Learning Preoperative Planning Software for Automatic Lumbosacral Screw Selection Using Computed Tomography
Achieving precise pedicle screw placement in posterior lumbar interbody fusion (PLIF) is essential but difficult due to the intricacies of manual preoperative planning with CT scans. We analyzed CT data from 316 PLIF patients, using Mimics software for manual planning by two surgeons. A deep learnin...
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2024-10-01
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| author | Baodong Wang Congying Zou Xingyu Liu Dong Liu Yiling Zhang Lei Zang |
| author_facet | Baodong Wang Congying Zou Xingyu Liu Dong Liu Yiling Zhang Lei Zang |
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| description | Achieving precise pedicle screw placement in posterior lumbar interbody fusion (PLIF) is essential but difficult due to the intricacies of manual preoperative planning with CT scans. We analyzed CT data from 316 PLIF patients, using Mimics software for manual planning by two surgeons. A deep learning model was trained on 228 patients and validated on 88 patients, assessing planning efficiency and accuracy. Automatic planning successfully segmented and placed screws in all 316 cases, significantly outperforming manual planning in speed. The Dice coefficient for segmentation accuracy was 0.95. The difference in mean pedicle transverse angle (PTA) and pedicle sagittal angle (PSA) for automatic planning screws compared to manual planning screws was 1.63 ± 0.83° and 1.39 ± 1.03°, respectively, and these differences were either statistically comparable or not significantly different compared to the variability of manual planning screws. The average Dice coefficient of implanted screws was 0.63 ± 0.08, and the consistency between automatic screws and manual reference screws was higher than that of internal screws (Dice 0.62 ± 0.09). Compared with manual screws, automatic screws were shorter (46.58 ± 3.09 mm) and thinner (6.24 ± 0.35 mm), and the difference was statistically significant. In qualitative validation, 97.7% of the automatic planning screws were rated Gertzbein–Robbins (GR) Class A and 97.3% of the automatic planning screws were rated Badu Class 0. Deep learning software automates lumbosacral pedicle screw planning, enhancing surgical efficiency and accuracy. |
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| spelling | doaj-art-9f7a394c77f94c0385da8e82f1bef8bd2025-08-20T02:08:12ZengMDPI AGBioengineering2306-53542024-10-011111109410.3390/bioengineering11111094Development and Validation of Deep Learning Preoperative Planning Software for Automatic Lumbosacral Screw Selection Using Computed TomographyBaodong Wang0Congying Zou1Xingyu Liu2Dong Liu3Yiling Zhang4Lei Zang5Department of Orthopedics, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100043, ChinaDepartment of Orthopedics, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100043, ChinaSchool of Life Sciences, Tsinghua University, Beijing 100084, ChinaLongwood Valley Medical Technology Co., Ltd., Beijing 101111, ChinaSchool of Biomedical Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Orthopedics, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100043, ChinaAchieving precise pedicle screw placement in posterior lumbar interbody fusion (PLIF) is essential but difficult due to the intricacies of manual preoperative planning with CT scans. We analyzed CT data from 316 PLIF patients, using Mimics software for manual planning by two surgeons. A deep learning model was trained on 228 patients and validated on 88 patients, assessing planning efficiency and accuracy. Automatic planning successfully segmented and placed screws in all 316 cases, significantly outperforming manual planning in speed. The Dice coefficient for segmentation accuracy was 0.95. The difference in mean pedicle transverse angle (PTA) and pedicle sagittal angle (PSA) for automatic planning screws compared to manual planning screws was 1.63 ± 0.83° and 1.39 ± 1.03°, respectively, and these differences were either statistically comparable or not significantly different compared to the variability of manual planning screws. The average Dice coefficient of implanted screws was 0.63 ± 0.08, and the consistency between automatic screws and manual reference screws was higher than that of internal screws (Dice 0.62 ± 0.09). Compared with manual screws, automatic screws were shorter (46.58 ± 3.09 mm) and thinner (6.24 ± 0.35 mm), and the difference was statistically significant. In qualitative validation, 97.7% of the automatic planning screws were rated Gertzbein–Robbins (GR) Class A and 97.3% of the automatic planning screws were rated Badu Class 0. Deep learning software automates lumbosacral pedicle screw planning, enhancing surgical efficiency and accuracy.https://www.mdpi.com/2306-5354/11/11/1094posterior lumbar interbody fusiondeep learningcomputed tomographypedicle screwsGertzbein–Robbins classificationBadu grading |
| spellingShingle | Baodong Wang Congying Zou Xingyu Liu Dong Liu Yiling Zhang Lei Zang Development and Validation of Deep Learning Preoperative Planning Software for Automatic Lumbosacral Screw Selection Using Computed Tomography Bioengineering posterior lumbar interbody fusion deep learning computed tomography pedicle screws Gertzbein–Robbins classification Badu grading |
| title | Development and Validation of Deep Learning Preoperative Planning Software for Automatic Lumbosacral Screw Selection Using Computed Tomography |
| title_full | Development and Validation of Deep Learning Preoperative Planning Software for Automatic Lumbosacral Screw Selection Using Computed Tomography |
| title_fullStr | Development and Validation of Deep Learning Preoperative Planning Software for Automatic Lumbosacral Screw Selection Using Computed Tomography |
| title_full_unstemmed | Development and Validation of Deep Learning Preoperative Planning Software for Automatic Lumbosacral Screw Selection Using Computed Tomography |
| title_short | Development and Validation of Deep Learning Preoperative Planning Software for Automatic Lumbosacral Screw Selection Using Computed Tomography |
| title_sort | development and validation of deep learning preoperative planning software for automatic lumbosacral screw selection using computed tomography |
| topic | posterior lumbar interbody fusion deep learning computed tomography pedicle screws Gertzbein–Robbins classification Badu grading |
| url | https://www.mdpi.com/2306-5354/11/11/1094 |
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