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Chui CS(E, He Z, Lam TP, Mak KK(K, Ng HT(R, Fung CH(E, Chan MS, Law SW, Lee YW(W, Hung LH(A, Chu CW(W, Mak SY(S, Yau WF(E, Liu Z, Li WJ, Zhu Z, Wong MY(R, Cheng CY(J, Qiu Y, Yung SH(P. Deep Learning-Based Prediction Model for the Cobb Angle in Adolescent Idiopathic Scoliosis Patients. Diagnostics (Basel) 2024; 14:1263. [PMID: 38928678 PMCID: PMC11203267 DOI: 10.3390/diagnostics14121263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/02/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024] Open
Abstract
Scoliosis, characterized by spine deformity, is most common in adolescent idiopathic scoliosis (AIS). Manual Cobb angle measurement limitations underscore the need for automated tools. This study employed a vertebral landmark extraction method and Feedforward Neural Network (FNN) to predict scoliosis progression in 79 AIS patients. The novel intervertebral angles matrix format showcased results. The mean absolute error for the intervertebral angle progression was 1.5 degrees, while the Pearson correlation of the predicted Cobb angles was 0.86. The accuracy in classifying Cobb angles (<15°, 15-25°, 25-35°, 35-45°, >45°) was 0.85, with 0.65 sensitivity and 0.91 specificity. The FNN demonstrated superior accuracy, sensitivity, and specificity, aiding in tailored treatments for potential scoliosis progression. Addressing FNNs' over-fitting issue through strategies like "dropout" or regularization could further enhance their performance. This study presents a promising step towards automated scoliosis diagnosis and prognosis.
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Affiliation(s)
- Chun-Sing (Elvis) Chui
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China (T.-P.L.); (M.-S.C.); (S.-W.L.)
| | - Zhong He
- Division of Spine Surgery, Department of Orthopedic Surgery, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210000, China; (Z.H.); (Z.L.); (Z.Z.)
| | - Tsz-Ping Lam
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China (T.-P.L.); (M.-S.C.); (S.-W.L.)
| | - Ka-Kwan (Kyle) Mak
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China (T.-P.L.); (M.-S.C.); (S.-W.L.)
| | - Hin-Ting (Randy) Ng
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China (T.-P.L.); (M.-S.C.); (S.-W.L.)
| | - Chun-Hai (Ericsson) Fung
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China (T.-P.L.); (M.-S.C.); (S.-W.L.)
| | - Mei-Shuen Chan
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China (T.-P.L.); (M.-S.C.); (S.-W.L.)
| | - Sheung-Wai Law
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China (T.-P.L.); (M.-S.C.); (S.-W.L.)
| | - Yuk-Wai (Wayne) Lee
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China (T.-P.L.); (M.-S.C.); (S.-W.L.)
| | - Lik-Hang (Alec) Hung
- Department of Orthopaedics and Traumatology, Prince of Wales Hospital, Hong Kong, China;
| | - Chiu-Wing (Winnie) Chu
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China;
| | - Sze-Yi (Sibyl) Mak
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China;
| | | | - Zhen Liu
- Division of Spine Surgery, Department of Orthopedic Surgery, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210000, China; (Z.H.); (Z.L.); (Z.Z.)
| | - Wu-Jun Li
- National Institute of Healthcare Data Science, Nanjing University, Nanjing 210023, China;
- National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China
| | - Zezhang Zhu
- Division of Spine Surgery, Department of Orthopedic Surgery, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210000, China; (Z.H.); (Z.L.); (Z.Z.)
| | - Man Yeung (Ronald) Wong
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China (T.-P.L.); (M.-S.C.); (S.-W.L.)
| | - Chun-Yiu (Jack) Cheng
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China (T.-P.L.); (M.-S.C.); (S.-W.L.)
| | - Yong Qiu
- Division of Spine Surgery, Department of Orthopedic Surgery, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210000, China; (Z.H.); (Z.L.); (Z.Z.)
| | - Shu-Hang (Patrick) Yung
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China (T.-P.L.); (M.-S.C.); (S.-W.L.)
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Zhang B, Chen K, Yuan H, Liao Z, Zhou T, Guo W, Zhao S, Wang R, Su P. Automatic Lenke classification of adolescent idiopathic scoliosis with deep learning. JOR Spine 2024; 7:e1327. [PMID: 38690524 PMCID: PMC11058480 DOI: 10.1002/jsp2.1327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 02/26/2024] [Accepted: 03/10/2024] [Indexed: 05/02/2024] Open
Abstract
Purpose The Lenke classification system is widely utilized as the preoperative evaluation protocol for adolescent idiopathic scoliosis (AIS). However, manual measurement is susceptible to observer-induced variability, which consequently impacts the evaluation of progression. The goal of this investigation was to develop an automated Lenke classification system utilizing innovative deep learning algorithms. Methods Using the database from the First Affiliated Hospital of Sun Yat-sen University, the whole spinal x-rays images were retrospectively collected. Specifically, images collection was divided into AIS and control group. The control group consisted of individuals who underwent routine health checks and did not have scoliosis. Afterwards, relative features of all images were annotated. Deep learning was implemented through the utilization of the key-point based detection method to realize the vertebral detection, and Cobb angle measurement and scoliosis classification were performed based on relevant standards. Besides, the segmentation method was employed to achieve the recognition of lumbar vertebral pedicle to determine the type of lumbar spine modifier. Finally, the model performance was further quantitatively analyzed. Results In the study, a total of 2082 spinal x-ray images were collected from 407 AIS patients and 227 individuals in the control group. The model for vertebral detection achieved an F1-score of 0.809 for curve type evaluation and an F1-score of 0.901 for thoracic sagittal profile. The intraclass correlation efficient (ICC) of the Cobb angle measurement was 0.925. In the analysis of performance for vertebra pedicle segmentation model, the F1-score of lumbar modification profile was 0.942, the intersection over union (IOU) of the target pixels was 0.827, and the Hausdorff distance (HD) was 6.565 ± 2.583 mm. Specifically, the F1-score for ultimate Lenke type classifier was 0.885. Conclusions This study has constructed an automated Lenke classification system by employing the deep learning networks to achieve the recognition pattern and feature extraction. Our models require further validation in additional cases in the future.
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Affiliation(s)
- Baolin Zhang
- Department of Orthopaedic SurgeryFirst Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouGuangdongChina
- Guangdong Provincial Key Laboratory of Orthopedics and TraumatologyFirst Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouGuangdongChina
| | - Kanghao Chen
- School of Computer Science and EngineeringSun Yat‐sen UniversityGuangzhouGuangdongChina
| | - Haodong Yuan
- Department of Orthopaedic SurgeryFirst Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouGuangdongChina
- Guangdong Provincial Key Laboratory of Orthopedics and TraumatologyFirst Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouGuangdongChina
- Graduate School of Biomedical EngineeringUNSW SydneySydneyNew South WalesAustralia
| | - Zhiheng Liao
- Department of Orthopaedic SurgeryFirst Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouGuangdongChina
- Guangdong Provincial Key Laboratory of Orthopedics and TraumatologyFirst Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouGuangdongChina
| | - Taifeng Zhou
- Department of Orthopaedic SurgeryFirst Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouGuangdongChina
- Guangdong Provincial Key Laboratory of Orthopedics and TraumatologyFirst Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouGuangdongChina
| | - Weiming Guo
- Department of Orthopaedic SurgeryFirst Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouGuangdongChina
- Guangdong Provincial Key Laboratory of Orthopedics and TraumatologyFirst Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouGuangdongChina
| | - Shen Zhao
- School of Intelligent Systems EngineeringSun Yat‐sen UniversityGuangzhouGuangdongChina
| | - Ruixuan Wang
- School of Computer Science and EngineeringSun Yat‐sen UniversityGuangzhouGuangdongChina
| | - Peiqiang Su
- Department of Orthopaedic SurgeryFirst Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouGuangdongChina
- Guangdong Provincial Key Laboratory of Orthopedics and TraumatologyFirst Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouGuangdongChina
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Han S, Zhao H, Zhang Y, Yang C, Han X, Wu H, Cao L, Yu B, Wen JX, Wu T, Gao B, Wu W. Application of machine learning standardized integral area algorithm in measuring the scoliosis. Sci Rep 2023; 13:19255. [PMID: 37935731 PMCID: PMC10630500 DOI: 10.1038/s41598-023-44252-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 10/05/2023] [Indexed: 11/09/2023] Open
Abstract
This study was to develop a computer vision evaluation method to automatically measure the degree of scoliosis based on the machine learning algorithm. For the X-ray images of 204 patients with idiopathic scoliosis who underwent full-spine radiography, histogram equalization of original image was performed before a flipping method was used to magnify asymmetric elements, search for the global maximum pixel value in each line, and scan local maximal pixel value, with the intersection set of two point sets being regarded as candidate anchor points. All fine anchors were fitted with cubic spline algorithm to obtain the approximate curve of the spine, and the degree of scoliosis was measured by the standardized integral area. All measured data were analyzed. In manual measurement, the Cobb angle was 11.70-25.00 (20.15 ± 3.60), 25.20-44.70 (33.89 ± 5.41), and 45.10-49.40 (46.98 ± 1.25) in the mild, moderate and severe scoliosis group, respectively, whereas the value for the standardized integral area algorithm was 0.072-0.298 (0.185 ± 0.040), 0.100-0.399 (0.245 ± 0.050), and 0.246-0.901 (0.349 ± 0.181) in the mild, moderate and severe scoliosis group, respectively. Correlation analysis between the manual measurement of the Cobb angle and the evaluation of the standardized integral area algorithm demonstrated the Spearman correlation coefficient r = 0.643 (P < 0.001). There was a positive correlation between the manual measurement of the Cobb angle and the measurement of the standardized integral area value. Two methods had good consistency in evaluating the degree of scoliosis. ROC curve analysis of the standardized integral area algorithm to measure the degree of scoliosis showed he cutoff value of the standardized integral area algorithm was 0.20 for the moderate scoliosis with an AUC of 0.865, sensitivity 0.907, specificity 0.635, accuracy 0.779, positive prediction value 0.737 and negative prediction value 0.859, and the cutoff value of the standardized integral area algorithm was 0.40 for the severe scoliosis with an AUC of 0.873, sensitivity 0.188, specificity 1.00, accuracy 0.936, positive prediction value 1 and a negative prediction value 0.935. Using the standardized integral area as an independent variable and the Cobb angle as a dependent variable, a linear regression equation was established as Cobb angle = 13.36 + 70.54 × Standardized area, the model has statistical significance. In conclusion, the integrated area algorithm method of machine learning can quickly and efficiently assess the degree of scoliosis and is suitable for screening the degree of scoliosis in a large dataset as a useful supplement to the fine measurement of scoliosis Cobb angle.
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Affiliation(s)
- Shuman Han
- Department of Radiology, The Third Affiliated Hospital of Hebei Medical University, Shijiazhuang 139 Ziqiang Road, Shijiazhuang, 050051, Hebei, China
| | - Hongyu Zhao
- Department of Radiology, The Third Affiliated Hospital of Hebei Medical University, Shijiazhuang 139 Ziqiang Road, Shijiazhuang, 050051, Hebei, China
| | - Yi Zhang
- Hebei University of Science and Technology, Shijiazhuang, 050051, Hebei, China.
| | - Chen Yang
- Department of Radiology, The Third Affiliated Hospital of Hebei Medical University, Shijiazhuang 139 Ziqiang Road, Shijiazhuang, 050051, Hebei, China
| | - Xiaonan Han
- Department of Radiology, The Third Affiliated Hospital of Hebei Medical University, Shijiazhuang 139 Ziqiang Road, Shijiazhuang, 050051, Hebei, China
| | - Huizhao Wu
- Department of Radiology, The Third Affiliated Hospital of Hebei Medical University, Shijiazhuang 139 Ziqiang Road, Shijiazhuang, 050051, Hebei, China
| | - Lei Cao
- Department of Radiology, The Third Affiliated Hospital of Hebei Medical University, Shijiazhuang 139 Ziqiang Road, Shijiazhuang, 050051, Hebei, China
| | - Baohai Yu
- Department of Radiology, The Third Affiliated Hospital of Hebei Medical University, Shijiazhuang 139 Ziqiang Road, Shijiazhuang, 050051, Hebei, China
| | - Jin-Xu Wen
- Department of Radiology, The Third Affiliated Hospital of Hebei Medical University, Shijiazhuang 139 Ziqiang Road, Shijiazhuang, 050051, Hebei, China
| | - Tianhao Wu
- Department of Radiology, The Third Affiliated Hospital of Hebei Medical University, Shijiazhuang 139 Ziqiang Road, Shijiazhuang, 050051, Hebei, China
| | - Bulang Gao
- Department of Radiology, The Third Affiliated Hospital of Hebei Medical University, Shijiazhuang 139 Ziqiang Road, Shijiazhuang, 050051, Hebei, China
| | - Wenjuan Wu
- Department of Radiology, The Third Affiliated Hospital of Hebei Medical University, Shijiazhuang 139 Ziqiang Road, Shijiazhuang, 050051, Hebei, China.
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Dong J, Fuentes A, Yoon S, Kim H, Park DS. An iterative noisy annotation correction model for robust plant disease detection. FRONTIERS IN PLANT SCIENCE 2023; 14:1238722. [PMID: 37941667 PMCID: PMC10628849 DOI: 10.3389/fpls.2023.1238722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 09/22/2023] [Indexed: 11/10/2023]
Abstract
Previous work on plant disease detection demonstrated that object detectors generally suffer from degraded training data, and annotations with noise may cause the training task to fail. Well-annotated datasets are therefore crucial to build a robust detector. However, a good label set generally requires much expert knowledge and meticulous work, which is expensive and time-consuming. This paper aims to learn robust feature representations with inaccurate bounding boxes, thereby reducing the model requirements for annotation quality. Specifically, we analyze the distribution of noisy annotations in the real world. A teacher-student learning paradigm is proposed to correct inaccurate bounding boxes. The teacher model is used to rectify the degraded bounding boxes, and the student model extracts more robust feature representations from the corrected bounding boxes. Furthermore, the method can be easily generalized to semi-supervised learning paradigms and auto-labeling techniques. Experimental results show that applying our method to the Faster-RCNN detector achieves a 26% performance improvement on the noisy dataset. Besides, our method achieves approximately 75% of the performance of a fully supervised object detector when 1% of the labels are available. Overall, this work provides a robust solution to real-world location noise. It alleviates the challenges posed by noisy data to precision agriculture, optimizes data labeling technology, and encourages practitioners to further investigate plant disease detection and intelligent agriculture at a lower cost. The code will be released at https://github.com/JiuqingDong/TS_OAMIL-for-Plant-disease-detection.
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Affiliation(s)
- Jiuqing Dong
- Department of Electronic Engineering, Jeonbuk National University, Jeonju, Republic of Korea
- Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, Republic of Korea
| | - Alvaro Fuentes
- Department of Electronic Engineering, Jeonbuk National University, Jeonju, Republic of Korea
- Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, Republic of Korea
| | - Sook Yoon
- Department of Computer Engineering, Mokpo National University, Muan, Republic of Korea
| | - Hyongsuk Kim
- Department of Electronic Engineering, Jeonbuk National University, Jeonju, Republic of Korea
- Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, Republic of Korea
| | - Dong Sun Park
- Department of Electronic Engineering, Jeonbuk National University, Jeonju, Republic of Korea
- Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, Republic of Korea
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Suri A, Tang S, Kargilis D, Taratuta E, Kneeland BJ, Choi G, Agarwal A, Anabaraonye N, Xu W, Parente JB, Terry A, Kalluri A, Song K, Rajapakse CS. Conquering the Cobb Angle: A Deep Learning Algorithm for Automated, Hardware-Invariant Measurement of Cobb Angle on Radiographs in Patients with Scoliosis. Radiol Artif Intell 2023; 5:e220158. [PMID: 37529207 PMCID: PMC10388214 DOI: 10.1148/ryai.220158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 05/16/2023] [Accepted: 06/05/2023] [Indexed: 08/03/2023]
Abstract
Scoliosis is a disease estimated to affect more than 8% of adults in the United States. It is diagnosed with use of radiography by means of manual measurement of the angle between maximally tilted vertebrae on a radiograph (ie, the Cobb angle). However, these measurements are time-consuming, limiting their use in scoliosis surgical planning and postoperative monitoring. In this retrospective study, a pipeline (using the SpineTK architecture) was developed that was trained, validated, and tested on 1310 anterior-posterior images obtained with a low-dose stereoradiographic scanning system and radiographs obtained in patients with suspected scoliosis to automatically measure Cobb angles. The images were obtained at six centers (2005-2020). The algorithm measured Cobb angles on hold-out internal (n = 460) and external (n = 161) test sets with less than 2° error (intraclass correlation coefficient, 0.96) compared with ground truth measurements by two experienced radiologists. Measurements, produced in less than 0.5 second, did not differ significantly (P = .05 cutoff) from ground truth measurements, regardless of the presence or absence of surgical hardware (P = .80), age (P = .58), sex (P = .83), body mass index (P = .63), scoliosis severity (P = .44), or image type (low-dose stereoradiographic image vs radiograph; P = .51) in the patient. These findings suggest that the algorithm is highly robust across different clinical characteristics. Given its automated, rapid, and accurate measurements, this network may be used for monitoring scoliosis progression in patients. Keywords: Cobb Angle, Convolutional Neural Network, Deep Learning Algorithms, Pediatrics, Machine Learning Algorithms, Scoliosis, Spine Supplemental material is available for this article. © RSNA, 2023.
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Baroncini A, Migliorini F, Eschweiler J, Hildebrand F, Trobisch P. The timing of tether breakage influences clinical results after VBT. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2022; 31:2362-2367. [PMID: 35864248 DOI: 10.1007/s00586-022-07321-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/01/2022] [Accepted: 07/06/2022] [Indexed: 05/20/2023]
Abstract
INTRODUCTION Tether breakage is a frequent mechanical complications after vertebral body tethering (VBT), but not all patients with a breakage show loss of correction. The reason of this clinical finding has not yet been clarified. We hypothesized that the integrity of the tether is relevant only in the early stages after VBT, when it drives growth modulation and tissue remodelling. After these mechanisms have taken place, the tether loses its function and a breakage will not alter the new shape of the spine. Thus, tether breakage would have a greater clinical relevance when occurring shortly after surgery. METHODS All consecutive patients who underwent VBT and had a min. 2-year follow-up were included. The difference in curve magnitude between the 1st standing x-ray and the last follow-up was calculated (ΔCobb). For each curve, the presence and timing of tether breakage were recorded. The curves were grouped according to if and when the breakage was observed (no breakage, breakage at 0-6 months, 6-12 months, > 12 months). The ΔCobb was compared among these groups with the analysis of variance (ANOVA). RESULTS Data from 152 curves were available: 68 with no breakage, 12 with a breakage at 0-6 months, 37 at 6-12 months and 35 > 12 months. The ANOVA found significant difference in the ΔCobb among the groups (Sum of square 2553.59; degree of freedom 3; mean of square 851.1; Fisher test 13.8; P < 0.0001). Patients with no breakage or breakage at > 12 months had similar ΔCobb (mean 4.8° and 7.8°, respectively, P = 0.3), smaller than the 0-6 or 6-12 groups (15.8° and 13.8°, respectively). CONCLUSION Tether breakage leads to a consistent loss of correction when occurring within the first 12 months, while it has limited clinical relevance when occurring later on.
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Affiliation(s)
- A Baroncini
- Department of Orthopaedic Surgery, RWTH Aachen University Clinic, Pauwelsstrasse 30, 52074, Aachen, Germany.
- Department of Spine Surgery, Eifelklinik St. Brigida, Simmerath, Germany.
| | - F Migliorini
- Department of Orthopaedic Surgery, RWTH Aachen University Clinic, Pauwelsstrasse 30, 52074, Aachen, Germany
| | - J Eschweiler
- Department of Orthopaedic Surgery, RWTH Aachen University Clinic, Pauwelsstrasse 30, 52074, Aachen, Germany
| | - F Hildebrand
- Department of Orthopaedic Surgery, RWTH Aachen University Clinic, Pauwelsstrasse 30, 52074, Aachen, Germany
| | - P Trobisch
- Department of Spine Surgery, Eifelklinik St. Brigida, Simmerath, Germany
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