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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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Huo X, Li H, Shao K. Automatic Vertebral Rotation Angle Measurement of 3D Vertebrae Based on an Improved Transformer Network. ENTROPY (BASEL, SWITZERLAND) 2024; 26:97. [PMID: 38392353 DOI: 10.3390/e26020097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 02/24/2024]
Abstract
The measurement of vertebral rotation angles serves as a crucial parameter in spinal assessments, particularly in understanding conditions such as idiopathic scoliosis. Historically, these angles were calculated from 2D CT images. However, such 2D techniques fail to comprehensively capture the intricate three-dimensional deformities inherent in spinal curvatures. To overcome the limitations of manual measurements and 2D imaging, we introduce an entirely automated approach for quantifying vertebral rotation angles using a three-dimensional vertebral model. Our method involves refining a point cloud segmentation network based on a transformer architecture. This enhanced network segments the three-dimensional vertebral point cloud, allowing for accurate measurement of vertebral rotation angles. In contrast to conventional network methodologies, our approach exhibits notable improvements in segmenting vertebral datasets. To validate our approach, we compare our automated measurements with angles derived from prevalent manual labeling techniques. The analysis, conducted through Bland-Altman plots and the corresponding intraclass correlation coefficient results, indicates significant agreement between our automated measurement method and manual measurements. The observed high intraclass correlation coefficients (ranging from 0.980 to 0.993) further underscore the reliability of our automated measurement process. Consequently, our proposed method demonstrates substantial potential for clinical applications, showcasing its capacity to provide accurate and efficient vertebral rotation angle measurements.
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Affiliation(s)
- Xing Huo
- School of Mathematics, Hefei University of Technology, Hefei 230601, China
| | - Hao Li
- School of Mathematics, Hefei University of Technology, Hefei 230601, China
| | - Kun Shao
- School of Software, Hefei University of Technology, Hefei 230601, China
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Aubert B, Cresson T, de Guise JA, Vazquez C. X-Ray to DRR Images Translation for Efficient Multiple Objects Similarity Measures in Deformable Model 3D/2D Registration. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:897-909. [PMID: 36318556 DOI: 10.1109/tmi.2022.3218568] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The robustness and accuracy of the intensity-based 3D/2D registration of a 3D model on planar X-ray image(s) is related to the quality of the image correspondences between the digitally reconstructed radiographs (DRR) generated from the 3D models (varying image) and the X-ray images (fixed target). While much effort may be devoted to generating realistic DRR that are similar to real X-rays (using complex X-ray simulation, adding densities information in 3D models, etc.), significant differences still remain between DRR and real X-ray images. Differences such as the presence of adjacent or superimposed soft tissue and bony or foreign structures lead to image matching difficulties and decrease the 3D/2D registration performance. In the proposed method, the X-ray images were converted into DRR images using a GAN-based cross-modality image-to-images translation. With this added prior step of XRAY-to-DRR translation, standard similarity measures become efficient even when using simple and fast DRR projection. For both images to match, they must belong to the same image domain and essentially contain the same kind of information. The XRAY-to-DRR translation also addresses the well-known issue of registering an object in a scene composed of multiple objects by separating the superimposed or/and adjacent objects to avoid mismatching across similar structures. We applied the proposed method to the 3D/2D fine registration of vertebra deformable models to biplanar radiographs of the spine. We showed that the XRAY-to-DRR translation enhances the registration results, by increasing the capture range and decreasing dependence on the similarity measure choice since the multi-modal registration becomes mono-modal.
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Ren G, Yu K, Xie Z, Wang P, Zhang W, Huang Y, Wang Y, Wu X. Current Applications of Machine Learning in Spine: From Clinical View. Global Spine J 2022; 12:1827-1840. [PMID: 34628966 PMCID: PMC9609532 DOI: 10.1177/21925682211035363] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES This review aims to present current applications of machine learning (ML) in spine domain to clinicians. METHODS We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. RESULTS Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. CONCLUSIONS ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.
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Affiliation(s)
- GuanRui Ren
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Kun Yu
- Nanjing Jiangbei Hospital, Nanjing,
Jiangsu, China
| | - ZhiYang Xie
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - PeiYang Wang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Wei Zhang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Yong Huang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - YunTao Wang
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,YunTao Wang, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
| | - XiaoTao Wu
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,XiaoTao Wu, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
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Using machine learning to automatically measure axial vertebral rotation on radiographs in adolescents with idiopathic scoliosis. Med Eng Phys 2022; 107:103848. [DOI: 10.1016/j.medengphy.2022.103848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 06/06/2022] [Accepted: 07/09/2022] [Indexed: 11/22/2022]
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Galbusera F, Bassani T, Panico M, Sconfienza LM, Cina A. A fresh look at spinal alignment and deformities: Automated analysis of a large database of 9832 biplanar radiographs. Front Bioeng Biotechnol 2022; 10:863054. [PMID: 35910028 PMCID: PMC9335010 DOI: 10.3389/fbioe.2022.863054] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
We developed and used a deep learning tool to process biplanar radiographs of 9,832 non-surgical patients suffering from spinal deformities, with the aim of reporting the statistical distribution of radiological parameters describing the spinal shape and the correlations and interdependencies between them. An existing tool able to automatically perform a three-dimensional reconstruction of the thoracolumbar spine has been improved and used to analyze a large set of biplanar radiographs of the trunk. For all patients, the following parameters were calculated: spinopelvic parameters; lumbar lordosis; mismatch between pelvic incidence and lumbar lordosis; thoracic kyphosis; maximal coronal Cobb angle; sagittal vertical axis; T1-pelvic angle; maximal vertebral rotation in the transverse plane. The radiological parameters describing the sagittal alignment were found to be highly interrelated with each other, as well as dependent on age, while sex had relatively minor but statistically significant importance. Lumbar lordosis was associated with thoracic kyphosis, pelvic incidence and sagittal vertical axis. The pelvic incidence-lumbar lordosis mismatch was found to be dependent on the pelvic incidence and on age. Scoliosis had a distinct association with the sagittal alignment in adolescent and adult subjects. The deep learning-based tool allowed for the analysis of a large imaging database which would not be reasonably feasible if performed by human operators. The large set of results will be valuable to trigger new research questions in the field of spinal deformities, as well as to challenge the current knowledge.
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Affiliation(s)
- Fabio Galbusera
- Spine Center, Schulthess Clinic, Zurich, Switzerland
- *Correspondence: Fabio Galbusera,
| | - Tito Bassani
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Matteo Panico
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Milan, Italy
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, Università Degli Studi di Milano, Milan, Italy
| | - Andrea Cina
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
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Artificial Intelligence in Adult Spinal Deformity. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:313-318. [PMID: 34862555 DOI: 10.1007/978-3-030-85292-4_35] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Artificial Intelligence is gaining traction in medicine for its ease of use and advancements in technology. This study evaluates the current literature on the use of artificial intelligence in adult spinal deformity.
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Chen K, Zhai X, Sun K, Wang H, Yang C, Li M. A narrative review of machine learning as promising revolution in clinical practice of scoliosis. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:67. [PMID: 33553360 PMCID: PMC7859734 DOI: 10.21037/atm-20-5495] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Machine learning (ML), as an advanced domain of artificial intelligence (AI), is progressively changing our view of the world. By implementing its algorithms, our ability to detect previously undiscoverable patterns in data has the potential to revolutionize predictive analytics. Scoliosis, as a relatively specialized branch in the spine field, mainly covers the pediatric, adult and the elderly populations, and its diagnosis and treatment remain difficult. With recent efforts and interdisciplinary cooperation, ML has been widely applied to investigate issues related to scoliosis, and surprisingly augment a surgeon's ability in clinical practice related to scoliosis. Meanwhile, ML models penetrate in every stage of the clinical practice procedure of scoliosis. In this review, we first present a brief description of the application of ML in the clinical practice procedures regarding scoliosis, including screening, diagnosis and classification, surgical decision making, intraoperative manipulation, complication prediction, prognosis prediction and rehabilitation. Meanwhile, the ML models and specific applications adopted are presented. Additionally, current limitations and future directions are briefly discussed regarding its use in the field of scoliosis. We believe that the implementation of ML is a promising revolution to assist surgeons in all aspects of clinical practice related to scoliosis in the near future.
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Affiliation(s)
- Kai Chen
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
| | - Xiao Zhai
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
| | - Kaiqiang Sun
- Department of Orthopedics, Shanghai Changzheng Hospital, Shanghai, China
| | - Haojue Wang
- Basic medicine college, Navy Medical University, Shanghai, China
| | - Changwei Yang
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
| | - Ming Li
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
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