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Yahanda AT, Joseph K, Bui T, Greenberg JK, Ray WZ, Ogunlade JI, Hafez D, Pallotta NA, Neuman BJ, Molina CA. Current Applications and Future Implications of Artificial Intelligence in Spine Surgery and Research: A Narrative Review and Commentary. Global Spine J 2024:21925682241290752. [PMID: 39359113 DOI: 10.1177/21925682241290752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/04/2024] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES Artificial intelligence (AI) is being increasingly applied to the domain of spine surgery. We present a review of AI in spine surgery, including its use across all stages of the perioperative process and applications for research. We also provide commentary regarding future ethical considerations of AI use and how it may affect surgeon-industry relations. METHODS We conducted a comprehensive literature review of peer-reviewed articles that examined applications of AI during the pre-, intra-, or postoperative spine surgery process. We also discussed the relationship among AI, spine industry partners, and surgeons. RESULTS Preoperatively, AI has been mainly applied to image analysis, patient diagnosis and stratification, decision-making. Intraoperatively, AI has been used to aid image guidance and navigation. Postoperatively, AI has been used for outcomes prediction and analysis. AI can enable curation and analysis of huge datasets that can enhance research efforts. Large amounts of data are being accrued by industry sources for use by their AI platforms, though the inner workings of these datasets or algorithms are not well known. CONCLUSIONS AI has found numerous uses in the pre-, intra-, or postoperative spine surgery process, and the applications of AI continue to grow. The clinical applications and benefits of AI will continue to be more fully realized, but so will certain ethical considerations. Making industry-sponsored databases open source, or at least somehow available to the public, will help alleviate potential biases and obscurities between surgeons and industry and will benefit patient care.
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Affiliation(s)
- Alexander T Yahanda
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Karan Joseph
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Tim Bui
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Jacob K Greenberg
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Wilson Z Ray
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - John I Ogunlade
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Daniel Hafez
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Nicholas A Pallotta
- Department of Orthopedic Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Brian J Neuman
- Department of Orthopedic Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Camilo A Molina
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
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Zhu Y, Yin X, Chen Z, Zhang H, Xu K, Zhang J, Wu N. Deep learning in Cobb angle automated measurement on X-rays: a systematic review and meta-analysis. Spine Deform 2024:10.1007/s43390-024-00954-4. [PMID: 39320698 DOI: 10.1007/s43390-024-00954-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 08/10/2024] [Indexed: 09/26/2024]
Abstract
PURPOSE This study aims to provide an overview of different deep learning algorithms (DLAs), identify the limitations, and summarize potential solutions to improve the performance of DLAs. METHODS We reviewed eligible studies on DLAs for automated Cobb angle estimation on X-rays and conducted a meta-analysis. A systematic literature search was conducted in six databases up until September 2023. Our meta-analysis included an evaluation of reported circular mean absolute error (CMAE) from the studies, as well as a subgroup analysis of implementation strategies. Risk of bias was assessed using the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). This study was registered in PROSPERO prior to initiation (CRD42023403057). RESULTS We identified 120 articles from our systematic search (n = 3022), eventually including 50 studies in the systematic review and 17 studies in the meta-analysis. The overall estimate for CMAE was 2.99 (95% CI 2.61-3.38), with high heterogeneity (94%, p < 0.01). Segmentation-based methods showed greater accuracy (p < 0.01), with a CMAE of 2.40 (95% CI 1.85-2.95), compared to landmark-based methods, which had a CMAE of 3.31 (95% CI 2.89-3.72). CONCLUSIONS According to our limited meta-analysis results, DLAs have shown relatively high accuracy for automated Cobb angle measurement. In terms of CMAE, segmentation-based methods may perform better than landmark-based methods. We also summarized potential ways to improve model design in future studies. It is important to follow quality guidelines when reporting on DLAs.
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Affiliation(s)
- Yuanpeng Zhu
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing, 100730, China
- Key Laboratory of Spinal Deformity Research and Application of Big Data, Beijing, 100730, China
| | - Xiangjie Yin
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing, 100730, China
- Key Laboratory of Spinal Deformity Research and Application of Big Data, Beijing, 100730, China
| | - Zefu Chen
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing, 100730, China
- Key Laboratory of Spinal Deformity Research and Application of Big Data, Beijing, 100730, China
| | - Haoran Zhang
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing, 100730, China
- Key Laboratory of Spinal Deformity Research and Application of Big Data, Beijing, 100730, China
| | - Kexin Xu
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing, 100730, China
- Key Laboratory of Spinal Deformity Research and Application of Big Data, Beijing, 100730, China
| | - Jianguo Zhang
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing, 100730, China.
- Key Laboratory of Spinal Deformity Research and Application of Big Data, Beijing, 100730, China.
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Beijing, 100730, China.
| | - Nan Wu
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing, 100730, China.
- Key Laboratory of Spinal Deformity Research and Application of Big Data, Beijing, 100730, China.
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Beijing, 100730, China.
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3
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Ruitenbeek HC, Oei EHG, Visser JJ, Kijowski R. Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade. Skeletal Radiol 2024; 53:1849-1868. [PMID: 38902420 DOI: 10.1007/s00256-024-04684-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/06/2024] [Accepted: 04/15/2024] [Indexed: 06/22/2024]
Abstract
This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.
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Affiliation(s)
- Huibert C Ruitenbeek
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Edwin H G Oei
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Jacob J Visser
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Richard Kijowski
- Department of Radiology, New York University Grossman School of Medicine, 660 First Avenue, 3rd Floor, New York, NY, 10016, USA.
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Kato S, Maeda Y, Nagura T, Nakamura M, Watanabe K. Comparison of three artificial intelligence algorithms for automatic cobb angle measurement using teaching data specific to three disease groups. Sci Rep 2024; 14:17989. [PMID: 39097613 PMCID: PMC11297987 DOI: 10.1038/s41598-024-68937-z] [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: 04/09/2024] [Accepted: 07/30/2024] [Indexed: 08/05/2024] Open
Abstract
Spinal deformities, including adolescent idiopathic scoliosis (AIS) and adult spinal deformity (ASD), affect many patients. The measurement of the Cobb angle on coronal radiographs is essential for their diagnosis and treatment planning. To enhance the precision of Cobb angle measurements for both AIS and ASD, we developed three distinct artificial intelligence (AI) algorithms: AIS/ASD-trained AI (trained with both AIS and ASD cases); AIS-trained AI (trained solely on AIS cases); ASD-trained AI (trained solely on ASD cases). We used 1612 whole-spine radiographs, including 1029 AIS and 583 ASD cases with variable postures, as teaching data. We measured the major and two minor curves. To assess the accuracy, we used 285 radiographs (159 AIS and 126 ASD) as a test set and calculated the mean absolute error (MAE) and intraclass correlation coefficient (ICC) between each AI algorithm and the average of manual measurements by four spine experts. The AIS/ASD-trained AI showed the highest accuracy among the three AI algorithms. This result suggested that learning across multiple diseases rather than disease-specific training may be an efficient AI learning method. The presented AI algorithm has the potential to reduce errors in Cobb angle measurements and improve the quality of clinical practice.
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Affiliation(s)
- Shuzo Kato
- Department of Orthopedic Surgery, Keio University School of Medicine, Shinanomachi 35, Shinjuku, Tokyo, 160-8582, Japan
| | - Yoshihiro Maeda
- Department of Orthopedic Surgery, Keio University School of Medicine, Shinanomachi 35, Shinjuku, Tokyo, 160-8582, Japan
| | - Takeo Nagura
- Department of Orthopedic Surgery, Keio University School of Medicine, Shinanomachi 35, Shinjuku, Tokyo, 160-8582, Japan
| | - Masaya Nakamura
- Department of Orthopedic Surgery, Keio University School of Medicine, Shinanomachi 35, Shinjuku, Tokyo, 160-8582, Japan
| | - Kota Watanabe
- Department of Orthopedic Surgery, Keio University School of Medicine, Shinanomachi 35, Shinjuku, Tokyo, 160-8582, Japan.
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Tingsheng L, Chunshan L, Shudan Y, Xingwei P, Qiling C, Minglu Y, Lu C, Lihang W. Validation of Artificial Intelligence in the Classification of Adolescent Idiopathic Scoliosis and the Compairment to Clinical Manual Handling. Orthop Surg 2024; 16:2040-2051. [PMID: 38961674 PMCID: PMC11293916 DOI: 10.1111/os.14144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 07/05/2024] Open
Abstract
OBJECTIVE The accurate measurement of Cobb angles is crucial for the effective clinical management of patients with adolescent idiopathic scoliosis (AIS). The Lenke classification system plays a pivotal role in determining the appropriate fusion levels for treatment planning. However, the presence of interobserver variability and time-intensive procedures presents challenges for clinicians. The purpose of this study is to compare the measurement accuracy of our developed artificial intelligence measurement system for Cobb angles and Lenke classification in AIS patients with manual measurements to validate its feasibility. METHODS An artificial intelligence (AI) system measured the Cobb angle of AIS patients using convolutional neural networks, which identified the vertebral boundaries and sequences, recognized the upper and lower end vertebras, and estimated the Cobb angles of the proximal thoracic, main thoracic, and thoracolumbar/lumbar curves sequentially. Accordingly, the Lenke classifications of scoliosis were divided by oscillogram and defined by the AI system. Furthermore, a man-machine comparison (n = 300) was conducted for senior spine surgeons (n = 2), junior spine surgeons (n = 2), and the AI system for the image measurements of proximal thoracic (PT), main thoracic (MT), thoracolumbar/lumbar (TL/L), thoracic sagittal profile T5-T12, bending views PT, bending views MT, bending views TL/L, the Lenke classification system, the lumbar modifier, and sagittal thoracic alignment. RESULTS In the AI system, the calculation time for each patient's data was 0.2 s, while the measurement time for each surgeon was 23.6 min. The AI system showed high accuracy in the recognition of the Lenke classification and had high reliability compared to senior doctors (ICC 0.962). CONCLUSION The AI system has high reliability for the Lenke classification and is a potential auxiliary tool for spinal surgeons.
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Affiliation(s)
- Lu Tingsheng
- Department of Spine SurgeryBeijing Jishuitan Hospital Guizhou HospitalGuiyangChina
| | - Luo Chunshan
- Department of Spine SurgeryBeijing Jishuitan Hospital Guizhou HospitalGuiyangChina
| | - Yao Shudan
- Department of Spine SurgeryBeijing Jishuitan Hospital Guizhou HospitalGuiyangChina
| | - Pu Xingwei
- Department of Spine SurgeryBeijing Jishuitan Hospital Guizhou HospitalGuiyangChina
| | - Chen Qiling
- Department of Spine SurgeryBeijing Jishuitan Hospital Guizhou HospitalGuiyangChina
| | - Yang Minglu
- Department of Spine SurgeryBeijing Jishuitan Hospital Guizhou HospitalGuiyangChina
| | - Chen Lu
- Department of Spine SurgeryBeijing Jishuitan Hospital Guizhou HospitalGuiyangChina
| | - Wang Lihang
- Department of Spine SurgeryBeijing Jishuitan Hospital Guizhou HospitalGuiyangChina
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6
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Wong J, Reformat M, Parent E, Lou E. Using machine learning to automatically measure kyphotic and lordotic angle measurements on radiographs for children with adolescent idiopathic scoliosis. Med Eng Phys 2024; 130:104202. [PMID: 39160016 DOI: 10.1016/j.medengphy.2024.104202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 05/08/2024] [Accepted: 06/27/2024] [Indexed: 08/21/2024]
Abstract
Measuring the kyphotic angle (KA) and lordotic angle (LA) on lateral radiographs is important to truly diagnose children with adolescent idiopathic scoliosis. However, it is a time-consuming process to measure the KA because the endplate of the upper thoracic vertebra is normally difficult to identify. To save time and improve measurement accuracy, a machine learning algorithm was developed to automatically extract the KA and LA. The accuracy and reliability of the T1-T12 KA, T5-T12 KA, and L1-L5 LA were reported. A convolutional neural network was trained using 100 radiographs with data augmentation to segment the T1-L5 vertebrae. Sixty radiographs were used to test the method. Accuracy and reliability were reported using the percentage of measurements within clinical acceptance (≤9°), standard error of measurement (SEM), and inter-method intraclass correlation coefficient (ICC2,1). The automatic method detected 95 % (57/60), 100 %, and 100 % for T1-T12 KA, T5-T12 KA, and L1-L5 LA, respectively. The clinical acceptance rate, SEM, and ICC2,1 for T1-T12 KA, T5-T12 KA, and L1-L5 LA were (98 %, 0.80°, 0.91), (75 %, 4.08°, 0.60), and (97 %, 1.38°, 0.88), respectively. The automatic method measured quickly with an average of 4 ± 2 s per radiograph and illustrated how measurements were made on the image, allowing verifications by clinicians.
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Affiliation(s)
- Jason Wong
- Department of Electrical and Computer Engineering, University of Alberta, Donadeo Innovation Centre for Engineering, 9211-116 Street, Edmonton, AB T6G 1H9, Canada
| | - Marek Reformat
- Department of Electrical and Computer Engineering, University of Alberta, Donadeo Innovation Centre for Engineering, 9211-116 Street, Edmonton, AB T6G 1H9, Canada; Information Technology Institute, University of Social Sciences, 90-113 Lodz, Poland
| | - Eric Parent
- Department of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Corbett Hall, Edmonton, AB T6G 2G4, Canada
| | - Edmond Lou
- Department of Electrical and Computer Engineering, University of Alberta, Donadeo Innovation Centre for Engineering, 9211-116 Street, Edmonton, AB T6G 1H9, Canada.
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Berlin C, Adomeit S, Grover P, Dreischarf M, Halm H, Dürr O, Obid P. Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays. Global Spine J 2024; 14:1728-1737. [PMID: 36708281 PMCID: PMC11268300 DOI: 10.1177/21925682231154543] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
STUDY DESIGN Retrospective, mono-centric cohort research study. OBJECTIVES The purpose of this study is to validate a novel artificial intelligence (AI)-based algorithm against human-generated ground truth for radiographic parameters of adolescent idiopathic scoliosis (AIS). METHODS An AI-algorithm was developed that is capable of detecting anatomical structures of interest (clavicles, cervical, thoracic, lumbar spine and sacrum) and calculate essential radiographic parameters in AP spine X-rays fully automatically. The evaluated parameters included T1-tilt, clavicle angle (CA), coronal balance (CB), lumbar modifier, and Cobb angles in the proximal thoracic (C-PT), thoracic, and thoracolumbar regions. Measurements from 2 experienced physicians on 100 preoperative AP full spine X-rays of AIS patients were used as ground truth and to evaluate inter-rater and intra-rater reliability. The agreement between human raters and AI was compared by means of single measure Intra-class Correlation Coefficients (ICC; absolute agreement; >.75 rated as excellent), mean error and additional statistical metrics. RESULTS The comparison between human raters resulted in excellent ICC values for intra- (range: .97-1) and inter-rater (.85-.99) reliability. The algorithm was able to determine all parameters in 100% of images with excellent ICC values (.78-.98). Consistently with the human raters, ICC values were typically smallest for C-PT (eg, rater 1A vs AI: .78, mean error: 4.7°) and largest for CB (.96, -.5 mm) as well as CA (.98, .2°). CONCLUSIONS The AI-algorithm shows excellent reliability and agreement with human raters for coronal parameters in preoperative full spine images. The reliability and speed offered by the AI-algorithm could contribute to the efficient analysis of large datasets (eg, registry studies) and measurements in clinical practice.
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Affiliation(s)
- Clara Berlin
- Spine Surgery and Scoliosis Center,Schön Klinik Neustadt, Germany
| | - Sonja Adomeit
- Heidelberg University, Interdisciplinary Center for Scientific Computing, Germany
| | | | | | - Henry Halm
- Spine Surgery and Scoliosis Center,Schön Klinik Neustadt, Germany
| | - Oliver Dürr
- Research and Development, RAYLYTIC GmbH, Germany
| | - Peter Obid
- Department of Orthopaedics and Traumatology, Freiburg University Hospital, Germany
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8
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Landriel F, Franchi BC, Mosquera C, Lichtenberger FP, Benitez S, Aineseder M, Guiroy A, Hem S. Artificial Intelligence Assistance for the Measurement of Full Alignment Parameters in Whole-Spine Lateral Radiographs. World Neurosurg 2024; 187:e363-e382. [PMID: 38649028 DOI: 10.1016/j.wneu.2024.04.091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Measuring spinal alignment with radiological parameters is essential in patients with spinal conditions likely to be treated surgically. These evaluations are not usually included in the radiological report. As a result, spinal surgeons commonly perform the measurement, which is time-consuming and subject to errors. We aim to develop a fully automated artificial intelligence (AI) tool to assist in measuring alignment parameters in whole-spine lateral radiograph (WSL X-rays). METHODS We developed a tool called Vertebrai that automatically calculates the global spinal parameters (GSPs): Pelvic incidence, sacral slope, pelvic tilt, L1-L4 angle, L4-S1 lumbo-pelvic angle, T1 pelvic angle, sagittal vertical axis, cervical lordosis, C1-C2 lordosis, lumbar lordosis, mid-thoracic kyphosis, proximal thoracic kyphosis, global thoracic kyphosis, T1 slope, C2-C7 plummet, spino-sacral angle, C7 tilt, global tilt, spinopelvic tilt, and hip odontoid axis. We assessed human-AI interaction instead of AI performance alone. We compared the time to measure GSP and inter-rater agreement with and without AI assistance. Two institutional datasets were created with 2267 multilabel images for classification and 784 WSL X-rays with reference standard landmark labeled by spinal surgeons. RESULTS Vertebrai significantly reduced the measurement time comparing spine surgeons with AI assistance and the AI algorithm alone, without human intervention (3 minutes vs. 0.26 minutes; P < 0.05). Vertebrai achieved an average accuracy of 83% in detecting abnormal alignment values, with the sacral slope parameter exhibiting the lowest accuracy at 61.5% and spinopelvic tilt demonstrating the highest accuracy at 100%. Intraclass correlation analysis revealed a high level of correlation and consistency in the global alignment parameters. CONCLUSIONS Vertebrai's measurements can accurately detect alignment parameters, making it a promising tool for measuring GSP automatically.
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Affiliation(s)
- Federico Landriel
- Neurosurgical Department, Spine Unit, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
| | - Bruno Cruz Franchi
- Health Informatic Department, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | - Candelaria Mosquera
- Health Informatic Department, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | | | - Sonia Benitez
- Health Informatic Department, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | - Martina Aineseder
- Radiology Department, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | | | - Santiago Hem
- Neurosurgical Department, Spine Unit, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
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9
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Lee S, Jung JY, Mahatthanatrakul A, Kim JS. Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances. Neurospine 2024; 21:474-486. [PMID: 38955525 PMCID: PMC11224760 DOI: 10.14245/ns.2448388.194] [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: 04/16/2024] [Revised: 05/14/2024] [Accepted: 05/23/2024] [Indexed: 07/04/2024] Open
Abstract
Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.
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Affiliation(s)
- Sungwon Lee
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Visual Analysis and Learning for Improved Diagnostics (VALID) Lab, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Joon-Yong Jung
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Visual Analysis and Learning for Improved Diagnostics (VALID) Lab, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Akaworn Mahatthanatrakul
- Department of Orthopaedics, Faculty of Medicine, Naresuan University Hospital, Phitsanulok, Thailand
| | - Jin-Sung Kim
- Spine Center, Department of Neurosurgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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10
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Noh SH, Lee G, Bae HJ, Han JY, Son SJ, Kim D, Park JY, Choi SK, Cho PG, Kim SH, Yuh WT, Lee SH, Park B, Kim KR, Kim KT, Ha Y. Deep Learning Method for Precise Landmark Identification and Structural Assessment of Whole-Spine Radiographs. Bioengineering (Basel) 2024; 11:481. [PMID: 38790348 PMCID: PMC11117576 DOI: 10.3390/bioengineering11050481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 05/02/2024] [Accepted: 05/09/2024] [Indexed: 05/26/2024] Open
Abstract
This study measured parameters automatically by marking the point for measuring each parameter on whole-spine radiographs. Between January 2020 and December 2021, 1017 sequential lateral whole-spine radiographs were retrospectively obtained. Of these, 819 and 198 were used for training and testing the performance of the landmark detection model, respectively. To objectively evaluate the program's performance, 690 whole-spine radiographs from four other institutions were used for external validation. The combined dataset comprised radiographs from 857 female and 850 male patients (average age 42.2 ± 27.3 years; range 20-85 years). The landmark localizer showed the highest accuracy in identifying cervical landmarks (median error 1.5-2.4 mm), followed by lumbosacral landmarks (median error 2.1-3.0 mm). However, thoracic landmarks displayed larger localization errors (median 2.4-4.3 mm), indicating slightly reduced precision compared with the cervical and lumbosacral regions. The agreement between the deep learning model and two experts was good to excellent, with intraclass correlation coefficient values >0.88. The deep learning model also performed well on the external validation set. There were no statistical differences between datasets in all parameters, suggesting that the performance of the artificial intelligence model created was excellent. The proposed automatic alignment analysis system identified anatomical landmarks and positions of the spine with high precision and generated various radiograph imaging parameters that had a good correlation with manual measurements.
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Affiliation(s)
- Sung Hyun Noh
- Department of Neurosurgery, Ajou University College of Medicine, Suwon 16499, Republic of Korea
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Gaeun Lee
- Promedius Inc., Seoul 05609, Republic of Korea
| | | | - Ju Yeon Han
- Department of Neurosurgery, Ajou University College of Medicine, Suwon 16499, Republic of Korea
| | - Su Jeong Son
- Department of Neurosurgery, Ajou University College of Medicine, Suwon 16499, Republic of Korea
| | - Deok Kim
- Department of Neurosurgery, Ajou University College of Medicine, Suwon 16499, Republic of Korea
| | - Jeong Yeon Park
- Department of Neurosurgery, Ajou University College of Medicine, Suwon 16499, Republic of Korea
| | - Seung Kyeong Choi
- Department of Neurosurgery, Ajou University College of Medicine, Suwon 16499, Republic of Korea
| | - Pyung Goo Cho
- Department of Neurosurgery, Ajou University College of Medicine, Suwon 16499, Republic of Korea
| | - Sang Hyun Kim
- Department of Neurosurgery, Ajou University College of Medicine, Suwon 16499, Republic of Korea
| | - Woon Tak Yuh
- Department of Neurosurgery, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si 18450, Republic of Korea
| | - Su Hun Lee
- Department of Neurosurgery, Pusan National University Yangsan Hospital, Busan 50612, Republic of Korea
| | - Bumsoo Park
- Department of Neurosurgery, Bon Hospital, Daejeon 34188, Republic of Korea
| | - Kwang-Ryeol Kim
- Department of Neurosurgery, Daegu Catholic University College of Medicine, Daegu 42400, Republic of Korea
| | - Kyoung-Tae Kim
- Department. of Neurosurgery, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu 41944, Republic of Korea
| | - Yoon Ha
- Department of Neurosurgery, Spine and Spinal Cord Institute, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
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Bcharah G, Gupta N, Panico N, Winspear S, Bagley A, Turnow M, D'Amico R, Ukachukwu AEK. Innovations in Spine Surgery: A Narrative Review of Current Integrative Technologies. World Neurosurg 2024; 184:127-136. [PMID: 38159609 DOI: 10.1016/j.wneu.2023.12.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 12/22/2023] [Indexed: 01/03/2024]
Abstract
Neurosurgical technologies have become increasingly more adaptive, featuring real-time and patient-specific guidance in preoperative, intraoperative, and postoperative settings. This review offers insight into how these integrative innovations compare with conventional approaches in spine surgery, focusing on machine learning (ML), artificial intelligence, augmented reality and virtual reality, and spinal navigation systems. Data on technology applications, diagnostic and procedural accuracy, intraoperative times, radiation exposures, postoperative outcomes, and costs were extracted and compared with conventional methods to assess their advantages and limitations. Preoperatively, augmented reality and virtual reality have applications in surgical training and planning that are more immersive, case specific, and risk-free and have been shown to enhance accuracy and reduce complications. ML algorithms have demonstrated high accuracy in predicting surgical candidacy (up to 92.1%) and tailoring personalized treatments based on patient-specific variables. Intraoperatively, advantages include more accurate pedicle screw insertion (96%-99% with ML), enhanced visualization, reduced radiation exposure (49 μSv with O-arm navigation vs. 556 μSv with fluoroscopy), increased efficiency, and potential for fewer intraoperative complications compared with conventional approaches. Postoperatively, certain ML and artificial intelligence models have outperformed conventional methods in predicting all postoperative complications of >6000 patients as well as predicting variables contributing to in-hospital and 90-day mortality. However, applying these technologies comes with limitations, such as longer operative times (up to 35.6% longer) with navigation, dependency on datasets, costs, accessibility, steep learning curve, and inherent software malfunctions. As these technologies advance, continuing to assess their efficacy and limitations will be crucial to their successful integration within spine surgery.
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Affiliation(s)
- George Bcharah
- Mayo Clinic Alix School of Medicine, Scottsdale, Arizona, USA
| | - Nithin Gupta
- Campbell University School of Osteopathic Medicine, Lillington, North Carolina, USA
| | - Nicholas Panico
- Lake Erie College of Osteopathic Medicine, Erie, Pennsylvania, USA
| | - Spencer Winspear
- Campbell University School of Osteopathic Medicine, Lillington, North Carolina, USA
| | - Austin Bagley
- Campbell University School of Osteopathic Medicine, Lillington, North Carolina, USA
| | - Morgan Turnow
- Kentucky College of Osteopathic Medicine, Pikeville, Kentucky, USA
| | - Randy D'Amico
- Department of Neurosurgery, Lenox Hill Hospital, New York, New York, USA
| | - Alvan-Emeka K Ukachukwu
- Department of Neurosurgery, Duke University, Durham, North Carolina, USA; Duke Global Neurosurgery and Neurology, Durham, North Carolina, USA.
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MohammadiNasrabadi A, Moammer G, Quateen A, Bhanot K, McPhee J. Landet: an efficient physics-informed deep learning approach for automatic detection of anatomical landmarks and measurement of spinopelvic alignment. J Orthop Surg Res 2024; 19:199. [PMID: 38528514 DOI: 10.1186/s13018-024-04654-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 03/02/2024] [Indexed: 03/27/2024] Open
Abstract
PURPOSE An efficient physics-informed deep learning approach for extracting spinopelvic measures from X-ray images is introduced and its performance is evaluated against manual annotations. METHODS Two datasets, comprising a total of 1470 images, were collected to evaluate the model's performance. We propose a novel method of detecting landmarks as objects, incorporating their relationships as constraints (LanDet). Using this approach, we trained our deep learning model to extract five spine and pelvis measures: Sacrum Slope (SS), Pelvic Tilt (PT), Pelvic Incidence (PI), Lumbar Lordosis (LL), and Sagittal Vertical Axis (SVA). The results were compared to manually labelled test dataset (GT) as well as measures annotated separately by three surgeons. RESULTS The LanDet model was evaluated on the two datasets separately and on an extended dataset combining both. The final accuracy for each measure is reported in terms of Mean Absolute Error (MAE), Standard Deviation (SD), and R Pearson correlation coefficient as follows: [ S S ∘ : 3.7 ( 2.7 ) , R = 0.89 ] ,[ P T ∘ : 1.3 ( 1.1 ) , R = 0.98 ] , [ P I ∘ : 4.2 ( 3.1 ) , R = 0.93 ] , [ L L ∘ : 5.1 ( 6.4 ) , R = 0.83 ] , [ S V A ( m m ) : 2.1 ( 1.9 ) , R = 0.96 ] . To assess model reliability and compare it against surgeons, the intraclass correlation coefficient (ICC) metric is used. The model demonstrated better consistency with surgeons with all values over 0.88 compared to what was previously reported in the literature. CONCLUSION The LanDet model exhibits competitive performance compared to existing literature. The effectiveness of the physics-informed constraint method, utilized in our landmark detection as object algorithm, is highlighted. Furthermore, we addressed the limitations of heatmap-based methods for anatomical landmark detection and tackled issues related to mis-identifying of similar or adjacent landmarks instead of intended landmark using this novel approach.
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Affiliation(s)
- AliAsghar MohammadiNasrabadi
- Department of Systems Design Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada.
| | - Gemah Moammer
- Department of Spine Surgery, Grand River Hospital (GRH), 835 King St W, Kitchener, ON, N2G 1G3, Canada
| | - Ahmed Quateen
- Department of Spine Surgery, Grand River Hospital (GRH), 835 King St W, Kitchener, ON, N2G 1G3, Canada
| | - Kunal Bhanot
- Department of Surgery, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada
| | - John McPhee
- Department of Systems Design Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada
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Yuh WT, Khil EK, Yoon YS, Kim B, Yoon H, Lim J, Lee KY, Yoo YS, An KD. Deep Learning-Assisted Quantitative Measurement of Thoracolumbar Fracture Features on Lateral Radiographs. Neurospine 2024; 21:30-43. [PMID: 38569629 PMCID: PMC10992637 DOI: 10.14245/ns.2347366.683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 01/24/2024] [Accepted: 02/02/2024] [Indexed: 04/05/2024] Open
Abstract
OBJECTIVE This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. METHODS Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics-compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)-from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. RESULTS The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. CONCLUSION The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.
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Affiliation(s)
- Woon Tak Yuh
- Department of Neurosurgery, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea
| | - Eun Kyung Khil
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea
- Department of Radiology, Fastbone Orthopedic Hospital, Hwaseong, Korea
| | - Yu Sung Yoon
- Department of Radiology, Kyungpook National University Hospital, School of Medicine, Kyungpook National University, Daegu, Korea
| | | | | | - Jihe Lim
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea
| | - Kyoung Yeon Lee
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea
| | - Yeong Seo Yoo
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea
| | - Kyeong Deuk An
- Department of Neurosurgery, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea
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14
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Bharadwaj UU, Chin CT, Majumdar S. Practical Applications of Artificial Intelligence in Spine Imaging: A Review. Radiol Clin North Am 2024; 62:355-370. [PMID: 38272627 DOI: 10.1016/j.rcl.2023.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Artificial intelligence (AI), a transformative technology with unprecedented potential in medical imaging, can be applied to various spinal pathologies. AI-based approaches may improve imaging efficiency, diagnostic accuracy, and interpretation, which is essential for positive patient outcomes. This review explores AI algorithms, techniques, and applications in spine imaging, highlighting diagnostic impact and challenges with future directions for integrating AI into spine imaging workflow.
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Affiliation(s)
- Upasana Upadhyay Bharadwaj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 1700 4th Street, Byers Hall, Suite 203, Room 203D, San Francisco, CA 94158, USA
| | - Cynthia T Chin
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, Box 0628, San Francisco, CA 94143, USA.
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 1700 4th Street, Byers Hall, Suite 203, Room 203D, San Francisco, CA 94158, USA
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Lee S, Kim KG, Kim YJ, Jeon JS, Lee GP, Kim KC, Jeon SH. Automatic Segmentation and Radiologic Measurement of Distal Radius Fractures Using Deep Learning. Clin Orthop Surg 2024; 16:113-124. [PMID: 38304219 PMCID: PMC10825247 DOI: 10.4055/cios23130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/10/2023] [Accepted: 10/24/2023] [Indexed: 02/03/2024] Open
Abstract
Background Recently, deep learning techniques have been used in medical imaging studies. We present an algorithm that measures radiologic parameters of distal radius fractures using a deep learning technique and compares the predicted parameters with those measured by an orthopedic hand surgeon. Methods We collected anteroposterior (AP) and lateral X-ray images of 634 wrists in 624 patients with distal radius fractures treated conservatively with a follow-up of at least 2 months. We allocated 507 AP and 507 lateral images to the training set (80% of the images were used to train the model, and 20% were utilized for validation) and 127 AP and 127 lateral images to the test set. The margins of the radius and ulna were annotated for ground truth, and the scaphoid in the lateral views was annotated in the box configuration to determine the volar side of the images. Radius segmentation was performed using attention U-Net, and the volar/dorsal side was identified using a detection and classification model based on RetinaNet. The proposed algorithm measures the radial inclination, dorsal or volar tilt, and radial height by index axes and points from the segmented radius and ulna. Results The segmentation model for the radius exhibited an accuracy of 99.98% and a Dice similarity coefficient (DSC) of 98.07% for AP images, and an accuracy of 99.75% and a DSC of 94.84% for lateral images. The segmentation model for the ulna showed an accuracy of 99.84% and a DSC of 96.48%. Based on the comparison of the radial inclinations measured by the algorithm and the manual method, the Pearson correlation coefficient was 0.952, and the intraclass correlation coefficient was 0.975. For dorsal/volar tilt, the correlation coefficient was 0.940, and the intraclass correlation coefficient was 0.968. For radial height, it was 0.768 and 0.868, respectively. Conclusions The deep learning-based algorithm demonstrated excellent segmentation of the distal radius and ulna in AP and lateral radiographs of the wrist with distal radius fractures and afforded automatic measurements of radiologic parameters.
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Affiliation(s)
- Sanglim Lee
- Department of Orthopedic Surgery, Inje University Sanggye Paik Hospital, Seoul, Korea
| | - Kwang Gi Kim
- Department of Biomedical Engineering, Gachon University College of Medicine, Incheon, Korea
| | - Young Jae Kim
- Department of Biomedical Engineering, Gachon University College of Medicine, Incheon, Korea
| | - Ji Soo Jeon
- Department of Biomedical Engineering, Gachon University College of Medicine, Incheon, Korea
| | - Gi Pyo Lee
- Department of Biomedical Engineering, Gachon University College of Medicine, Incheon, Korea
| | - Kyung-Chan Kim
- Department of Orthopedic Surgery, Inje University Sanggye Paik Hospital, Seoul, Korea
| | - Suk Ha Jeon
- Department of Orthopaedic Surgery, National Medical Center, Seoul, Korea
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16
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Löchel J, Putzier M, Dreischarf M, Grover P, Urinbayev K, Abbas F, Labbus K, Zahn R. Deep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity. 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 2024:10.1007/s00586-023-08109-1. [PMID: 38231388 DOI: 10.1007/s00586-023-08109-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 11/03/2023] [Accepted: 12/15/2023] [Indexed: 01/18/2024]
Abstract
AIM Deep learning (DL) algorithms can be used for automated analysis of medical imaging. The aim of this study was to assess the accuracy of an innovative, fully automated DL algorithm for analysis of sagittal balance in adult spinal deformity (ASD). MATERIAL AND METHODS Sagittal balance (sacral slope, pelvic tilt, pelvic incidence, lumbar lordosis and sagittal vertical axis) was evaluated in 141 preoperative and postoperative radiographs of patients with ASD. The DL, landmark-based measurements, were compared with the ground truth values from validated manual measurements. RESULTS The DL algorithm showed an excellent consistency with the ground truth measurements. The intra-class correlation coefficient between the DL and ground truth measurements was 0.71-0.99 for preoperative and 0.72-0.96 for postoperative measurements. The DL detection rate was 91.5% and 84% for preoperative and postoperative images, respectively. CONCLUSION This is the first study evaluating a complete automated DL algorithm for analysis of sagittal balance with high accuracy for all evaluated parameters. The excellent accuracy in the challenging pathology of ASD with long construct instrumentation demonstrates the eligibility and possibility for implementation in clinical routine.
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Affiliation(s)
- Jannis Löchel
- Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany.
| | - Michael Putzier
- Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Marcel Dreischarf
- RAYLYTIC - medical data automation, Petersstr. 32-34, 04109, Leipzig, Germany
| | - Priyanka Grover
- RAYLYTIC - medical data automation, Petersstr. 32-34, 04109, Leipzig, Germany
| | | | - Fahad Abbas
- Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Kirsten Labbus
- Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Robert Zahn
- Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany
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17
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Huang X, Luo M, Liu L, Wu D, You X, Deng Z, Xiu P, Yang X, Zhou C, Feng G, Wang L, Zhou Z, Fan J, He M, Gao Z, Pu L, Wu Z, Zhou Z, Song Y, Huang S. The Comparison of Convolutional Neural Networks and the Manual Measurement of Cobb Angle in Adolescent Idiopathic Scoliosis. Global Spine J 2024; 14:159-168. [PMID: 35622711 PMCID: PMC10676172 DOI: 10.1177/21925682221098672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
STUDY DESIGN Comparative study. OBJECTIVE To compare manual and deep learning-based automated measurement of Cobb angle in adolescent idiopathic scoliosis. METHODS We proposed a fully automated framework to measure the Cobb angle of AIS patients. Whole-spine images of 500 AIS individuals were collected. 200 digital radiographic (DR) images were labeled manually as training set, and the remaining 300 images were used to validate by mean absolute error (MAE), Pearson or spearman correlation coefficients, and intra/interclass correlation coefficients (ICCs). The relationship between accuracy of vertebral boundary identification and the subjective image quality score was evaluated. RESULTS The PT, MT, and TL/L Cobb angles were measured by the automated framework within 300 milliseconds. Remarkable 2.92° MAE, .967 ICC, and high correlation coefficient (r = .972) were obtained for the major curve. The MAEs of PT, MT, and TL/L were 3.04°, 2.72°, and 2.53°, respectively. The ICCs of these 3 curves were .936, .977, and .964, respectively. 88.7% (266/300) of cases had a difference range of ±5°, with 84.3% (253/300) for PT, 89.7% (269/300) for MT, and 93.0% (279/300) for TL/L. The decreased bone/soft tissue contrast (2.94 vs 3.26; P=.039) and bone sharpness (2.97 vs 3.35; P=.029) were identified in the images with MAE exceeding 5°. CONCLUSION The fully automated framework not only identifies the vertebral boundaries, vertebral sequences, the upper/lower end vertebras and apical vertebra, but also calculates the Cobb angle of PT, MT, and TL/L curves sequentially. The framework would shed new light on the assessment of AIS curvature.
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Affiliation(s)
- Xianming Huang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Ming Luo
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
- Department of Spine Surgery and Musculoskeletal Tumor, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China
- Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing, China
| | - Limin Liu
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Diwei Wu
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Xuanhe You
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Zhipeng Deng
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Peng Xiu
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Xi Yang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Chunguang Zhou
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Ganjun Feng
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Lei Wang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Zhongjie Zhou
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Jipeng Fan
- Chengdu Chengdian Goldisc Health Data Technology Co., Ltd, Chengdu, China
| | - Mingjie He
- Chengdu Chengdian Goldisc Health Data Technology Co., Ltd, Chengdu, China
| | - Zhongjun Gao
- Chengdu Chengdian Goldisc Health Data Technology Co., Ltd, Chengdu, China
| | - Lixin Pu
- Chengdu Chengdian Goldisc Health Data Technology Co., Ltd, Chengdu, China
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhihong Wu
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing, China
| | - Zongke Zhou
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Yueming Song
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Shishu Huang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
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Rauber C, Lüscher D, Poux L, Schori M, Deml MC, Hasler CC, Bassani T, Galbusera F, Büchler P, Schmid S. Predicted vs. measured paraspinal muscle activity in adolescent idiopathic scoliosis patients: EMG validation of optimization-based musculoskeletal simulations. J Biomech 2024; 163:111922. [PMID: 38220500 DOI: 10.1016/j.jbiomech.2023.111922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/16/2023] [Accepted: 12/31/2023] [Indexed: 01/16/2024]
Abstract
Musculoskeletal (MSK) models offer great potential for predicting the muscle forces required to inform more detailed simulations of vertebral endplate loading in adolescent idiopathic scoliosis (AIS). In this work, simulations based on static optimization were compared with in vivo measurements in two AIS patients to determine whether computational approaches alone are sufficient for accurate prediction of paraspinal muscle activity during functional activities. We used biplanar radiographs and marker-based motion capture, ground reaction force, and electromyography (EMG) data from two patients with mild and moderate thoracolumbar AIS (Cobb angles: 21° and 45°, respectively) during standing while holding two weights in front (reference position), walking, running, and object lifting. Using a fully automated approach, 3D spinal shape was extracted from the radiographs. Geometrically personalized OpenSim-based MSK models were created by deforming the spine of pre-scaled full-body models of children/adolescents. Simulations were performed using an experimentally controlled backward approach. Differences between model predictions and EMG measurements of paraspinal muscle activity (both expressed as a percentage of the reference position values) at three different locations around the scoliotic main curve were quantified by root mean square error (RMSE) and cross-correlation (XCorr). Predicted and measured muscle activity correlated best for mild AIS during object lifting (XCorr's ≥ 0.97), with relatively low RMSE values. For moderate AIS as well as the walking and running activities, agreement was lower, with XCorr reaching values of 0.51 and comparably high RMSE values. This study demonstrates that static optimization alone seems not appropriate for predicting muscle activity in AIS patients, particularly in those with more than mild deformations as well as when performing upright activities such as walking and running.
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Affiliation(s)
- Cedric Rauber
- Spinal Movement Biomechanics Group, School of Health Professions, Bern University of Applied Sciences, Bern, Switzerland; Computational Bioengineering Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Dominique Lüscher
- Spinal Movement Biomechanics Group, School of Health Professions, Bern University of Applied Sciences, Bern, Switzerland; Computational Bioengineering Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Lucile Poux
- Spinal Movement Biomechanics Group, School of Health Professions, Bern University of Applied Sciences, Bern, Switzerland
| | - Maria Schori
- Physiotherapie Maria Schori Bern, Bern, Switzerland
| | - Moritz C Deml
- Department of Orthopaedic Surgery and Traumatology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Carol-Claudius Hasler
- Orthopaedic Department and Spine Surgery, University Children's Hospital Basel, Basel, Switzerland
| | - Tito Bassani
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Fabio Galbusera
- Spine Research Group, Schulthess Klinik, Zürich, Switzerland
| | - Philippe Büchler
- Computational Bioengineering Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Stefan Schmid
- Spinal Movement Biomechanics Group, School of Health Professions, Bern University of Applied Sciences, Bern, Switzerland; Faculty of Medicine, University of Basel, Basel, Switzerland.
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Johnson GW, Chanbour H, Ali MA, Chen J, Metcalf T, Doss D, Younus I, Jonzzon S, Roth SG, Abtahi AM, Stephens BF, Zuckerman SL. Artificial Intelligence to Preoperatively Predict Proximal Junction Kyphosis Following Adult Spinal Deformity Surgery: Soft Tissue Imaging May Be Necessary for Accurate Models. Spine (Phila Pa 1976) 2023; 48:1688-1695. [PMID: 37644737 PMCID: PMC11101214 DOI: 10.1097/brs.0000000000004816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
STUDY DESIGN Retrospective cohort. OBJECTIVE In a cohort of patients undergoing adult spinal deformity (ASD) surgery, we used artificial intelligence to compare three models of preoperatively predicting radiographic proximal junction kyphosis (PJK) using: (1) traditional demographics and radiographic measurements, (2) raw preoperative scoliosis radiographs, and (3) raw preoperative thoracic magnetic resonance imaging (MRI). SUMMARY OF BACKGROUND DATA Despite many proposed risk factors, PJK following ASD surgery remains difficult to predict. MATERIALS AND METHODS A single-institution, retrospective cohort study was undertaken for patients undergoing ASD surgery from 2009 to 2021. PJK was defined as a sagittal Cobb angle of upper-instrumented vertebra (UIV) and UIV+2>10° and a postoperative change in UIV/UIV+2>10°. For model 1, a support vector machine was used to predict PJK within 2 years postoperatively using clinical and traditional sagittal/coronal radiographic variables and intended levels of instrumentation. Next, for model 2, a convolutional neural network (CNN) was trained on raw preoperative lateral and posterior-anterior scoliosis radiographs. Finally, for model 3, a CNN was trained on raw preoperative thoracic T1 MRIs. RESULTS A total of 191 patients underwent ASD surgery with at least 2-year follow-up and 89 (46.6%) developed radiographic PJK within 2 years. Model 1: Using clinical variables and traditional radiographic measurements, the model achieved a sensitivity: 57.2% and a specificity: 56.3%. Model 2: a CNN with raw scoliosis x-rays predicted PJK with a sensitivity: 68.2% and specificity: 58.3%. Model 3: a CNN with raw thoracic MRIs predicted PJK with average sensitivity: 73.1% and specificity: 79.5%. Finally, an attention map outlined the imaging features used by model 3 elucidated that soft tissue features predominated all true positive PJK predictions. CONCLUSIONS The use of raw MRIs in an artificial intelligence model improved the accuracy of PJK prediction compared with raw scoliosis radiographs and traditional clinical/radiographic measurements. The improved predictive accuracy using MRI may indicate that PJK is best predicted by soft tissue degeneration and muscle atrophy.
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Affiliation(s)
| | - Hani Chanbour
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Mir Amaan Ali
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Jeffrey Chen
- Vanderbilt University School of Medicine, Nashville, TN
| | - Tyler Metcalf
- Department of Orthopedic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Derek Doss
- Vanderbilt University School of Medicine, Nashville, TN
| | - Iyan Younus
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Soren Jonzzon
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Steven G. Roth
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Amir M. Abtahi
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
- Department of Orthopedic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Byron F. Stephens
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
- Department of Orthopedic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Scott L. Zuckerman
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
- Department of Orthopedic Surgery, Vanderbilt University Medical Center, Nashville, TN
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20
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Wong J, Reformat M, Lou E. Applying Machine Learning and Point-Set Registration to Automatically Measure the Severity of Spinal Curvature on Radiographs. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 12:151-161. [PMID: 38089001 PMCID: PMC10712667 DOI: 10.1109/jtehm.2023.3332618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/21/2023] [Accepted: 11/07/2023] [Indexed: 12/18/2023]
Abstract
OBJECTIVE Measuring the severity of the lateral spinal curvature, or Cobb angle, is critical for monitoring and making treatment decisions for children with adolescent idiopathic scoliosis (AIS). However, manual measurement is time-consuming and subject to human error. Therefore, clinicians seek an automated measurement method to streamline workflow and improve accuracy. This paper reports on a novel machine learning algorithm of cascaded convolutional neural networks (CNN) to measure the Cobb angle on spinal radiographs automatically. METHODS The developed method consisted of spinal column segmentation using a CNN, vertebra localization and segmentation using iterative vertebra body location coupled with another CNN, point-set registration to correct vertebra segmentations, and Cobb angle measurement using the final segmentations. Measurement performance was evaluated with the circular mean absolute error (CMAE) and percentage within clinical acceptance ([Formula: see text]) between automatic and manual measurements. Analysis was separated by curve severity to identify any potential systematic biases using independent samples Student's t-tests. RESULTS The method detected 346 of the 352 manually measured Cobb angles (98%), with a CMAE of 2.8° and 91% of measurements within the 5° clinical acceptance. No statistically significant differences were found between the CMAEs of mild ([Formula: see text]), moderate (25°-45°), and severe ([Formula: see text]) groups. The average measurement time per radiograph was 17.7±10.2s, improving upon the estimated average of 30s it takes an experienced rater to measure. Additionally, the algorithm outputs segmentations with the measurement, allowing clinicians to interpret measurement results. DISCUSSION/CONCLUSION The developed method measured Cobb angles on radiographs automatically with high accuracy, quick measurement time, and interpretability, suggesting clinical feasibility.
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Affiliation(s)
- Jason Wong
- Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonABT6G 1H9Canada
| | - Marek Reformat
- Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonABT6G 1H9Canada
| | - Edmond Lou
- Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonABT6G 1H9Canada
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21
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Niemeyer F, Galbusera F, Tao Y, Phillips FM, An HS, Louie PK, Samartzis D, Wilke HJ. Deep phenotyping the cervical spine: automatic characterization of cervical degenerative phenotypes based on T2-weighted MRI. 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 2023; 32:3846-3856. [PMID: 37644278 DOI: 10.1007/s00586-023-07909-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 04/17/2023] [Accepted: 08/17/2023] [Indexed: 08/31/2023]
Abstract
PURPOSE Radiological degenerative phenotypes provide insight into a patient's overall extent of disease and can be predictive for future pathological developments as well as surgical outcomes and complications. The objective of this study was to develop a reliable method for automatically classifying sagittal MRI image stacks of cervical spinal segments with respect to these degenerative phenotypes. METHODS We manually evaluated sagittal image data of the cervical spine of 873 patients (5182 motion segments) with respect to 5 radiological phenotypes. We then used this data set as ground truth for training a range of multi-class multi-label deep learning-based models to classify each motion segment automatically, on which we then performed hyper-parameter optimization. RESULTS The ground truth evaluations turned out to be relatively balanced for the labels disc displacement posterior, osteophyte anterior superior, osteophyte posterior superior, and osteophyte posterior inferior. Although we could not identify a single model that worked equally well across all the labels, the 3D-convolutional approach turned out to be preferable for classifying all labels. CONCLUSIONS Class imbalance in the training data and label noise made it difficult to achieve high predictive power for underrepresented classes. This shortcoming will be mitigated in the future versions by extending the training data set accordingly. Nevertheless, the classification performance rivals and in some cases surpasses that of human raters, while speeding up the evaluation process to only require a few seconds.
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Affiliation(s)
- Frank Niemeyer
- Institute for Orthopaedic Research and Biomechanics, Trauma Research Center Ulm, University Hospital Ulm, Ulm, Germany
| | - Fabio Galbusera
- Department of Teaching, Research and Development, Schulthess Clinic, Spine Center, Lengghalde 2, 8008, Zurich, Switzerland.
| | - Youping Tao
- Institute for Orthopaedic Research and Biomechanics, Trauma Research Center Ulm, University Hospital Ulm, Ulm, Germany
| | - Frank M Phillips
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Howard S An
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Philip K Louie
- Spine Clinic, Virginia Mason Medical Center, Seattle, WA, USA
| | - Dino Samartzis
- International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Hans-Joachim Wilke
- Institute for Orthopaedic Research and Biomechanics, Trauma Research Center Ulm, University Hospital Ulm, Ulm, Germany
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22
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Nguyen TP, Kim JH, Kim SH, Yoon J, Choi SH. Machine Learning-Based Measurement of Regional and Global Spinal Parameters Using the Concept of Incidence Angle of Inflection Points. Bioengineering (Basel) 2023; 10:1236. [PMID: 37892966 PMCID: PMC10604057 DOI: 10.3390/bioengineering10101236] [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: 09/12/2023] [Revised: 10/05/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023] Open
Abstract
This study delves into the application of convolutional neural networks (CNNs) in evaluating spinal sagittal alignment, introducing the innovative concept of incidence angles of inflection points (IAIPs) as intuitive parameters to capture the interplay between pelvic and spinal alignment. Pioneering the fusion of IAIPs with machine learning for sagittal alignment analysis, this research scrutinized whole-spine lateral radiographs from hundreds of patients who visited a single institution, utilizing high-quality images for parameter assessments. Noteworthy findings revealed robust success rates for certain parameters, including pelvic and C2 incidence angles, but comparatively lower rates for sacral slope and L1 incidence. The proposed CNN-based machine learning method demonstrated remarkable efficiency, achieving an impressive 80 percent detection rate for various spinal angles, such as lumbar lordosis and thoracic kyphosis, with a precise error threshold of 3.5°. Further bolstering the study's credibility, measurements derived from the novel formula closely aligned with those directly extracted from the CNN model. In conclusion, this research underscores the utility of the CNN-based deep learning algorithm in delivering precise measurements of spinal sagittal parameters, and highlights the potential for integrating machine learning with the IAIP concept for comprehensive data accumulation in the domain of sagittal spinal alignment analysis, thus advancing our understanding of spinal health.
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Affiliation(s)
- Thong Phi Nguyen
- Department of Mechanical Engineering, BK21 FOUR ERICA-ACE Center, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea
- Department of Mechanical Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea
| | - Ji-Hwan Kim
- Department of Orthopedic Surgery, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Seong-Ha Kim
- Department of Orthopedic Surgery, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Jonghun Yoon
- Department of Mechanical Engineering, BK21 FOUR ERICA-ACE Center, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea
- Department of Mechanical Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea
- AIDICOME Inc., 221, 5th Engineering Building, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea
| | - Sung-Hoon Choi
- Department of Orthopedic Surgery, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
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23
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Song SY, Seo MS, Kim CW, Kim YH, Yoo BC, Choi HJ, Seo SH, Kang SW, Song MG, Nam DC, Kim DH. AI-Driven Segmentation and Automated Analysis of the Whole Sagittal Spine from X-ray Images for Spinopelvic Parameter Evaluation. Bioengineering (Basel) 2023; 10:1229. [PMID: 37892959 PMCID: PMC10604000 DOI: 10.3390/bioengineering10101229] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/15/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023] Open
Abstract
Spinal-pelvic parameters are utilized in orthopedics for assessing patients' curvature and body alignment in diagnosing, treating, and planning surgeries for spinal and pelvic disorders. Segmenting and autodetecting the whole spine from lateral radiographs is challenging. Recent efforts have employed deep learning techniques to automate the segmentation and analysis of whole-spine lateral radiographs. This study aims to develop an artificial intelligence (AI)-based deep learning approach for the automated segmentation, alignment, and measurement of spinal-pelvic parameters through whole-spine lateral radiographs. We conducted the study on 932 annotated images from various spinal pathologies. Using a deep learning (DL) model, anatomical landmarks of the cervical, thoracic, lumbar vertebrae, sacrum, and femoral head were automatically distinguished. The algorithm was designed to measure 13 radiographic alignment and spinal-pelvic parameters from the whole-spine lateral radiographs. Training data comprised 748 digital radiographic (DR) X-ray images, while 90 X-ray images were used for validation. Another set of 90 X-ray images served as the test set. Inter-rater reliability between orthopedic spine specialists, orthopedic residents, and the DL model was evaluated using the intraclass correlation coefficient (ICC). The segmentation accuracy for anatomical landmarks was within an acceptable range (median error: 1.7-4.1 mm). The inter-rater reliability between the proposed DL model and individual experts was fair to good for measurements of spinal curvature characteristics (all ICC values > 0.62). The developed DL model in this study demonstrated good levels of inter-rater reliability for predicting anatomical landmark positions and measuring radiographic alignment and spinal-pelvic parameters. Automated segmentation and analysis of whole-spine lateral radiographs using deep learning offers a promising tool to enhance accuracy and efficiency in orthopedic diagnostics and treatments.
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Affiliation(s)
- Sang-Youn Song
- Department of Orthopaedic Surgery, Institute of Medical Science, Gyeongsang National University Hospital and Gyeongsang National University School of Medicine, Jinju 52727, Republic of Korea; (S.-Y.S.); (M.-S.S.); (C.-W.K.)
| | - Min-Seok Seo
- Department of Orthopaedic Surgery, Institute of Medical Science, Gyeongsang National University Hospital and Gyeongsang National University School of Medicine, Jinju 52727, Republic of Korea; (S.-Y.S.); (M.-S.S.); (C.-W.K.)
| | - Chang-Won Kim
- Department of Orthopaedic Surgery, Institute of Medical Science, Gyeongsang National University Hospital and Gyeongsang National University School of Medicine, Jinju 52727, Republic of Korea; (S.-Y.S.); (M.-S.S.); (C.-W.K.)
| | - Yun-Heung Kim
- Deepnoid. Inc., Seoul 08376, Republic of Korea; (Y.-H.K.); (B.-C.Y.); (H.-J.C.)
| | - Byeong-Cheol Yoo
- Deepnoid. Inc., Seoul 08376, Republic of Korea; (Y.-H.K.); (B.-C.Y.); (H.-J.C.)
| | - Hyun-Ju Choi
- Deepnoid. Inc., Seoul 08376, Republic of Korea; (Y.-H.K.); (B.-C.Y.); (H.-J.C.)
| | - Sung-Hyo Seo
- Department of Biomedical Research Institute, Gyeongsang National University Hospital, Jinju 52727, Republic of Korea;
| | - Sung-Wook Kang
- Precision Mechanical Process and Control R&D Group, Korea Institute of Industrial Technology, Seoul 06211, Republic of Korea;
| | - Myung-Geun Song
- Department of Orthopaedic Surgery, College of Medicine, Inha University Hospital, Incheon 22212, Republic of Korea;
| | - Dae-Cheol Nam
- Department of Orthopaedic Surgery, Institute of Medical Science, Gyeongsang National University Hospital and Gyeongsang National University School of Medicine, Jinju 52727, Republic of Korea; (S.-Y.S.); (M.-S.S.); (C.-W.K.)
| | - Dong-Hee Kim
- Department of Orthopaedic Surgery, Institute of Medical Science, Gyeongsang National University Hospital and Gyeongsang National University School of Medicine, Jinju 52727, Republic of Korea; (S.-Y.S.); (M.-S.S.); (C.-W.K.)
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Nakarai H, Cina A, Jutzeler C, Grob A, Haschtmann D, Loibl M, Fekete TF, Kleinstück F, Wilke HJ, Tao Y, Galbusera F. Automatic Calculation of Cervical Spine Parameters Using Deep Learning: Development and Validation on an External Dataset. Global Spine J 2023:21925682231205352. [PMID: 37811580 DOI: 10.1177/21925682231205352] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/10/2023] Open
Abstract
STUDY DESIGN Retrospective data analysis. OBJECTIVES This study aims to develop a deep learning model for the automatic calculation of some important spine parameters from lateral cervical radiographs. METHODS We collected two datasets from two different institutions. The first dataset of 1498 images was used to train and optimize the model to find the best hyperparameters while the second dataset of 79 images was used as an external validation set to evaluate the robustness and generalizability of our model. The performance of the model was assessed by calculating the median absolute errors between the model prediction and the ground truth for the following parameters: T1 slope, C7 slope, C2-C7 angle, C2-C6 angle, Sagittal Vertical Axis (SVA), C0-C2, Redlund-Johnell distance (RJD), the cranial tilting (CT) and the craniocervical angle (CCA). RESULTS Regarding the angles, we found median errors of 1.66° (SD 2.46°), 1.56° (1.95°), 2.46° (SD 2.55), 1.85° (SD 3.93°), 1.25° (SD 1.83°), .29° (SD .31°) and .67° (SD .77°) for T1 slope, C7 slope, C2-C7, C2-C6, C0-C2, CT, and CCA respectively. As concerns the distances, we found median errors of .55 mm (SD .47 mm) and .47 mm (.62 mm) for SVA and RJD respectively. CONCLUSIONS In this work, we developed a model that was able to accurately predict cervical spine parameters from lateral cervical radiographs. In particular, the performances on the external validation set demonstrate the robustness and the high degree of generalizability of our model on images acquired in a different institution.
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Affiliation(s)
- Hiroyuki Nakarai
- Department of Spine Surgery and Neurosurgery, Schulthess Klinik, Zürich, Switzerland
- Department of Spine Surgery, Hospital for Special Surgery, New York, US
- Spine Group (UTSG), The University of Tokyo, Bunkyo-ku, Japan
| | - Andrea Cina
- Department of Health Sciences and Technologies, ETH Zürich, Zürich, Switzerland
- Department of Teaching, Research and Development, Schulthess Klinik, Zürich, Switzerland
| | - Catherine Jutzeler
- Department of Health Sciences and Technologies, ETH Zürich, Zürich, Switzerland
| | - Alexandra Grob
- Department of Spine Surgery and Neurosurgery, Schulthess Klinik, Zürich, Switzerland
- Department of Neurosurgery, University Hospital Zürich, Zürich, Switzerland
| | - Daniel Haschtmann
- Department of Spine Surgery and Neurosurgery, Schulthess Klinik, Zürich, Switzerland
| | - Markus Loibl
- Department of Spine Surgery and Neurosurgery, Schulthess Klinik, Zürich, Switzerland
| | - Tamas F Fekete
- Department of Spine Surgery and Neurosurgery, Schulthess Klinik, Zürich, Switzerland
| | - Frank Kleinstück
- Department of Spine Surgery and Neurosurgery, Schulthess Klinik, Zürich, Switzerland
| | - Hans-Joachim Wilke
- Institute of Orthopaedic Research and Biomechanics, Centre for Trauma Research, Ulm University, Ulm, Germany
| | - Youping Tao
- Institute of Orthopaedic Research and Biomechanics, Centre for Trauma Research, Ulm University, Ulm, Germany
| | - Fabio Galbusera
- Department of Teaching, Research and Development, Schulthess Klinik, Zürich, Switzerland
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Wu Y, Chen X, Dong F, He L, Cheng G, Zheng Y, Ma C, Yao H, Zhou S. Performance evaluation of a deep learning-based cascaded HRNet model for automatic measurement of X-ray imaging parameters of lumbar sagittal curvature. 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 2023:10.1007/s00586-023-07937-5. [PMID: 37787781 DOI: 10.1007/s00586-023-07937-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 04/03/2023] [Accepted: 08/30/2023] [Indexed: 10/04/2023]
Abstract
PURPOSE To develop a deep learning-based cascaded HRNet model, in order to automatically measure X-ray imaging parameters of lumbar sagittal curvature and to evaluate its prediction performance. METHODS A total of 3730 lumbar lateral digital radiography (DR) images were collected from picture archiving and communication system (PACS). Among them, 3150 images were randomly selected as the training dataset and validation dataset, and 580 images as the test dataset. The landmarks of the lumbar curve index (LCI), lumbar lordosis angle (LLA), sacral slope (SS), lumbar lordosis index (LLI), and the posterior edge tangent angle of the vertebral body (PTA) were identified and marked. The measured results of landmarks on the test dataset were compared with the mean values of manual measurement as the reference standard. Percentage of correct key-points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root-mean-square error (RMSE), and Bland-Altman plot were used to evaluate the performance of the cascade HRNet model. RESULTS The PCK of the cascaded HRNet model was 97.9-100% in the 3 mm distance threshold. The mean differences between the reference standard and the predicted values for LCI, LLA, SS, LLI, and PTA were 0.43 mm, 0.99°, 1.11°, 0.01 mm, and 0.23°, respectively. There were strong correlation and consistency of the five parameters between the cascaded HRNet model and manual measurements (ICC = 0.989-0.999, R = 0.991-0.999, MAE = 0.63-1.65, MSE = 0.61-4.06, RMSE = 0.78-2.01). CONCLUSION The cascaded HRNet model based on deep learning algorithm could accurately identify the sagittal curvature-related landmarks on lateral lumbar DR images and automatically measure the relevant parameters, which is of great significance in clinical application.
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Affiliation(s)
- Yuhua Wu
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Xiaofei Chen
- Department of Radiology, Gansu Provincial Hospital of Traditional Chinese Medicine (The first affiliated hospital of Gansu University of Traditional Chinese Medicine), Lanzhou, 730050, Gansu, China
| | - Fuwen Dong
- Department of Radiology, Gansu Provincial Hospital of Traditional Chinese Medicine (The first affiliated hospital of Gansu University of Traditional Chinese Medicine), Lanzhou, 730050, Gansu, China
| | - Linyang He
- Hangzhou Jianpei Technology Company Ltd, Hangzhou, 311200, Zhejiang, China
| | - Guohua Cheng
- Hangzhou Jianpei Technology Company Ltd, Hangzhou, 311200, Zhejiang, China
| | - Yuwen Zheng
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Chunyu Ma
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Hongyan Yao
- Department of Radiology, Gansu Provincial Hospital, No. 204, Donggang West Road, Lanzhou, 730000, Gansu, China
| | - Sheng Zhou
- Department of Radiology, Gansu Provincial Hospital, No. 204, Donggang West Road, Lanzhou, 730000, Gansu, China.
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Kim YT, Jeong TS, Kim YJ, Kim WS, Kim KG, Yee GT. Automatic Spine Segmentation and Parameter Measurement for Radiological Analysis of Whole-Spine Lateral Radiographs Using Deep Learning and Computer Vision. J Digit Imaging 2023; 36:1447-1459. [PMID: 37131065 PMCID: PMC10406753 DOI: 10.1007/s10278-023-00830-z] [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: 01/26/2023] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 05/04/2023] Open
Abstract
Radiographic examination is essential for diagnosing spinal disorders, and the measurement of spino-pelvic parameters provides important information for the diagnosis and treatment planning of spinal sagittal deformities. While manual measurement methods are the golden standard for measuring parameters, they can be time consuming, inefficient, and rater dependent. Previous studies that have used automatic measurement methods to alleviate the downsides of manual measurements showed low accuracy or could not be applied to general films. We propose a pipeline for automated measurement of spinal parameters by combining a Mask R-CNN model for spine segmentation with computer vision algorithms. This pipeline can be incorporated into clinical workflows to provide clinical utility in diagnosis and treatment planning. A total of 1807 lateral radiographs were used for the training (n = 1607) and validation (n = 200) of the spine segmentation model. An additional 200 radiographs, which were also used for validation, were examined by three surgeons to evaluate the performance of the pipeline. Parameters automatically measured by the algorithm in the test set were statistically compared to parameters measured manually by the three surgeons. The Mask R-CNN model achieved an average precision at 50% intersection over union (AP50) of 96.2% and a Dice score of 92.6% for the spine segmentation task in the test set. The mean absolute error values of the spino-pelvic parameters measurement results were within the range of 0.4° (pelvic tilt) to 3.0° (lumbar lordosis, pelvic incidence), and the standard error of estimate was within the range of 0.5° (pelvic tilt) to 4.0° (pelvic incidence). The intraclass correlation coefficient values ranged from 0.86 (sacral slope) to 0.99 (pelvic tilt, sagittal vertical axis).
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Affiliation(s)
- Yong-Tae Kim
- Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea
| | - Tae Seok Jeong
- Department of Neurosurgery, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea
| | - Young Jae Kim
- Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea
| | - Woo Seok Kim
- Department of Traumatology, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea
| | - Kwang Gi Kim
- Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.
| | - Gi Taek Yee
- Department of Neurosurgery, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.
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Wong JC, Reformat MZ, Parent EC, Stampe KP, Southon Hryniuk SC, Lou EH. Validation of an artificial intelligence-based method to automate Cobb angle measurement on spinal radiographs of children with adolescent idiopathic scoliosis. Eur J Phys Rehabil Med 2023; 59:535-542. [PMID: 37746786 PMCID: PMC10548476 DOI: 10.23736/s1973-9087.23.08091-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/09/2023] [Accepted: 09/07/2023] [Indexed: 09/26/2023]
Abstract
BACKGROUND Accurately measuring the Cobb angle on radiographs is crucial for diagnosis and treatment decisions for adolescent idiopathic scoliosis (AIS). However, manual Cobb angle measurement is time-consuming and subject to measurement variation, especially for inexperienced clinicians. AIM This study aimed to validate a novel artificial-intelligence-based (AI) algorithm that automatically measures the Cobb angle on radiographs. DESIGN This is a retrospective cross-sectional study. SETTING The population of patients attended the Stollery Children's Hospital in Alberta, Canada. POPULATION Children who: 1) were diagnosed with AIS, 2) were aged between 10 and 18 years old, 3) had no prior surgery, and 4) had a radiograph out of brace, were enrolled. METHODS A total of 330 spinal radiographs were used. Among those, 130 were used for AI model development and 200 were used for measurement validation. Automatic Cobb angle measurements were validated by comparing them with manual ones measured by a rater with 20+ years of experience. Analysis was performed using the standard error of measurement (SEM), inter-method intraclass correlation coefficient (ICC2,1), and percentage of measurements within clinical acceptance (≤5°). Subgroup analysis was conducted by severity, region, and X-ray system to identify any systematic biases. RESULTS The AI method detected 346 of 352 manually measured curves (mean±standard deviation: 24.7±9.5°), achieving 91% (316/346) of measurements within clinical acceptance. Excellent reliability was obtained with 0.92 ICC and 0.79° SEM. Comparable performance was found throughout all subgroups, and no systematic biases in performance affecting any subgroup were discovered. The algorithm measured each radiograph approximately 18s on average which is slightly faster than the estimated measurement time of an experienced rater. Radiographs taken by the EOS X-ray system were measured more quickly on average than those taken by a conventional digital X-ray system (10s vs. 26s). CONCLUSIONS An AI-based algorithm was developed to measure the Cobb angle automatically on radiographs and yielded reliable measurements quickly. The algorithm provides detailed images on how the angles were measured, providing interpretability that can give clinicians confidence in the measurements. CLINICAL REHABILITATION IMPACT Employing the algorithm in practice could streamline clinical workflow and optimize measurement accuracy and speed in order to inform AIS treatment decisions.
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Affiliation(s)
- Jason C Wong
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Marek Z Reformat
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Eric C Parent
- Department of Physical Therapy, University of Alberta, Edmonton, Canada
| | - Kyle P Stampe
- Department of Surgery, University of Alberta, Edmonton, Canada
| | | | - Edmond H Lou
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada -
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Bassani T, Cina A, Galbusera F, Sconfienza LM, Albano D, Barcellona F, Colombini A, Luca A, Brayda-Bruno M. Automatic classification of the vertebral endplate lesions in magnetic resonance imaging by deep learning model. Front Surg 2023; 10:1172313. [PMID: 37425349 PMCID: PMC10324976 DOI: 10.3389/fsurg.2023.1172313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/08/2023] [Indexed: 07/11/2023] Open
Abstract
Introduction A novel classification scheme for endplate lesions, based on T2-weighted images from magnetic resonance imaging (MRI) scan, has been recently introduced and validated. The scheme categorizes intervertebral spaces as "normal," "wavy/irregular," "notched," and "Schmorl's node." These lesions have been associated with spinal pathologies, including disc degeneration and low back pain. The exploitation of an automatic tool for the detection of the lesions would facilitate clinical practice by reducing the workload and the diagnosis time. The present work exploits a deep learning application based on convolutional neural networks to automatically classify the type of lesion. Methods T2-weighted MRI scans of the sagittal lumbosacral spine of consecutive patients were retrospectively collected. The middle slice of each scan was manually processed to identify the intervertebral spaces from L1L2 to L5S1, and the corresponding lesion type was labeled. A total of 1,559 gradable discs were obtained, with the following types of distribution: "normal" (567 discs), "wavy/irregular" (485), "notched" (362), and "Schmorl's node" (145). The dataset was divided randomly into a training set and a validation set while preserving the original distribution of lesion types in each set. A pretrained network for image classification was utilized, and fine-tuning was performed using the training set. The retrained net was then applied to the validation set to evaluate the overall accuracy and accuracy for each specific lesion type. Results The overall rate of accuracy was found equal to 88%. The accuracy for the specific lesion type was found as follows: 91% (normal), 82% (wavy/irregular), 93% (notched), and 83% (Schmorl's node). Discussion The results indicate that the deep learning approach achieved high accuracy for both overall classification and individual lesion types. In clinical applications, this implementation could be employed as part of an automatic detection tool for pathological conditions characterized by the presence of endplate lesions, such as spinal osteochondrosis.
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Affiliation(s)
- Tito Bassani
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Andrea Cina
- Spine Center, Schulthess Clinic, Zurich, Switzerland
- Department of Health Sciences and Technologies, ETH Zurich, Zurich, Switzerland
| | | | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento di Scienze Biomediche per la Salute, Università Degli Studi di Milano, Milan, Italy
| | | | - Federica Barcellona
- Complex Unit of Radiology, Department of Diagnostic and Interventional Radiology, Azienda Socio Sanitaria Territoriale (ASST) Lodi, Lodi, Italy
| | | | - Andrea Luca
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
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Martín-Noguerol T, Oñate Miranda M, Amrhein TJ, Paulano-Godino F, Xiberta P, Vilanova JC, Luna A. The role of Artificial intelligence in the assessment of the spine and spinal cord. Eur J Radiol 2023; 161:110726. [PMID: 36758280 DOI: 10.1016/j.ejrad.2023.110726] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 01/13/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI) application development is underway in all areas of radiology where many promising tools are focused on the spine and spinal cord. In the past decade, multiple spine AI algorithms have been created based on radiographs, computed tomography, and magnetic resonance imaging. These algorithms have wide-ranging purposes including automatic labeling of vertebral levels, automated description of disc degenerative changes, detection and classification of spine trauma, identification of osseous lesions, and the assessment of cord pathology. The overarching goals for these algorithms include improved patient throughput, reducing radiologist workload burden, and improving diagnostic accuracy. There are several pre-requisite tasks required in order to achieve these goals, such as automatic image segmentation, facilitating image acquisition and postprocessing. In this narrative review, we discuss some of the important imaging AI solutions that have been developed for the assessment of the spine and spinal cord. We focus on their practical applications and briefly discuss some key requirements for the successful integration of these tools into practice. The potential impact of AI in the imaging assessment of the spine and cord is vast and promises to provide broad reaching improvements for clinicians, radiologists, and patients alike.
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Affiliation(s)
| | - Marta Oñate Miranda
- Department of Radiology, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec, Canada.
| | - Timothy J Amrhein
- Department of Radiology, Duke University Medical Center, Durham, USA.
| | | | - Pau Xiberta
- Graphics and Imaging Laboratory (GILAB), University of Girona, 17003 Girona, Spain.
| | - Joan C Vilanova
- Department of Radiology. Clinica Girona, Diagnostic Imaging Institute (IDI), University of Girona, 17002 Girona, Spain.
| | - Antonio Luna
- MRI unit, Radiology department. HT medica, Carmelo Torres n°2, 23007 Jaén, Spain.
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30
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Chen K, Zhai X, Wang S, Li X, Lu Z, Xia D, Li M. Emerging trends and research foci of deep learning in spine: bibliometric and visualization study. Neurosurg Rev 2023; 46:81. [PMID: 37000304 DOI: 10.1007/s10143-023-01987-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/10/2023] [Accepted: 03/26/2023] [Indexed: 04/01/2023]
Abstract
As the cognition of spine develops, deep learning (DL) emerges as a powerful tool with tremendous potential for advancing research in this field. To provide a comprehensive overview of DL-spine research, our study utilized bibliometric and visual methods to retrieve relevant articles from the Web of Science database. VOSviewer and CiteSpace were primarily used for literature measurement and knowledge graph analysis. A total of 273 studies focusing on deep learning in the spine, with a combined total of 2302 citations, were retrieved. Additionally, the overall number of articles published on this topic demonstrated a continuous upward trend. China was the country with the highest number of publications, whereas the USA had the most citations. The two most prominent journals were "European Spine Journal" and "Medical Image Analysis," and the most involved research area was Radiology Nuclear Medicine Medical Imaging. VOSviewer identified three visually distinct clusters: "segmentation," "area," and "neural network." Meanwhile, CiteSpace highlighted "magnetic resonance image" and "lumbar" as the keywords with the longest usage, and "agreement" and "automated detection" as the most commonly used keywords. Although the application of DL in spine is still in its infancy, its future is promising. Intercontinental cooperation, extensive application, and more interpretable algorithms will invigorate DL in the field of spine.
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Affiliation(s)
- Kai Chen
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China
| | - Xiao Zhai
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China
| | - Sheng Wang
- Department of Emergency, Shanghai Changhai Hospital, Shanghai, China
| | - Xiaoyu Li
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China
| | - Zhikai Lu
- Department of Orthopedics, No. 906 Hospital of Joint Logistic Support Force of PLA, Ningbo, Zhejiang, China.
| | - Demeng Xia
- Luodian Clinical Drug Research Center, Shanghai Baoshan Luodian Hospital, Shanghai University, Shanghai, China.
- Emergency Department, Naval Hospital of Eastern Theater, Zhoushan, Zhejiang, China.
| | - Ming Li
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China.
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Zerouali M, Parpaleix A, Benbakoura M, Rigault C, Champsaur P, Guenoun D. Automatic deep learning-based assessment of spinopelvic coronal and sagittal alignment. Diagn Interv Imaging 2023:S2211-5684(23)00051-7. [PMID: 36959006 DOI: 10.1016/j.diii.2023.03.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/28/2023] [Accepted: 03/07/2023] [Indexed: 03/25/2023]
Abstract
PURPOSE The purpose of this study was to evaluate an artificial intelligence (AI) solution for estimating coronal and sagittal spinopelvic alignment on conventional uniplanar two-dimensional whole-spine radiograph. MATERIAL AND METHODS This retrospective observational study included 100 patients (35 men, 65 women) with a median age of 14 years (IQR: 13, 15.25; age range: 3-64 years) who underwent conventional uniplanar two-dimensional whole-spine radiograph in standing position between January and July 2022. Ten most commonly used spinopelvic coronal and sagittal parameters were retrospectively measured without AI by a junior radiologist and approved or adjusted by a senior musculoskeletal radiologist to reach final measurements. Final measurements were used as the ground truth to assess AI performance for each parameter. AI performances were estimated using mean absolute errors (MAE), intraclass correlation coefficient (ICCs), and accuracy for selected clinically relevant thresholds. Readers visually classified AI outputs to assess reliability at a patient-level. RESULTS AI solution showed excellent consistency without bias in coronal (ICCs ≥ 0.95; MAE ≤ 2.9° or 1.97 mm) and sagittal (ICCs ≥ 0.85; MAE ≤ 4.4° or 2.7 mm) spinopelvic evaluation, except for kyphosis (ICC = 0.58; MAE = 8.7°). AI accuracy to classify low Cobb angle, severe scoliosis or frontal pelvic asymmetry was 91% (95% CI: 85-96), 99% (95% CI: 97-100) and 94% (95% CI: 89-98), respectively. Overall, AI provided reliable measurements in 72/100 patients (72%). CONCLUSION The AI solution used in this study for combined coronal and sagittal spinopelvic balance assessment provides results consistent with those of a senior musculoskeletal radiologist, and shows potential benefit for reducing workload in future routine implementation.
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Affiliation(s)
- Mohamed Zerouali
- Department of Radiology, Institute for Locomotion, Sainte-Marguerite Hospital, APHM, 13009 Marseille, France
| | | | | | | | - Pierre Champsaur
- Department of Radiology, Institute for Locomotion, Sainte-Marguerite Hospital, APHM, 13009 Marseille, France; Institute of Movement Sciences (ISM), CNRS, Aix Marseille University, 13005 Marseille, France
| | - Daphné Guenoun
- Department of Radiology, Institute for Locomotion, Sainte-Marguerite Hospital, APHM, 13009 Marseille, France; Institute of Movement Sciences (ISM), CNRS, Aix Marseille University, 13005 Marseille, France.
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Charles YP, Lamas V, Ntilikina Y. Artificial intelligence and treatment algorithms in spine surgery. Orthop Traumatol Surg Res 2023; 109:103456. [PMID: 36302452 DOI: 10.1016/j.otsr.2022.103456] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 05/12/2022] [Accepted: 05/25/2022] [Indexed: 12/24/2022]
Abstract
Artificial intelligence (AI) is a set of theories and techniques in which machines are used to simulate human intelligence with complex computer programs. The various machine learning (ML) methods are a subtype of AI. They originate from computer science and use algorithms established from analyzing a database to accomplish certain tasks. Among these methods are decision trees or random forests, support vector machines along with artificial neural networks. Convolutive neural networks were inspired from the visual cortex; they process combinations of information used in image or voice recognition. Deep learning (DL) groups together a set of ML methods and is useful for modeling complex relationships with a high degree of abstraction by using multiple layers of artificial neurons. ML techniques have a growing role in spine surgery. The main applications are the segmentation of intraoperative images for surgical navigation or robotics used for pedicle screw placement, the interpretation of images of intervertebral discs or full spine radiographs, which can be automated using ML algorithms. ML techniques can also be used as aids for surgical decision-making in complex fields, such as preoperative evaluation of adult spinal deformity. ML algorithms "learn" from large clinical databases. They make it possible to establish the intraoperative risk level and make a prognosis on how the postoperative functional scores will change over time as a function of the patient profile. These applications open a new path relative to standard statistical analyses. They make it possible to explore more complex relationships with multiple indirect interactions. In the future, AI algorithms could have a greater role in clinical research, evaluating clinical and surgical practices, and conducting health economics analyses.
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Affiliation(s)
- Yann Philippe Charles
- Service de chirurgie du rachis, hôpitaux universitaires de Strasbourg, université de Strasbourg, 1, avenue Molière, 67200 Strasbourg, France.
| | - Vincent Lamas
- Service de chirurgie du rachis, hôpitaux universitaires de Strasbourg, université de Strasbourg, 1, avenue Molière, 67200 Strasbourg, France
| | - Yves Ntilikina
- Service de chirurgie du rachis, hôpitaux universitaires de Strasbourg, université de Strasbourg, 1, avenue Molière, 67200 Strasbourg, France
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Geng EA, Cho BH, Valliani AA, Arvind V, Patel AV, Cho SK, Kim JS, Cagle PJ. Development of a machine learning algorithm to identify total and reverse shoulder arthroplasty implants from X-ray images. J Orthop 2023; 35:74-78. [PMID: 36411845 PMCID: PMC9674869 DOI: 10.1016/j.jor.2022.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/16/2022] [Accepted: 11/07/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Demand for total shoulder arthroplasty (TSA) has risen significantly and is projected to continue growing. From 2012 to 2017, the incidence of reverse total shoulder arthroplasty (rTSA) rose from 7.3 cases per 100,000 to 19.3 per 100,000. Anatomical TSA saw a growth from 9.5 cases per 100,000 to 12.5 per 100,000. Failure to identify implants in a timely manner can increase operative time, cost and risk of complications. Several machine learning models have been developed to perform medical image analysis. However, they have not been widely applied in shoulder surgery. The authors developed a machine learning model to identify shoulder implant manufacturers and type from anterior-posterior X-ray images. Methods The model deployed was a convolutional neural network (CNN), which has been widely used in computer vision tasks. 696 radiographs were obtained from a single institution. 70% were used to train the model, while evaluation was done on 30%. Results On the evaluation set, the model performed with an overall accuracy of 93.9% with positive predictive value, sensitivity and F-1 scores of 94% across 10 different implant types (4 reverse, 6 anatomical). Average identification time was 0.110 s per implant. Conclusion This proof of concept study demonstrates that machine learning can assist with preoperative planning and improve cost-efficiency in shoulder surgery.
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Affiliation(s)
- Eric A. Geng
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
| | - Brian H. Cho
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
| | - Aly A. Valliani
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
| | - Varun Arvind
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
| | - Akshar V. Patel
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
| | - Samuel K. Cho
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
| | - Jun S. Kim
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
| | - Paul J. Cagle
- Department of Orthopaedic Surgery, Mount Sinai Health System, New York, NY, 10029, USA
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Orosz LD, Bhatt FR, Jazini E, Dreischarf M, Grover P, Grigorian J, Roy R, Schuler TC, Good CR, Haines CM. Novel artificial intelligence algorithm: an accurate and independent measure of spinopelvic parameters. J Neurosurg Spine 2022; 37:893-901. [PMID: 35901700 DOI: 10.3171/2022.5.spine22109] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 05/16/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The analysis of sagittal alignment by measuring spinopelvic parameters has been widely adopted among spine surgeons globally, and sagittal imbalance is a well-documented cause of poor quality of life. These measurements are time-consuming but necessary to make, which creates a growing need for an automated analysis tool that measures spinopelvic parameters with speed, precision, and reproducibility without relying on user input. This study introduces and evaluates an algorithm based on artificial intelligence (AI) that fully automatically measures spinopelvic parameters. METHODS Two hundred lateral lumbar radiographs (pre- and postoperative images from 100 patients undergoing lumbar fusion) were retrospectively analyzed by board-certified spine surgeons who digitally measured lumbar lordosis, pelvic incidence, pelvic tilt, and sacral slope. The novel AI algorithm was also used to measure the same parameters. To evaluate the agreement between human and AI-automated measurements, the mean error (95% CI, SD) was calculated and interrater reliability was assessed using the 2-way random single-measure intraclass correlation coefficient (ICC). ICC values larger than 0.75 were considered excellent. RESULTS The AI algorithm determined all parameters in 98% of preoperative and in 95% of postoperative images with excellent ICC values (preoperative range 0.85-0.92, postoperative range 0.81-0.87). The mean errors were smallest for pelvic incidence both pre- and postoperatively (preoperatively -0.5° [95% CI -1.5° to 0.6°] and postoperatively 0.0° [95% CI -1.1° to 1.2°]) and largest preoperatively for sacral slope (-2.2° [95% CI -3.0° to -1.5°]) and postoperatively for lumbar lordosis (3.8° [95% CI 2.5° to 5.0°]). CONCLUSIONS Advancements in AI translate to the arena of medical imaging analysis. This method of measuring spinopelvic parameters on spine radiographs has excellent reliability comparable to expert human raters. This application allows users to accurately obtain critical spinopelvic measurements automatically, which can be applied to clinical practice. This solution can assist physicians by saving time in routine work and by avoiding error-prone manual measurements.
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Affiliation(s)
- Lindsay D Orosz
- 1Department of Research, National Spine Health Foundation, Reston
| | - Fenil R Bhatt
- 2Department of Spine Surgery, Virginia Spine Institute, Reston, Virginia
| | - Ehsan Jazini
- 2Department of Spine Surgery, Virginia Spine Institute, Reston, Virginia
| | - Marcel Dreischarf
- 3Department of Research and Development, RAYLYTIC GmbH, Leipzig, Germany
| | - Priyanka Grover
- 3Department of Research and Development, RAYLYTIC GmbH, Leipzig, Germany
| | - Julia Grigorian
- 1Department of Research, National Spine Health Foundation, Reston
| | - Rita Roy
- 1Department of Research, National Spine Health Foundation, Reston
| | - Thomas C Schuler
- 2Department of Spine Surgery, Virginia Spine Institute, Reston, Virginia
| | - Christopher R Good
- 2Department of Spine Surgery, Virginia Spine Institute, Reston, Virginia
| | - Colin M Haines
- 2Department of Spine Surgery, Virginia Spine Institute, Reston, Virginia
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Current models to understand the onset and progression of scoliotic deformities in adolescent idiopathic scoliosis: a systematic review. Spine Deform 2022; 11:545-558. [PMID: 36454530 DOI: 10.1007/s43390-022-00618-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 11/12/2022] [Indexed: 12/05/2022]
Abstract
PURPOSE To create an updated and comprehensive overview of the modeling studies that have been done to understand the mechanics underlying deformities of adolescent idiopathic scoliosis (AIS), to predict the risk of curve progression and thereby substantiate etiopathogenetic theories. METHODS In this systematic review, an online search in Scopus and PubMed together with an analysis in secondary references was done, which yielded 86 studies. The modeling types were extracted and the studies were categorized accordingly. RESULTS Animal modeling, together with machine learning modeling, forms the category of black box models. This category is perceived as the most clinically relevant. While animal models provide a tangible idea of the biomechanical effects in scoliotic deformities, machine learning modeling was found to be the best curve-progression predictor. The second category, that of artificial models, has, just as animal modeling, a tangible model as a result, but focusses more on the biomechanical process of the scoliotic deformity. The third category is formed by computational models, which are very popular in etiopathogenetic parameter-based studies. They are also the best in calculating stresses and strains on vertebrae, intervertebral discs, and other surrounding tissues. CONCLUSION This study presents a comprehensive overview of the current modeling techniques to understand the mechanics of the scoliotic deformities, predict the risk of curve progression in AIS and thereby substantiate etiopathogenetic theories. Although AIS remains to be seen as a complex and multifactorial problem, the progression of its deformity can be predicted with good accuracy. Modeling of AIS develops rapidly and may lead to the identification of risk factors and mitigation strategies in the near future. The overview presented provides a basis to follow this development.
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Lee JH, Woo H, Jang JS, Kim JI, Na YC, Kim KR, Cho E, Lee JH, Park TY. Comparison of Concordance between Chuna Manual Therapy Diagnostic Methods (Palpation, X-ray, Artificial Intelligence Program) in Lumbar Spine: An Exploratory, Cross-Sectional Clinical Study. Diagnostics (Basel) 2022; 12:2732. [PMID: 36359575 PMCID: PMC9689192 DOI: 10.3390/diagnostics12112732] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/04/2022] [Accepted: 11/07/2022] [Indexed: 10/15/2023] Open
Abstract
Before Chuna manual therapy (CMT), a manual therapy applied in Korean medicine, CMT spinal diagnosis using palpation or X-ray is performed. However, studies on the inter-rater concordance of CMT diagnostic methods, concordance among diagnostic methods, and standard CMT diagnostic methods are scarce. Moreover, no clinical studies have used artificial intelligence (AI) programs for X-ray image-based CMT diagnosis. Therefore, this study sought a feasible and standard CMT spinal diagnostic method and explored the clinical applicability of the CMT-AI program. One hundred participants were recruited, and the concordance within and among different diagnostic modalities was analyzed by dividing them into manual diagnosis (MD), X-ray image-based diagnosis (XRD) by experts and non-experts, and XRD using a CMT-AI program by non-experts. Regarding intra-group concordance, XRD by experts showed the highest concordance (used as a gold standard when comparing inter-group concordance), followed by XRD using the AI program, XRD by non-experts, and then MD. Comparing diagnostic results between the groups, concordance with the gold standard was the highest for XRD using the AI program, followed by XRD by non-experts, and MD. Therefore, XRD is a more reasonable CMT diagnostic method than MD. Furthermore, the clinical applicability of the CMT-AI program is high.
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Affiliation(s)
- Jin-Hyun Lee
- Institute for Integrative Medicine, Catholic Kwandong University International St. Mary’s Hospital, 25 Simgok-ro 100 Beon-gil, Seo-gu, Incheon 22711, Republic of Korea
| | - Hyeonjun Woo
- Department of Korean Medicine Rehabilitation, College of Korean Medicine, Wonkwang University, 460 Iksan-daero, Iksan-si 54538, Republic of Korea
| | - Jun-Su Jang
- Digital Health Research Division, Korea Institute of Oriental Medicine, 1672 Yuseong-daero, Yuseong-gu, Daejeon 34054, Republic of Korea
| | - Joong Il Kim
- Digital Health Research Division, Korea Institute of Oriental Medicine, 1672 Yuseong-daero, Yuseong-gu, Daejeon 34054, Republic of Korea
| | - Young Cheol Na
- Department of Neurosurgery, Catholic Kwandong University International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, 25 Simgok-ro 100 Beon-gil, Seo-gu, Incheon 22711, Republic of Korea
| | - Kwang-Ryeol Kim
- Department of Neurosurgery, Catholic Kwandong University International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, 25 Simgok-ro 100 Beon-gil, Seo-gu, Incheon 22711, Republic of Korea
| | - Eunbyul Cho
- Department of Acupuncture and Moxibustion Medicine, College of Korean Medicine, Wonkwang University, 460 Iksan-daero, Iksan-si 54538, Republic of Korea
| | - Jung-Han Lee
- Department of Korean Medicine Rehabilitation, College of Korean Medicine, Wonkwang University, 460 Iksan-daero, Iksan-si 54538, Republic of Korea
| | - Tae-Yong Park
- Institute for Integrative Medicine, Catholic Kwandong University International St. Mary’s Hospital, 25 Simgok-ro 100 Beon-gil, Seo-gu, Incheon 22711, Republic of Korea
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Lee W, Shin K, Lee J, Yoo SJ, Yoon MA, Choi YW, Hong GS, Kim N, Paik S. Diagnosis of Scoliosis Using Chest Radiographs with a Semi-Supervised Generative Adversarial Network. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2022; 83:1298-1311. [PMID: 36545424 PMCID: PMC9748451 DOI: 10.3348/jksr.2021.0146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 10/03/2021] [Accepted: 11/08/2021] [Indexed: 06/17/2023]
Abstract
Purpose To develop and validate a deep learning-based screening tool for the early diagnosis of scoliosis using chest radiographs with a semi-supervised generative adversarial network (GAN). Materials and Methods Using a semi-supervised learning framework with a GAN, a screening tool for diagnosing scoliosis was developed and validated through the chest PA radiographs of patients at two different tertiary hospitals. Our proposed method used training GAN with mild to severe scoliosis only in a semi-supervised manner, as an upstream task to learn scoliosis representations and a downstream task to perform simple classification for differentiating between normal and scoliosis states sensitively. Results The area under the receiver operating characteristic curve, negative predictive value (NPV), positive predictive value, sensitivity, and specificity were 0.856, 0.950, 0.579, 0.985, and 0.285, respectively. Conclusion Our deep learning-based artificial intelligence software in a semi-supervised manner achieved excellent performance in diagnosing scoliosis using the chest PA radiographs of young individuals; thus, it could be used as a screening tool with high NPV and sensitivity and reduce the burden on radiologists for diagnosing scoliosis through health screening chest radiographs.
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Offiah AC. Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology. Pediatr Radiol 2022; 52:2149-2158. [PMID: 34272573 PMCID: PMC9537230 DOI: 10.1007/s00247-021-05130-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/28/2021] [Accepted: 06/10/2021] [Indexed: 12/03/2022]
Abstract
Artificial intelligence (AI) is playing an ever-increasing role in radiology (more so in the adult world than in pediatrics), to the extent that there are unfounded fears it will completely take over the role of the radiologist. In relation to musculoskeletal applications of AI in pediatric radiology, we are far from the time when AI will replace radiologists; even for the commonest application (bone age assessment), AI is more often employed in an AI-assist mode rather than an AI-replace or AI-extend mode. AI for bone age assessment has been in clinical use for more than a decade and is the area in which most research has been conducted. Most other potential indications in children (such as appendicular and vertebral fracture detection) remain largely in the research domain. This article reviews the areas in which AI is most prominent in relation to the pediatric musculoskeletal system, briefly summarizing the current literature and highlighting areas for future research. Pediatric radiologists are encouraged to participate as members of the research teams conducting pediatric radiology artificial intelligence research.
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Affiliation(s)
- Amaka C Offiah
- Department of Oncology and Metabolism, University of Sheffield, Damer Street Building, Sheffield, S10 2TH, UK.
- Department of Radiology, Sheffield Children's NHS Foundation Trust, Sheffield, UK.
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Baur D, Kroboth K, Heyde CE, Voelker A. Convolutional Neural Networks in Spinal Magnetic Resonance Imaging: A Systematic Review. World Neurosurg 2022; 166:60-70. [PMID: 35863650 DOI: 10.1016/j.wneu.2022.07.041] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 07/08/2022] [Accepted: 07/09/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Convolutional neural networks (CNNs) are being increasingly used in the medical field, especially for image recognition in high-resolution, large-volume data sets. The study represents the current state of research on the application of CNNs in image segmentation and pathology detection in spine magnetic resonance imaging. METHODS For this systematic literature review, the authors performed a systematic initial search of the PubMed/Medline and Web of Science (Core collection) databases for eligible investigations. The authors limited the search to observational studies. Outcome parameters were analyzed according to the inclusion criteria and assigned to 3 groups: 1) segmentation of anatomical structures, 2) segmentation and evaluation of pathologic structures, and 3) specific implementation of CNNs. RESULTS Twenty-four retrospectively designed articles met the inclusion criteria. Publication dates ranged from 2017 to 2021. In total, 14,065 patients with 113,110 analyzed images were included. Most authors trained their network with a training-to-testing ratio of 80/20, while all but 2 articles used 5- to 10-fold cross-validation. Nine articles compared their performance results with other neural networks and algorithms, and all 24 articles described outcomes as positive. CONCLUSIONS State-of-the-art CNNs can detect and segment-specific anatomical landmarks and pathologies across a wide range, comparable to the skills of radiologists and experienced clinicians. With rapidly evolving network architectures and growing medical image databases, the future is likely to show growth in the development and refinement of these capable networks. However, the aid of automated segmentation and classification by neural networks cannot and should not be expected to replace clinical experts.
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Affiliation(s)
- David Baur
- Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Katharina Kroboth
- Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Christoph-Eckhard Heyde
- Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Anna Voelker
- Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany.
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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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Development of artificial intelligence for automated measurement of cervical lordosis on lateral radiographs. Sci Rep 2022; 12:15732. [PMID: 36130962 PMCID: PMC9492662 DOI: 10.1038/s41598-022-19914-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 09/06/2022] [Indexed: 11/25/2022] Open
Abstract
Cervical sagittal alignment is an essential parameter for the evaluation of spine disorders. Manual measurement is time-consuming and burdensome to measurers. Artificial intelligence (AI) in the form of convolutional neural networks has begun to be used to measure x-rays. This study aimed to develop AI for automated measurement of lordosis on lateral cervical x-rays. We included 4546 cervical x-rays from 1674 patients. For all x-rays, the caudal endplates of C2 and C7 were labeled based on consensus among well-experienced spine surgeons, the data for which were used as ground truth. This ground truth was split into training data and test data, and the AI model learned the training data. The absolute error of the AI measurements relative to the ground truth for 4546 x-rays was determined by fivefold cross-validation. Additionally, the absolute error of AI measurements was compared with the error of other 2 surgeons’ measurements on 415 radiographs of 168 randomly selected patients. In fivefold cross-validation, the absolute error of the AI model was 3.3° in the average and 2.2° in the median. For comparison of other surgeons, the mean absolute error for measurement of 168 patients was 3.1° ± 3.4° for the AI model, 3.9° ± 3.4° for Surgeon 1, and 3.8° ± 4.7° for Surgeon 2. The AI model had a significantly smaller error than Surgeon 1 and Surgeon 2 (P = 0.002 and 0.036). This algorithm is available at (https://ykszk.github.io/c2c7demo/). The AI model measured cervical spine alignment with better accuracy than surgeons. AI can assist in routine medical care and can be helpful in research that measures large numbers of images. However, because of the large errors in rare cases such as highly deformed ones, AI may, in principle, be limited to assisting humans.
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Meng X, Wang Z, Ma X, Liu X, Ji H, Cheng JZ, Dong P. Fully automated measurement on coronal alignment of lower limbs using deep convolutional neural networks on radiographic images. BMC Musculoskelet Disord 2022; 23:869. [PMID: 36115981 PMCID: PMC9482267 DOI: 10.1186/s12891-022-05818-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 09/07/2022] [Indexed: 11/25/2022] Open
Abstract
Background A deep convolutional neural network (DCNN) system is proposed to measure the lower limb parameters of the mechanical lateral distal femur angle (mLDFA), medial proximal tibial angle (MPTA), lateral distal tibial angle (LDTA), joint line convergence angle (JLCA), and mechanical axis of the lower limbs. Methods Standing X-rays of 1000 patients’ lower limbs were examined for the DCNN and assigned to training, validation, and test sets. A coarse-to-fine network was employed to locate 20 key landmarks on both limbs that first recognised the regions of hip, knee, and ankle, and subsequently outputted the key points in each sub-region from a full-length X-ray. Finally, information from these key landmark locations was used to calculate the above five parameters. Results The DCNN system showed high consistency (intraclass correlation coefficient > 0.91) for all five lower limb parameters. Additionally, the mean absolute error (MAE) and root mean squared error (RMSE) of all angle predictions were lower than 3° for both the left and right limbs. The MAE of the mechanical axis of the lower limbs was 1.124 mm and 1.416 mm and the RMSE was 1.032 mm and 1.321 mm, for the right and left limbs, respectively. The measurement time of the DCNN system was 1.8 ± 1.3 s, which was significantly shorter than that of experienced radiologists (616.8 ± 48.2 s, t = -180.4, P < 0.001). Conclusions The proposed DCNN system can automatically measure mLDFA, MPTA, LDTA, JLCA, and the mechanical axis of the lower limbs, thus helping physicians manage lower limb alignment accurately and efficiently. Supplementary Information The online version contains supplementary material available at 10.1186/s12891-022-05818-4.
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Cui Y, Zhu J, Duan Z, Liao Z, Wang S, Liu W. Artificial Intelligence in Spinal Imaging: Current Status and Future Directions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11708. [PMID: 36141981 PMCID: PMC9517575 DOI: 10.3390/ijerph191811708] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 06/16/2023]
Abstract
Spinal maladies are among the most common causes of pain and disability worldwide. Imaging represents an important diagnostic procedure in spinal care. Imaging investigations can provide information and insights that are not visible through ordinary visual inspection. Multiscale in vivo interrogation has the potential to improve the assessment and monitoring of pathologies thanks to the convergence of imaging, artificial intelligence (AI), and radiomic techniques. AI is revolutionizing computer vision, autonomous driving, natural language processing, and speech recognition. These revolutionary technologies are already impacting radiology, diagnostics, and other fields, where automated solutions can increase precision and reproducibility. In the first section of this narrative review, we provide a brief explanation of the many approaches currently being developed, with a particular emphasis on those employed in spinal imaging studies. The previously documented uses of AI for challenges involving spinal imaging, including imaging appropriateness and protocoling, image acquisition and reconstruction, image presentation, image interpretation, and quantitative image analysis, are then detailed. Finally, the future applications of AI to imaging of the spine are discussed. AI has the potential to significantly affect every step in spinal imaging. AI can make images of the spine more useful to patients and doctors by improving image quality, imaging efficiency, and diagnostic accuracy.
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Affiliation(s)
- Yangyang Cui
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
- Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
| | - Jia Zhu
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
- Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
| | - Zhili Duan
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
- Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
| | - Zhenhua Liao
- Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
| | - Song Wang
- Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
| | - Weiqiang Liu
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
- Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
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Measuring the critical shoulder angle on radiographs: an accurate and repeatable deep learning model. Skeletal Radiol 2022; 51:1873-1878. [PMID: 35347406 DOI: 10.1007/s00256-022-04041-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/23/2022] [Accepted: 03/23/2022] [Indexed: 02/02/2023]
Abstract
PURPOSE Since the critical shoulder angle (CSA) is considered a risk factor for shoulder pathology and the intra- and inter-rater variabilities in its calculation are not negligible, we developed a deep learning model that calculates it automatically and accurately. METHODS We used a dataset of 8467 anteroposterior x-ray images of the shoulder annotated with 3 landmarks of interest. A Convolutional Neural Network model coupled with a spatial to numerical transform (DSNT) layer was used to predict the landmark coordinates from which the CSA was calculated. The performances were evaluated by calculating the Euclidean distance between the ground truth coordinates and the predicted ones normalized with respect to the distance between the first and the second points, and the error between the CSA angle measured by a human observer and the predicted one. RESULTS Regarding the normalized point distances, we obtained a median error of 2.9%, 2.5%, and 2% for the three points among the entire set. Considering CSA calculations, the median errors were 1° (standard deviation 1.2°), 0.88° (standard deviation 0.87°), and 0.99° (standard deviation 1°) for angles below 30°, between 30° and 35°, and above 35°, respectively. DISCUSSION These results demonstrate that the model has the potential to be used in clinical settings where the replicability is important. The reported standard error of the CSA measurement is greater than 2° that is above the median error of our model, indicating a potential accuracy sufficient to be used in a clinical setting.
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Zhou S, Zhou F, Sun Y, Chen X, Diao Y, Zhao Y, Huang H, Fan X, Zhang G, Li X. The application of artificial intelligence in spine surgery. Front Surg 2022; 9:885599. [PMID: 36034349 PMCID: PMC9403075 DOI: 10.3389/fsurg.2022.885599] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Due to its obvious advantages in processing big data and image information, the combination of artificial intelligence and medical care may profoundly change medical practice and promote the gradual transition from traditional clinical care to precision medicine mode. In this artical, we reviewed the relevant literatures and found that artificial intelligence was widely used in spine surgery. The application scenarios included etiology, diagnosis, treatment, postoperative prognosis and decision support systems of spinal diseases. The shift to artificial intelligence model in medicine constantly improved the level of doctors' diagnosis and treatment and the development of orthopedics.
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Affiliation(s)
- Shuai Zhou
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Feifei Zhou
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
- Correspondence: Feifei Zhou
| | - Yu Sun
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Xin Chen
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Yinze Diao
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Yanbin Zhao
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Haoge Huang
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Xiao Fan
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Gangqiang Zhang
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Xinhang Li
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
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Grover P, Siebenwirth J, Caspari C, Drange S, Dreischarf M, Le Huec JC, Putzier M, Franke J. Can artificial intelligence support or even replace physicians in measuring sagittal balance? A validation study on preoperative and postoperative full spine images of 170 patients. 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; 31:1943-1951. [PMID: 35796837 DOI: 10.1007/s00586-022-07309-5] [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: 12/15/2021] [Revised: 05/06/2022] [Accepted: 06/24/2022] [Indexed: 01/20/2023]
Abstract
PURPOSE Sagittal balance (SB) plays an important role in the surgical treatment of spinal disorders. The aim of this research study is to provide a detailed evaluation of a new, fully automated algorithm based on artificial intelligence (AI) for the determination of SB parameters on a large number of patients with and without instrumentation. METHODS Pre- and postoperative sagittal full body radiographs of 170 patients were measured by two human raters, twice by one rater and by the AI algorithm which determined: pelvic incidence, pelvic tilt, sacral slope, L1-S1 lordosis, T4-T12 thoracic kyphosis (TK) and the spino-sacral angle (SSA). To evaluate the agreement between human raters and AI, the mean error (95% confidence interval (CI)), standard deviation and an intra- and inter-rater reliability was conducted using intra-class correlation (ICC) coefficients. RESULTS ICC values for the assessment of the intra- (range: 0.88-0.97) and inter-rater (0.86-0.97) reliability of human raters are excellent. The algorithm is able to determine all parameters in 95% of all pre- and in 91% of all postoperative images with excellent ICC values (PreOP-range: 0.83-0.91, PostOP: 0.72-0.89). Mean errors are smallest for the SSA (PreOP: -0.1° (95%-CI: -0.9°-0.6°); PostOP: -0.5° (-1.4°-0.4°)) and largest for TK (7.0° (6.1°-7.8°); 7.1° (6.1°-8.1°)). CONCLUSION A new, fully automated algorithm that determines SB parameters has excellent reliability and agreement with human raters, particularly on preoperative full spine images. The presented solution will relieve physicians from time-consuming routine work of measuring SB parameters and allow the analysis of large databases efficiently.
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Affiliation(s)
- Priyanka Grover
- Raylytic GmbH, Petersstrasse 32-34, 04109, Leipzig, Germany.
| | | | | | - Steffen Drange
- Department of Orthopedics, Klinikum Magdeburg, Magdeburg, Germany
| | | | | | | | - Jörg Franke
- Department of Orthopedics, Klinikum Magdeburg, Magdeburg, Germany
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Weng CH, Huang YJ, Fu CJ, Yeh YC, Yeh CY, Tsai TT. Automatic recognition of whole-spine sagittal alignment and curvature analysis through a deep learning technique. 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; 31:2092-2103. [PMID: 35366104 DOI: 10.1007/s00586-022-07189-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 02/21/2022] [Accepted: 03/12/2022] [Indexed: 01/20/2023]
Abstract
PURPOSE Artificial intelligence based on deep learning (DL) approaches enables the automatic recognition of anatomic landmarks and subsequent estimation of various spinopelvic parameters. The locations of inflection points (IPs) and apices (APs) in whole-spine lateral radiographs could be mathematically determined by a fully automatic spinal sagittal curvature analysis system. METHODS We developed a DL model for automatic spinal curvature analysis of whole-spine lateral plain radiographs by using 1800 annotated images of various spinal disease etiologies. The DL model comprised a landmark localizer to detect 25 vertebral landmarks and a numerical algorithm for the generation of an individualized spinal sagittal curvature. The characteristics of the spinal curvature, including the IPs, APs, and curvature angle, could thus be analyzed using mathematical definitions. The localization error of each landmark was calculated from the predictions of 300 test images to evaluate the performance of the landmark localizer. The interrater reliability among a senior orthopedic surgeon, a radiologist, and the DL model was assessed using the intraclass correlation coefficient (ICC). RESULTS The accuracy of the landmark localizer was within an acceptable range (median error: 1.7-4.1 mm), and the interrater reliabilities between the proposed DL model and each expert were good to excellent (all ICCs > 0.85) for the measurement of spinal curvature characteristics. CONCLUSION The interrater reliability between the proposed DL model and human experts was good to excellent in predicting the locations of IPs, APs, and curvature angles. Future applications should be explored to validate this system and improve its clinical efficiency.
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Affiliation(s)
- Chi-Hung Weng
- aetherAI Co., Ltd., 9 F., No. 3-2, Park St., Nangang Dist., Taipei, 115, Taiwan
| | - Yu-Jui Huang
- Spine Division, Department of Orthopaedic Surgery, Bone and Joint Research Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 5, Fuxing St., Guishan Dist., Taoyuan, 333, Taiwan
| | - Chen-Ju Fu
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, 333, Taiwan
| | - Yu-Cheng Yeh
- Spine Division, Department of Orthopaedic Surgery, Bone and Joint Research Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 5, Fuxing St., Guishan Dist., Taoyuan, 333, Taiwan.
| | - Chao-Yuan Yeh
- aetherAI Co., Ltd., 9 F., No. 3-2, Park St., Nangang Dist., Taipei, 115, Taiwan
| | - Tsung-Ting Tsai
- Spine Division, Department of Orthopaedic Surgery, Bone and Joint Research Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 5, Fuxing St., Guishan Dist., Taoyuan, 333, Taiwan
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Hornung AL, Hornung CM, Mallow GM, Barajas JN, Espinoza Orías AA, Galbusera F, Wilke HJ, Colman M, Phillips FM, An HS, Samartzis D. Artificial intelligence and spine imaging: limitations, regulatory issues and future direction. 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; 31:2007-2021. [PMID: 35084588 DOI: 10.1007/s00586-021-07108-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 11/29/2021] [Accepted: 12/30/2021] [Indexed: 01/20/2023]
Abstract
BACKGROUND As big data and artificial intelligence (AI) in spine care, and medicine as a whole, continue to be at the forefront of research, careful consideration to the quality and techniques utilized is necessary. Predictive modeling, data science, and deep analytics have taken center stage. Within that space, AI and machine learning (ML) approaches toward the use of spine imaging have gathered considerable attention in the past decade. Although several benefits of such applications exist, limitations are also present and need to be considered. PURPOSE The following narrative review presents the current status of AI, in particular, ML, with special regard to imaging studies, in the field of spinal research. METHODS A multi-database assessment of the literature was conducted up to September 1, 2021, that addressed AI as it related to imaging of the spine. Articles written in English were selected and critically assessed. RESULTS Overall, the review discussed the limitations, data quality and applications of ML models in the context of spine imaging. In particular, we addressed the data quality and ML algorithms in spine imaging research by describing preliminary results from a widely accessible imaging algorithm that is currently available for spine specialists to reference for information on severity of spine disease and degeneration which ultimately may alter clinical decision-making. In addition, awareness of the current, under-recognized regulation surrounding the execution of ML for spine imaging was raised. CONCLUSIONS Recommendations were provided for conducting high-quality, standardized AI applications for spine imaging.
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Affiliation(s)
- Alexander L Hornung
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA
| | | | - G Michael Mallow
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA
| | - J Nicolas Barajas
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA
| | - Alejandro A Espinoza Orías
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA
| | | | - Hans-Joachim Wilke
- Institute of Orthopaedic Research and Biomechanics, Trauma Research Center Ulm, Ulm University, Ulm, Germany
| | - Matthew Colman
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA
| | - Frank M Phillips
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA
| | - Howard S An
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA
| | - Dino Samartzis
- Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA.
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Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation. 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; 31:2031-2045. [PMID: 35278146 DOI: 10.1007/s00586-022-07155-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/04/2022] [Accepted: 02/14/2022] [Indexed: 01/20/2023]
Abstract
PURPOSE To summarize and critically evaluate the existing studies for spinopelvic measurements of sagittal balance that are based on deep learning (DL). METHODS Three databases (PubMed, WoS and Scopus) were queried for records using keywords related to DL and measurement of sagittal balance. After screening the resulting 529 records that were augmented with specific web search, 34 studies published between 2017 and 2022 were included in the final review, and evaluated from the perspective of the observed sagittal spinopelvic parameters, properties of spine image datasets, applied DL methodology and resulting measurement performance. RESULTS Studies reported DL measurement of up to 18 different spinopelvic parameters, but the actual number depended on the image field of view. Image datasets were composed of lateral lumbar spine and whole spine X-rays, biplanar whole spine X-rays and lumbar spine magnetic resonance cross sections, and were increasing in size or enriched by augmentation techniques. Spinopelvic parameter measurement was approached either by landmark detection or structure segmentation, and U-Net was the most frequently applied DL architecture. The latest DL methods achieved excellent performance in terms of mean absolute error against reference manual measurements (~ 2° or ~ 1 mm). CONCLUSION Although the application of relatively complex DL architectures resulted in an improved measurement accuracy of sagittal spinopelvic parameters, future methods should focus on multi-institution and multi-observer analyses as well as uncertainty estimation and error handling implementations for integration into the clinical workflow. Further advances will enhance the predictive analytics of DL methods for spinopelvic parameter measurement. LEVEL OF EVIDENCE I Diagnostic: individual cross-sectional studies with the consistently applied reference standard and blinding.
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Iriondo C, Mehany S, Shah R, Bharadwaj U, Bahroos E, Chin C, Diab M, Pedoia V, Majumdar S. Institution-wide shape analysis of 3D spinal curvature and global alignment parameters. J Orthop Res 2022; 40:1896-1908. [PMID: 34845751 DOI: 10.1002/jor.25213] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 09/07/2021] [Accepted: 10/18/2021] [Indexed: 02/04/2023]
Abstract
The spine is an articulated, 3D structure with 6 degrees of translational and rotational freedom. Clinical studies have shown spinal deformities are associated with pain and functional disability in both adult and pediatric populations. Clinical decision making relies on accurate characterization of the spinal deformity and monitoring of its progression over time. However, Cobb angle measurements are time-consuming, are limited by interobserver variability, and represent a simplified 2D view of a 3D structure. Instead, spine deformities can be described by 3D shape parameters, addressing the limitations of current measurement methods. To this end, we develop and validate a deep learning algorithm to automatically extract the vertebral midline (from the upper endplate of S1 to the lower endplate of C7) for frontal and lateral radiographs. Our results demonstrate robust performance across datasets and patient populations. Approximations of 3D spines are reconstructed from the unit normalized midline curves of 20,118 pairs of full spine radiographs belonging to 15,378 patients acquired at our institution between 2008 and 2020. The resulting 3D dataset is used to describe global imbalance parameters in the patient population and to build a statistical shape model to describe global spine shape variations in preoperative deformity patients via eight interpretable shape parameters. The developed method can identify patient subgroups with similar shape characteristics without relying on an existing shape classification system.
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Affiliation(s)
- Claudia Iriondo
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.,Berkeley Joint Graduate Group in Bioengineering, University of California, San Francisco & University of California, San Francisco, California, USA
| | - Sarah Mehany
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Rutwik Shah
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Upasana Bharadwaj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Emma Bahroos
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Cynthia Chin
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Mohammad Diab
- Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, California, USA
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
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