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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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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 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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Haselhuhn JJ, Soriano PBO, Grover P, Dreischarf M, Odland K, Hendrickson NR, Jones KE, Martin CT, Sembrano JN, Polly DW. Spine surgeon versus AI algorithm full-length radiographic measurements: a validation study of complex adult spinal deformity patients. Spine Deform 2024; 12:755-761. [PMID: 38336942 DOI: 10.1007/s43390-024-00825-y] [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: 08/22/2023] [Accepted: 01/06/2024] [Indexed: 02/12/2024]
Abstract
INTRODUCTION Spinal measurements play an integral role in surgical planning for a variety of spine procedures. Full-length imaging eliminates distortions that can occur with stitched images. However, these images take radiologists significantly longer to read than conventional radiographs. Artificial intelligence (AI) image analysis software that can make such measurements quickly and reliably would be advantageous to surgeons, radiologists, and the entire health system. MATERIALS AND METHODS Institutional Review Board approval was obtained for this study. Preoperative full-length standing anterior-posterior and lateral radiographs of patients that were previously measured by fellowship-trained spine surgeons at our institution were obtained. The measurements included lumbar lordosis (LL), greatest coronal Cobb angle (GCC), pelvic incidence (PI), coronal balance (CB), and T1-pelvic angle (T1PA). Inter-rater intra-class correlation (ICC) values were calculated based on an overlapping sample of 10 patients measured by surgeons. Full-length standing radiographs of an additional 100 patients were provided for AI software training. The AI algorithm then measured the radiographs and ICC values were calculated. RESULTS ICC values for inter-rater reliability between surgeons were excellent and calculated to 0.97 for LL (95% CI 0.88-0.99), 0.78 (0.33-0.94) for GCC, 0.86 (0.55-0.96) for PI, 0.99 for CB (0.93-0.99), and 0.95 for T1PA (0.82-0.99). The algorithm computed the five selected parameters with ICC values between 0.70 and 0.94, indicating excellent reliability. Exemplary for the comparison of AI and surgeons, the ICC for LL was 0.88 (95% CI 0.83-0.92) and 0.93 for CB (0.90-0.95). GCC, PI, and T1PA could be determined with ICC values of 0.81 (0.69-0.87), 0.70 (0.60-0.78), and 0.94 (0.91-0.96) respectively. CONCLUSIONS The AI algorithm presented here demonstrates excellent reliability for most of the parameters and good reliability for PI, with ICC values corresponding to measurements conducted by experienced surgeons. In future, it may facilitate the analysis of large data sets and aid physicians in diagnostics, pre-operative planning, and post-operative quality control.
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Affiliation(s)
- Jason J Haselhuhn
- Department of Orthopedic Surgery, University of Minnesota, 2450 Riverside Avenue South, Suite R200, Minneapolis, MN, 55454, USA
| | - Paul Brian O Soriano
- Department of Orthopedic Surgery, University of Minnesota, 2450 Riverside Avenue South, Suite R200, Minneapolis, MN, 55454, USA
| | | | | | - Kari Odland
- Department of Orthopedic Surgery, University of Minnesota, 2450 Riverside Avenue South, Suite R200, Minneapolis, MN, 55454, USA
| | - Nathan R Hendrickson
- Department of Orthopedic Surgery, University of Minnesota, 2450 Riverside Avenue South, Suite R200, Minneapolis, MN, 55454, USA
| | - Kristen E Jones
- Department of Orthopedic Surgery, University of Minnesota, 2450 Riverside Avenue South, Suite R200, Minneapolis, MN, 55454, USA
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN, USA
| | - Christopher T Martin
- Department of Orthopedic Surgery, University of Minnesota, 2450 Riverside Avenue South, Suite R200, Minneapolis, MN, 55454, USA
| | - Jonathan N Sembrano
- Department of Orthopedic Surgery, University of Minnesota, 2450 Riverside Avenue South, Suite R200, Minneapolis, MN, 55454, USA
| | - David W Polly
- Department of Orthopedic Surgery, University of Minnesota, 2450 Riverside Avenue South, Suite R200, Minneapolis, MN, 55454, USA.
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN, USA.
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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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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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Oh BH, Kim JY, Lee JB, Kim IS, Hong JT, Sung JH, Lee HJ. Analysis of sagittal parameters for easier and more accurate determination of cervical spine alignment. Medicine (Baltimore) 2023; 102:e35511. [PMID: 37832123 PMCID: PMC10578776 DOI: 10.1097/md.0000000000035511] [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: 05/30/2023] [Accepted: 09/14/2023] [Indexed: 10/15/2023] Open
Abstract
Cross-sectional comparative study. This study aimed to analyze the role of cervical parameters, in terms of the perception process, when evaluating cervical sagittal balance on an X-ray image. Reports on the role of cervical parameters in the perception of cervical sagittal balance have not been made. The study included 4 board-certified neurosurgeons and 6 residents of a neurosurgical department. They were instructed to answer a total of 40 questions. The parameter that was the most helpful in deriving the answer was checked. The correct answer rate, dependency on the parameter, and correct answer contribution of the parameter were analyzed. Among the various parameters, 5 parameters [C2-7 angle (C2-7A), T1 slope minus cervical lordosis (T1s-CL), C2 slope (C2s), C7 slope (C7s), and C2-7 sagittal vertical axis) were selected. The simple parameter (C2s, C7s) has a higher dependency and correct answer contribution than the complex parameter (C2-7A, T1s-CL). The angular (C2-7A, T1s-CL, C2s, C7s) parameters have a higher dependency; however, both the length and angular parameters correct answer contribution were similar. The cervical parameters that have simpler properties were highly preferred and had a lower perception error.
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Affiliation(s)
- Byeong Ho Oh
- Department of Neurosurgery, Chungbuk National University Hospital, Cheongju, Republic of Korea
| | - Jee Yong Kim
- Department of Neurosurgery, St. Vincent Hospital, The Catholic University of Korea, Suwon, Republic of Korea
| | - Jong Beom Lee
- Department of Neurosurgery, Chungbuk National University Hospital, Cheongju, Republic of Korea
| | - Il Sup Kim
- Department of Neurosurgery, St. Vincent Hospital, The Catholic University of Korea, Suwon, Republic of Korea
| | - Jae Taek Hong
- Department of Neurosurgery, Eunpyeong St. Mary’s Hospital, The Catholic University of Korea, Seoul, Korea
| | - Jae Hoon Sung
- Department of Neurosurgery, St. Vincent Hospital, The Catholic University of Korea, Suwon, Republic of Korea
| | - Ho Jin Lee
- Department of Neurosurgery, St. Vincent Hospital, The Catholic University of Korea, Suwon, Republic of Korea
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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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Dalton J, Mohamed A, Akioyamen N, Schwab FJ, Lafage V. PreOperative Planning for Adult Spinal Deformity Goals: Level Selection and Alignment Goals. Neurosurg Clin N Am 2023; 34:527-536. [PMID: 37718099 DOI: 10.1016/j.nec.2023.06.016] [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: 09/19/2023]
Abstract
Adult Spinal Deformity (ASD) is a complex pathologic condition with significant impact on quality of life, including pain, loss of function, and fatigue. Achieving realignment goals is crucial for long-term results. Reliable preoperative planning strategies, including nomograms, measurement tools, and level selection, are key to maximizing the likelihood of achieving a good outcome following ASD corrective surgery. This review covers recent literature on such strategies, including review of the different targets for realignment and their association with outcomes (both patients-reported outcomes and complications), selection of upper and lower instrumented vertebrae, and the latest innovation in preoperative planning for deformity surgery.
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Affiliation(s)
- Jay Dalton
- Department of Orthopaedic Surgery, University of Pittsburgh Medical Center, 3471 Fifth Avenue, Pittsburgh, PA 15213, USA
| | - Ayman Mohamed
- Department of Orthopaedic Surgery, Lenox Hill Hospital, 130 East 77th Street, 11th Floor, New York, NY 10075, USA
| | - Noel Akioyamen
- Department of Orthopaedic Surgery, Monteriore Medical Center, 1250 Waters Place, Tower 1, 11th Floor, Bronx, NY 10461, USA
| | - Frank J Schwab
- Department of Orthopaedic Surgery, Lenox Hill Hospital, 130 East 77th Street, 11th Floor, New York, NY 10075, USA
| | - Virginie Lafage
- Department of Orthopaedic Surgery, Lenox Hill Hospital, 130 East 77th Street, 11th Floor, New York, NY 10075, USA.
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Yagi M, Yamanouchi K, Fujita N, Funao H, Ebata S. Proximal Junctional Failure in Adult Spinal Deformity Surgery: An In-depth Review. Neurospine 2023; 20:876-889. [PMID: 37798983 PMCID: PMC10562237 DOI: 10.14245/ns.2346566.283] [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: 05/19/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 10/07/2023] Open
Abstract
Adult spinal deformity (ASD) surgery aims to correct abnormal spinal curvature in adults, leading to improved functionality and reduced pain. However, this surgery is associated with various complications, one of which is proximal junctional failure (PJF). PJF can have a significant impact on a patient's quality of life, necessitating a comprehensive understanding of its causes and the development of effective management strategies. This review aims to provide an in-depth understanding of PJF in ASD surgery. PJF is a complex complication resulting from a multitude of factors including patient characteristics, surgical techniques, and postoperative management. Age, osteoporosis, overcorrection of sagittal alignment, and poor bone quality are identified as significant risk factors. The clinical implications of PJF are substantial, often requiring revision surgery and causing a considerable decrease in patients' quality of life. Prevention strategies include careful preoperative planning, appropriate patient selection, and optimization of surgical techniques. Treatment often necessitates a multifaceted approach, including surgical intervention and the management of underlying risk factors. Predictive modeling is an emerging field that may offer a promising avenue for the risk stratification of patients and individualized preventive strategies. A thorough understanding of PJF's pathogenesis, risk factors, and clinical implications is essential for surgeons involved in ASD surgery. Current preventive measures and treatment strategies aim to mitigate the risk and manage the complications of PJF, but the complication cannot be entirely prevented. Future research should focus on the development of more effective preventive and treatment strategies, and predictive models could be valuable in this pursuit.
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Affiliation(s)
- Mitsuru Yagi
- Department of Orthopaedic Surgery, International University of Health and Welfare, School of Medicine, Narita, Japan
| | - Kento Yamanouchi
- Department of Orthopaedic Surgery, International University of Health and Welfare, School of Medicine, Narita, Japan
| | - Naruhito Fujita
- Department of Orthopaedic Surgery, International University of Health and Welfare, School of Medicine, Narita, Japan
| | - Haruki Funao
- Department of Orthopaedic Surgery, International University of Health and Welfare, School of Medicine, Narita, Japan
| | - Shigeto Ebata
- Department of Orthopaedic Surgery, International University of Health and Welfare, School of Medicine, Narita, Japan
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Performance of a deep convolutional neural network for MRI-based vertebral body measurements and insufficiency fracture detection. Eur Radiol 2022; 33:3188-3199. [PMID: 36576545 PMCID: PMC10121505 DOI: 10.1007/s00330-022-09354-6] [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: 07/30/2022] [Revised: 09/23/2022] [Accepted: 11/29/2022] [Indexed: 12/29/2022]
Abstract
OBJECTIVES The aim is to validate the performance of a deep convolutional neural network (DCNN) for vertebral body measurements and insufficiency fracture detection on lumbar spine MRI. METHODS This retrospective analysis included 1000 vertebral bodies in 200 patients (age 75.2 ± 9.8 years) who underwent lumbar spine MRI at multiple institutions. 160/200 patients had ≥ one vertebral body insufficiency fracture, 40/200 had no fracture. The performance of the DCNN and that of two fellowship-trained musculoskeletal radiologists in vertebral body measurements (anterior/posterior height, extent of endplate concavity, vertebral angle) and evaluation for insufficiency fractures were compared. Statistics included (a) interobserver reliability metrics using intraclass correlation coefficient (ICC), kappa statistics, and Bland-Altman analysis, and (b) diagnostic performance metrics (sensitivity, specificity, accuracy). A statistically significant difference was accepted if the 95% confidence intervals did not overlap. RESULTS The inter-reader agreement between radiologists and the DCNN was excellent for vertebral body measurements, with ICC values of > 0.94 for anterior and posterior vertebral height and vertebral angle, and good to excellent for superior and inferior endplate concavity with ICC values of 0.79-0.85. The performance of the DCNN in fracture detection yielded a sensitivity of 0.941 (0.903-0.968), specificity of 0.969 (0.954-0.980), and accuracy of 0.962 (0.948-0.973). The diagnostic performance of the DCNN was independent of the radiological institution (accuracy 0.964 vs. 0.960), type of MRI scanner (accuracy 0.957 vs. 0.964), and magnetic field strength (accuracy 0.966 vs. 0.957). CONCLUSIONS A DCNN can achieve high diagnostic performance in vertebral body measurements and insufficiency fracture detection on heterogeneous lumbar spine MRI. KEY POINTS • A DCNN has the potential for high diagnostic performance in measuring vertebral bodies and detecting insufficiency fractures of the lumbar spine.
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