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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. [PMID: 38961674 DOI: 10.1111/os.14144] [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: 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 Surgery, Beijing Jishuitan Hospital Guizhou Hospital, Guiyang, China
| | - Luo Chunshan
- Department of Spine Surgery, Beijing Jishuitan Hospital Guizhou Hospital, Guiyang, China
| | - Yao Shudan
- Department of Spine Surgery, Beijing Jishuitan Hospital Guizhou Hospital, Guiyang, China
| | - Pu Xingwei
- Department of Spine Surgery, Beijing Jishuitan Hospital Guizhou Hospital, Guiyang, China
| | - Chen Qiling
- Department of Spine Surgery, Beijing Jishuitan Hospital Guizhou Hospital, Guiyang, China
| | - Yang Minglu
- Department of Spine Surgery, Beijing Jishuitan Hospital Guizhou Hospital, Guiyang, China
| | - Chen Lu
- Department of Spine Surgery, Beijing Jishuitan Hospital Guizhou Hospital, Guiyang, China
| | - Wang Lihang
- Department of Spine Surgery, Beijing Jishuitan Hospital Guizhou Hospital, Guiyang, China
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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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Zeng H, Zhou K, Ge S, Gao Y, Zhao J, Gao S, Zheng R. Anatomical Prior and Inter-Slice Consistency for Semi-Supervised Vertebral Structure Detection in 3D Ultrasound Volume. IEEE J Biomed Health Inform 2024; 28:2211-2222. [PMID: 38289848 DOI: 10.1109/jbhi.2024.3360102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Three-dimensional (3D) ultrasound imaging technique has been applied for scoliosis assessment, but the current assessment method only uses coronal projection images and cannot illustrate the 3D deformity and vertebra rotation. The vertebra detection is essential to reveal 3D spine information, but the detection task is challenging due to complex data and limited annotations. We propose VertMatch to detect vertebral structures in 3D ultrasound volume containing a detector and classifier. The detector network finds the potential positions of structures on transverse slice globally, and then the local patches are cropped based on detected positions. The classifier is used to distinguish whether the patches contain real vertebral structures and screen the predicted positions from the detector. VertMatch utilizes unlabeled data in a semi-supervised manner, and we develop two novel techniques for semi-supervised learning: 1) anatomical prior is used to acquire high-quality pseudo labels; 2) inter-slice consistency is used to utilize more unlabeled data by inputting multiple adjacent slices. Experimental results demonstrate that VertMatch can detect vertebra accurately in ultrasound volume and outperforms state-of-the-art methods. Moreover, VertMatch is also validated in automatic spinous process angle measurement on forty subjects with scoliosis, and the results illustrate that it can be a promising approach for the 3D assessment of scoliosis.
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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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Vogt S, Scholl C, Grover P, Marks J, Dreischarf M, Braumann UD, Strube P, Hölzl A, Böhle S. Novel AI-Based Algorithm for the Automated Measurement of Cervical Sagittal Balance Parameters. A Validation Study on Pre- and Postoperative Radiographs of 129 Patients. Global Spine J 2024:21925682241227428. [PMID: 38272462 DOI: 10.1177/21925682241227428] [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: 01/27/2024] Open
Abstract
STUDY DESIGN Retrospective, mono-centric cohort research study. OBJECTIVES The analysis of cervical sagittal balance parameters is essential for preoperative planning and dependent on the physician's experience. A fully automated artificial intelligence-based algorithm could contribute to an objective analysis and save time. Therefore, this algorithm should be validated in this study. METHODS Two surgeons measured C2-C7 lordosis, C1-C7 Sagittal Vertical Axis (SVA), C2-C7-SVA, C7-slope and T1-slope in pre- and postoperative lateral cervical X-rays of 129 patients undergoing anterior cervical surgery. All parameters were measured twice by surgeons and compared to the measurements by the AI algorithm consisting of 4 deep convolutional neural networks. Agreement between raters was quantified, among other metrics, by mean errors and single measure intraclass correlation coefficients for absolute agreement. RESULTS ICC-values for intra- (range: .92-1.0) and inter-rater (.91-1.0) reliability reflect excellent agreement between human raters. The AI-algorithm could determine all parameters with excellent ICC-values (preop:0.80-1.0; postop:0.86-.99). For a comparison between the AI algorithm and 1 surgeon, mean errors were smallest for C1-C7 SVA (preop: -.3 mm (95% CI:-.6 to -.1 mm), post: .3 mm (.0-.7 mm)) and largest for C2-C7 lordosis (preop:-2.2° (-2.9 to -1.6°), postop: 2.3°(-3.0 to -1.7°)). The automatic measurement was possible in 99% and 98% of pre- and postoperative images for all parameters except T1 slope, which had a detection rate of 48% and 51% in pre- and postoperative images. CONCLUSION This study validates that an AI-algorithm can reliably measure cervical sagittal balance parameters automatically in patients suffering from degenerative spinal diseases. It may simplify manual measurements and autonomously analyze large-scale datasets. Further studies are required to validate the algorithm on a larger and more diverse patient cohort.
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Affiliation(s)
- Sophia Vogt
- Orthopedic department of University Hospital Jena, Waldkliniken Eisenberg GmbH, Germany
| | - Carolin Scholl
- Research and Development, RAYLYTIC GmbH, Leipzig, Germany
| | | | - Julian Marks
- Research and Development, RAYLYTIC GmbH, Leipzig, Germany
- Leipzig University of Aplied Sciences (HTWK Leipzig), Faculty of Engineering, Leipzig, Germany
| | | | - Ulf-Dietrich Braumann
- Leipzig University of Aplied Sciences (HTWK Leipzig), Faculty of Engineering, Leipzig, Germany
- Fraunhofer Institute for Cell Therapy and Immunology, Cell-functional Image Analysis Unit, Leipzig, Germany
| | - Patrick Strube
- Orthopedic department of University Hospital Jena, Waldkliniken Eisenberg GmbH, Germany
| | - Alexander Hölzl
- Orthopedic department of University Hospital Jena, Waldkliniken Eisenberg GmbH, Germany
| | - Sabrina Böhle
- Orthopedic department of University Hospital Jena, Waldkliniken Eisenberg GmbH, Germany
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Zhang H, Chung ACS. A Dual Coordinate System Vertebra Landmark Detection Network with Sparse-to-Dense Vertebral Line Interpolation. Bioengineering (Basel) 2024; 11:101. [PMID: 38275581 DOI: 10.3390/bioengineering11010101] [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: 11/28/2023] [Revised: 01/05/2024] [Accepted: 01/12/2024] [Indexed: 01/27/2024] Open
Abstract
Precise surveillance and assessment of spinal disorders are important for improving health care and patient survival rates. The assessment of spinal disorders, such as scoliosis assessment, depends heavily on precise vertebra landmark localization. However, existing methods usually search for only a handful of keypoints in a high-resolution image. In this paper, we propose the S2D-VLI VLDet network, a unified end-to-end vertebra landmark detection network for the assessment of scoliosis. The proposed network considers the spatially relevant information both from inside and between vertebrae. The new vertebral line interpolation method converts the training labels from sparse to dense, which can improve the network learning process and method performance. In addition, through the combined use of the Cartesian and polar coordinate systems in our method, the symmetric mean absolute percentage error (SMAPE) in scoliosis assessment can be reduced substantially. Specifically, as shown in the experiments, the SMAPE value decreases from 9.82 to 8.28. The experimental results indicate that our proposed approach is beneficial for estimating the Cobb angle and identifying landmarks in X-ray scans with low contrast.
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Affiliation(s)
- Han Zhang
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
| | - Albert C S Chung
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
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Adida S, Legarreta AD, Hudson JS, McCarthy D, Andrews E, Shanahan R, Taori S, Lavadi RS, Buell TJ, Hamilton DK, Agarwal N, Gerszten PC. Machine Learning in Spine Surgery: A Narrative Review. Neurosurgery 2024; 94:53-64. [PMID: 37930259 DOI: 10.1227/neu.0000000000002660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/06/2023] [Indexed: 11/07/2023] Open
Abstract
Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.
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Affiliation(s)
- Samuel Adida
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Andrew D Legarreta
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Joseph S Hudson
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - David McCarthy
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Edward Andrews
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Regan Shanahan
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Suchet Taori
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Raj Swaroop Lavadi
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Thomas J Buell
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - D Kojo Hamilton
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Nitin Agarwal
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh , Pennsylvania , USA
| | - Peter C Gerszten
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
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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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Zhao M, Meng N, Cheung JPY, Yu C, Lu P, Zhang T. SpineHRformer: A Transformer-Based Deep Learning Model for Automatic Spine Deformity Assessment with Prospective Validation. Bioengineering (Basel) 2023; 10:1333. [PMID: 38002457 PMCID: PMC10669780 DOI: 10.3390/bioengineering10111333] [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: 10/08/2023] [Revised: 11/08/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
The Cobb angle (CA) serves as the principal method for assessing spinal deformity, but manual measurements of the CA are time-consuming and susceptible to inter- and intra-observer variability. While learning-based methods, such as SpineHRNet+, have demonstrated potential in automating CA measurement, their accuracy can be influenced by the severity of spinal deformity, image quality, relative position of rib and vertebrae, etc. Our aim is to create a reliable learning-based approach that provides consistent and highly accurate measurements of the CA from posteroanterior (PA) X-rays, surpassing the state-of-the-art method. To accomplish this, we introduce SpineHRformer, which identifies anatomical landmarks, including the vertices of endplates from the 7th cervical vertebra (C7) to the 5th lumbar vertebra (L5) and the end vertebrae with different output heads, enabling the calculation of CAs. Within our SpineHRformer, a backbone HRNet first extracts multi-scale features from the input X-ray, while transformer blocks extract local and global features from the HRNet outputs. Subsequently, an output head to generate heatmaps of the endplate landmarks or end vertebra landmarks facilitates the computation of CAs. We used a dataset of 1934 PA X-rays with diverse degrees of spinal deformity and image quality, following an 8:2 ratio to train and test the model. The experimental results indicate that SpineHRformer outperforms SpineHRNet+ in landmark detection (Mean Euclidean Distance: 2.47 pixels vs. 2.74 pixels), CA prediction (Pearson correlation coefficient: 0.86 vs. 0.83), and severity grading (sensitivity: normal-mild; 0.93 vs. 0.74, moderate; 0.74 vs. 0.77, severe; 0.74 vs. 0.7). Our approach demonstrates greater robustness and accuracy compared to SpineHRNet+, offering substantial potential for improving the efficiency and reliability of CA measurements in clinical settings.
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Affiliation(s)
| | | | | | | | | | - Teng Zhang
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong; (M.Z.); (N.M.); (J.P.Y.C.); (C.Y.); (P.L.)
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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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12
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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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13
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Foley D, Hardacker P, McCarthy M. Emerging Technologies within Spine Surgery. Life (Basel) 2023; 13:2028. [PMID: 37895410 PMCID: PMC10608700 DOI: 10.3390/life13102028] [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: 08/30/2023] [Revised: 10/02/2023] [Accepted: 10/07/2023] [Indexed: 10/29/2023] Open
Abstract
New innovations within spine surgery continue to propel the field forward. These technologies improve surgeons' understanding of their patients and allow them to optimize treatment planning both in the operating room and clinic. Additionally, changes in the implants and surgeon practice habits continue to evolve secondary to emerging biomaterials and device design. With ongoing advancements, patients can expect enhanced preoperative decision-making, improved patient outcomes, and better intraoperative execution. Additionally, these changes may decrease many of the most common complications following spine surgery in order to reduce morbidity, mortality, and the need for reoperation. This article reviews some of these technological advancements and how they are projected to impact the field. As the field continues to advance, it is vital that practitioners remain knowledgeable of these changes in order to provide the most effective treatment possible.
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Affiliation(s)
- David Foley
- Department of Orthopaedic Surgery, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Pierce Hardacker
- Indiana University School of Medicine, Indianapolis, IN 46202, USA;
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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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15
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Maeda Y, Nagura T, Nakamura M, Watanabe K. Automatic measurement of the Cobb angle for adolescent idiopathic scoliosis using convolutional neural network. Sci Rep 2023; 13:14576. [PMID: 37666981 PMCID: PMC10477263 DOI: 10.1038/s41598-023-41821-y] [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: 04/20/2023] [Accepted: 08/31/2023] [Indexed: 09/06/2023] Open
Abstract
This study proposes a convolutional neural network method for automatic vertebrae detection and Cobb angle (CA) measurement on X-ray images for scoliosis. 1021 full-length X-ray images of the whole spine of patients with adolescent idiopathic scoliosis (AIS) were used for training and segmentation. The proposed AI algorithm's results were compared with those of the manual method by six doctors using the intraclass correlation coefficient (ICC). The ICCs recorded by six doctors and AI were excellent or good, with a value of 0.973 for the major curve in the standing position. The mean error between AI and doctors was not affected by the angle size, with AI tending to measure 1.7°-2.2° smaller than that measured by the doctors. The proposed method showed a high correlation with the doctors' measurements, regardless of the CA size, doctors' experience, and patient posture. The proposed method showed excellent reliability, indicating that it is a promising automated method for measuring CA in patients with AIS.
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Affiliation(s)
- Yoshihiro Maeda
- Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Takeo Nagura
- Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Masaya Nakamura
- Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Kota Watanabe
- Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo, Japan.
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16
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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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17
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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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18
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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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Pang C, Su Z, Lin L, Lin G, He J, Lu H, Feng Q, Pang S. Automated measurement of spine indices on axial MR images for lumbar spinal stenosis diagnosis using segmentation-guided regression network. Med Phys 2023; 50:104-116. [PMID: 36029008 DOI: 10.1002/mp.15961] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 08/03/2022] [Accepted: 08/21/2022] [Indexed: 01/25/2023] Open
Abstract
PURPOSE Automated measurement of spine indices on axial magnetic resonance (MR) images plays a significant role in lumbar spinal stenosis diagnosis. Existing direct spine indices measurement approaches fail to explicitly focus on the task-specific region or feature channel with the additional information for guiding. We aim to achieve accurate spine indices measurement by introducing the guidance of the segmentation task. METHODS In this paper, we propose a segmentation-guided regression network (SGRNet) to achieve automated spine indices measurement. SGRNet consists of a segmentation path for generating the spine segmentation prediction and a regression path for producing spine indices estimation. The segmentation path is a U-Net-like network which includes a segmentation encoder and a decoder which generates multilevel segmentation features and segmentation prediction. The proposed segmentation-guided attention module (SGAM) in the regression encoder extracts the attention-aware regression feature under the guidance of the segmentation feature. Based on the attention-aware regression feature, a fully connected layer is utilized to output the accurate spine indices estimation. RESULTS Experiments on the open-access Lumbar Spine MRI data set show that SGRNet achieves state-of-the-art performance with a mean absolute error of 0.49 mm and mean Pearson correlation coefficient of 0.956 for four indices estimation. CONCLUSIONS The proposed SGAM in SGRNet is capable of improving the performance of spine indices measurement by focusing on the task-specific region and feature channel under the guidance of the segmentation task.
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Affiliation(s)
- Chunlan Pang
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zhihai Su
- Department of Spinal Surgery, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
| | - Liyan Lin
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Guoye Lin
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Ji He
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China
| | - Hai Lu
- Department of Spinal Surgery, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
| | - Qianjin Feng
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Shumao Pang
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China
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20
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Huang Y, Jones CK, Zhang X, Johnston A, Waktola S, Aygun N, Witham TF, Bydon A, Theodore N, Helm PA, Siewerdsen JH, Uneri A. Multi-perspective region-based CNNs for vertebrae labeling in intraoperative long-length images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 227:107222. [PMID: 36370597 DOI: 10.1016/j.cmpb.2022.107222] [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: 06/17/2022] [Revised: 10/31/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
PURPOSE Effective aggregation of intraoperative x-ray images that capture the patient anatomy from multiple view-angles has the potential to enable and improve automated image analysis that can be readily performed during surgery. We present multi-perspective region-based neural networks that leverage knowledge of the imaging geometry for automatic vertebrae labeling in Long-Film images - a novel tomographic imaging modality with an extended field-of-view for spine imaging. METHOD A multi-perspective network architecture was designed to exploit small view-angle disparities produced by a multi-slot collimator and consolidate information from overlapping image regions. A second network incorporates large view-angle disparities to jointly perform labeling on images from multiple views (viz., AP and lateral). A recurrent module incorporates contextual information and enforce anatomical order for the detected vertebrae. The three modules are combined to form the multi-view multi-slot (MVMS) network for labeling vertebrae using images from all available perspectives. The network was trained on images synthesized from 297 CT images and tested on 50 AP and 50 lateral Long-Film images acquired from 13 cadaveric specimens. Labeling performance of the multi-perspective networks was evaluated with respect to the number of vertebrae appearances and presence of surgical instrumentation. RESULTS The MVMS network achieved an F1 score of >96% and an average vertebral localization error of 3.3 mm, with 88.3% labeling accuracy on both AP and lateral images - (15.5% and 35.0% higher than conventional Faster R-CNN on AP and lateral views, respectively). Aggregation of multiple appearances of the same vertebra using the multi-slot network significantly improved the labeling accuracy (p < 0.05). Using the multi-view network, labeling accuracy on the more challenging lateral views was improved to the same level as that of the AP views. The approach demonstrated robustness to the presence of surgical instrumentation, commonly encountered in intraoperative images, and achieved comparable performance in images with and without instrumentation (88.9% vs. 91.2% labeling accuracy). CONCLUSION The MVMS network demonstrated effective multi-perspective aggregation, providing means for accurate, automated vertebrae labeling during spine surgery. The algorithms may be generalized to other imaging tasks and modalities that involve multiple views with view-angle disparities (e.g., bi-plane radiography). Predicted labels can help avoid adverse events during surgery (e.g., wrong-level surgery), establish correspondence with labels in preoperative modalities to facilitate image registration, and enable automated measurement of spinal alignment metrics for intraoperative assessment of spinal curvature.
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Affiliation(s)
- Y Huang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, United States
| | - C K Jones
- Department of Computer Science, Johns Hopkins University, Baltimore MD, United States
| | - X Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, United States
| | - A Johnston
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, United States
| | - S Waktola
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, United States
| | - N Aygun
- Department of Radiology, Johns Hopkins Medicine, Baltimore MD, United States
| | - T F Witham
- Department of Neurosurgery, Johns Hopkins Medicine, Baltimore MD, United States
| | - A Bydon
- Department of Neurosurgery, Johns Hopkins Medicine, Baltimore MD, United States
| | - N Theodore
- Department of Neurosurgery, Johns Hopkins Medicine, Baltimore MD, United States
| | - P A Helm
- Medtronic, Littleton MA, United States
| | - J H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, United States; Department of Computer Science, Johns Hopkins University, Baltimore MD, United States; Department of Radiology, Johns Hopkins Medicine, Baltimore MD, United States; Department of Neurosurgery, Johns Hopkins Medicine, Baltimore MD, United States; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston TX, United States
| | - A Uneri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, United States.
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21
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Otjen JP, Moore MM, Romberg EK, Perez FA, Iyer RS. The current and future roles of artificial intelligence in pediatric radiology. Pediatr Radiol 2022; 52:2065-2073. [PMID: 34046708 DOI: 10.1007/s00247-021-05086-9] [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: 10/07/2020] [Revised: 01/27/2021] [Accepted: 04/20/2021] [Indexed: 12/11/2022]
Abstract
Artificial intelligence (AI) is a broad and complicated concept that has begun to affect many areas of medicine, perhaps none so much as radiology. While pediatric radiology has been less affected than other radiology subspecialties, there are some well-developed and some nascent applications within the field. This review focuses on the use of AI within pediatric radiology for image interpretation, with descriptive summaries of the literature to date. We highlight common features that enable successful application of the technology, along with some of the limitations that can inhibit the development of this field. We present some ideas for further research in this area and challenges that must be overcome, with an understanding that technology often advances in unpredictable ways.
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Affiliation(s)
- Jeffrey P Otjen
- Department of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, 4800 Sand Point Way NE, MA.7.220, Seattle, WA, 98105, USA
| | - Michael M Moore
- Department of Radiology, Penn State Children's Hospital, Penn State Health System, Hershey, PA, USA
| | - Erin K Romberg
- Department of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, 4800 Sand Point Way NE, MA.7.220, Seattle, WA, 98105, USA
| | - Francisco A Perez
- Department of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, 4800 Sand Point Way NE, MA.7.220, Seattle, WA, 98105, USA
| | - Ramesh S Iyer
- Department of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, 4800 Sand Point Way NE, MA.7.220, Seattle, WA, 98105, USA.
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22
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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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Trinh GM, Shao HC, Hsieh KLC, Lee CY, Liu HW, Lai CW, Chou SY, Tsai PI, Chen KJ, Chang FC, Wu MH, Huang TJ. Detection of Lumbar Spondylolisthesis from X-ray Images Using Deep Learning Network. J Clin Med 2022; 11:jcm11185450. [PMID: 36143096 PMCID: PMC9501139 DOI: 10.3390/jcm11185450] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/02/2022] [Accepted: 09/07/2022] [Indexed: 11/16/2022] Open
Abstract
Spondylolisthesis refers to the displacement of a vertebral body relative to the vertrabra below it, which can cause radicular symptoms, back pain or leg pain. It usually occurs in the lower lumbar spine, especially in women over the age of 60. The prevalence of spondylolisthesis is expected to rise as the global population ages, requiring prudent action to promptly identify it in clinical settings. The goal of this study was to develop a computer-aided diagnostic (CADx) algorithm, LumbarNet, and to evaluate the efficiency of this model in automatically detecting spondylolisthesis from lumbar X-ray images. Built upon U-Net, feature fusion module (FFM) and collaborating with (i) a P-grade, (ii) a piecewise slope detection (PSD) scheme, and (iii) a dynamic shift (DS), LumbarNet was able to analyze complex structural patterns on lumbar X-ray images, including true lateral, flexion, and extension lateral views. Our results showed that the model achieved a mean intersection over union (mIOU) value of 0.88 in vertebral region segmentation and an accuracy of 88.83% in vertebral slip detection. We conclude that LumbarNet outperformed U-Net, a commonly used method in medical image segmentation, and could serve as a reliable method to identify spondylolisthesis.
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Affiliation(s)
- Giam Minh Trinh
- International Graduate Program in Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Department of Trauma-Orthopedics, College of Medicine, Pham Ngoc Thach Medical University, Ho Chi Minh City 700000, Vietnam
- Department of Pediatric Orthopedics, Hospital for Traumatology and Orthopedics, Ho Chi Minh City 700000, Vietnam
| | - Hao-Chiang Shao
- Institute of Data Science and Information Computing, National Chung Hsing University, Taichung City 402, Taiwan
| | - Kevin Li-Chun Hsieh
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei 11031, Taiwan
- Research Center of Translational Imaging, Taipei Medical University Hospital, Taipei 11031, Taiwan
| | - Ching-Yu Lee
- Department of Orthopedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Department of Orthopedics, Taipei Medical University Hospital, Taipei 11031, Taiwan
| | - Hsiao-Wei Liu
- Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu 30044, Taiwan
| | - Chen-Wei Lai
- Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu 30044, Taiwan
| | - Sen-Yi Chou
- Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu 30044, Taiwan
| | - Pei-I Tsai
- Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu 31057, Taiwan
| | - Kuan-Jen Chen
- Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu 31057, Taiwan
| | - Fang-Chieh Chang
- Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu 31057, Taiwan
| | - Meng-Huang Wu
- Department of Orthopedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Department of Orthopedics, Taipei Medical University Hospital, Taipei 11031, Taiwan
- TMU Biodesign Center, Taipei Medical University, Taipei 11031, Taiwan
- Correspondence: (M.-H.W.); (T.-J.H.)
| | - Tsung-Jen Huang
- Department of Orthopedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Department of Orthopedics, Taipei Medical University Hospital, Taipei 11031, Taiwan
- Correspondence: (M.-H.W.); (T.-J.H.)
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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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25
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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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26
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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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27
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Zhao Y, Zhang J, Li H, Gu X, Li Z, Zhang S. Automatic Cobb angle measurement method based on vertebra segmentation by deep learning. Med Biol Eng Comput 2022; 60:2257-2269. [DOI: 10.1007/s11517-022-02563-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 03/25/2022] [Indexed: 10/18/2022]
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28
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Tseng T, Chen Y, Yeh Y, Kuo C, Fan T, Lin Y. Automatic prosthetic‐parameter estimation from anteroposterior pelvic radiographs after total hip arthroplasty using deep learning‐based keypoint detection. Int J Med Robot 2022; 18:e2394. [DOI: 10.1002/rcs.2394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 11/05/2022]
Affiliation(s)
- Tsung‐Wei Tseng
- Department of Orthopaedic Surgery Chang Gung Memorial Hospital (CGMH) Taoyuan Taiwan
- Bone and Joint Research Center Chang Gung Memorial Hospital (CGMH) Taoyuan Taiwan
| | - Yueh‐Peng Chen
- Center for Artificial Intelligence in Medicine Chang Gung Memorial Hospital Linkou Medical Center Taoyuan Taiwan
| | - Yu‐Cheng Yeh
- Department of Orthopaedic Surgery Chang Gung Memorial Hospital (CGMH) Taoyuan Taiwan
- Bone and Joint Research Center Chang Gung Memorial Hospital (CGMH) Taoyuan Taiwan
| | - Chang‐Fu Kuo
- Center for Artificial Intelligence in Medicine Chang Gung Memorial Hospital Linkou Medical Center Taoyuan Taiwan
| | - Tzuo‐Yau Fan
- Center for Artificial Intelligence in Medicine Chang Gung Memorial Hospital Linkou Medical Center Taoyuan Taiwan
| | - Yu‐Chih Lin
- Department of Orthopaedic Surgery Chang Gung Memorial Hospital (CGMH) Taoyuan Taiwan
- Bone and Joint Research Center Chang Gung Memorial Hospital (CGMH) Taoyuan Taiwan
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Qu B, Cao J, Qian C, Wu J, Lin J, Wang L, Ou-Yang L, Chen Y, Yan L, Hong Q, Zheng G, Qu X. Current development and prospects of deep learning in spine image analysis: a literature review. Quant Imaging Med Surg 2022; 12:3454-3479. [PMID: 35655825 PMCID: PMC9131328 DOI: 10.21037/qims-21-939] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 03/04/2022] [Indexed: 10/07/2023]
Abstract
BACKGROUND AND OBJECTIVE As the spine is pivotal in the support and protection of human bodies, much attention is given to the understanding of spinal diseases. Quick, accurate, and automatic analysis of a spine image greatly enhances the efficiency with which spine conditions can be diagnosed. Deep learning (DL) is a representative artificial intelligence technology that has made encouraging progress in the last 6 years. However, it is still difficult for clinicians and technicians to fully understand this rapidly evolving field due to the diversity of applications, network structures, and evaluation criteria. This study aimed to provide clinicians and technicians with a comprehensive understanding of the development and prospects of DL spine image analysis by reviewing published literature. METHODS A systematic literature search was conducted in the PubMed and Web of Science databases using the keywords "deep learning" and "spine". Date ranges used to conduct the search were from 1 January, 2015 to 20 March, 2021. A total of 79 English articles were reviewed. KEY CONTENT AND FINDINGS The DL technology has been applied extensively to the segmentation, detection, diagnosis, and quantitative evaluation of spine images. It uses static or dynamic image information, as well as local or non-local information. The high accuracy of analysis is comparable to that achieved manually by doctors. However, further exploration is needed in terms of data sharing, functional information, and network interpretability. CONCLUSIONS The DL technique is a powerful method for spine image analysis. We believe that, with the joint efforts of researchers and clinicians, intelligent, interpretable, and reliable DL spine analysis methods will be widely applied in clinical practice in the future.
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Affiliation(s)
- Biao Qu
- Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China
| | - Jianpeng Cao
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Chen Qian
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Jinyu Wu
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Jianzhong Lin
- Department of Radiology, Zhongshan Hospital of Xiamen University, Xiamen, China
| | - Liansheng Wang
- Department of Computer Science, School of Informatics, Xiamen University, Xiamen, China
| | - Lin Ou-Yang
- Department of Medical Imaging of Southeast Hospital, Medical College of Xiamen University, Zhangzhou, China
| | - Yongfa Chen
- Department of Pediatric Orthopedic Surgery, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Liyue Yan
- Department of Information & Computational Mathematics, Xiamen University, Xiamen, China
| | - Qing Hong
- Biomedical Intelligent Cloud R&D Center, China Mobile Group, Xiamen, China
| | - Gaofeng Zheng
- Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China
| | - Xiaobo Qu
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
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30
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Sigurdson S, Wong J, Reformat M, Lou E. Applying a Convolutional Neural Network Based Iterative Algorithm to Automatically Measure Spinal Curvature on Radiographs for Children with Scoliosis. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00712-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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31
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Jin C, Wang S, Yang G, Li E, Liang Z. A Review of the Methods on Cobb Angle Measurements for Spinal Curvature. SENSORS 2022; 22:s22093258. [PMID: 35590951 PMCID: PMC9101880 DOI: 10.3390/s22093258] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/11/2022] [Accepted: 04/19/2022] [Indexed: 11/16/2022]
Abstract
Scoliosis is a common disease of the spine and requires regular monitoring due to its progressive properties. A preferred indicator to assess scoliosis is by the Cobb angle, which is currently measured either manually by the relevant medical staff or semi-automatically, aided by a computer. These methods are not only labor-intensive but also vary in precision by the inter-observer and intra-observer. Therefore, a reliable and convenient method is urgently needed. With the development of computer vision and deep learning, it is possible to automatically calculate the Cobb angles by processing X-ray or CT/MR/US images. In this paper, the research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning. By comparing the measurement effects of typical methods, their advantages and disadvantages are analyzed. Finally, the key issues and their development trends are also discussed.
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Affiliation(s)
- Chen Jin
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; (C.J.); (E.L.); (Z.L.)
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shengru Wang
- Peking Union Medical College Hospital, Beijing 100005, China;
| | - Guodong Yang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; (C.J.); (E.L.); (Z.L.)
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: ; Tel.: +86-10-82544504
| | - En Li
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; (C.J.); (E.L.); (Z.L.)
| | - Zize Liang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; (C.J.); (E.L.); (Z.L.)
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Karandikar P, Massaad E, Hadzipasic M, Kiapour A, Joshi RS, Shankar GM, Shin JH. Machine Learning Applications of Surgical Imaging for the Diagnosis and Treatment of Spine Disorders: Current State of the Art. Neurosurgery 2022; 90:372-382. [PMID: 35107085 DOI: 10.1227/neu.0000000000001853] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 11/10/2021] [Indexed: 01/18/2023] Open
Abstract
Recent developments in machine learning (ML) methods demonstrate unparalleled potential for application in the spine. The ability for ML to provide diagnostic faculty, produce novel insights from existing capabilities, and augment or accelerate elements of surgical planning and decision making at levels equivalent or superior to humans will tremendously benefit spine surgeons and patients alike. In this review, we aim to provide a clinically relevant outline of ML-based technology in the contexts of spinal deformity, degeneration, and trauma, as well as an overview of commercial-level and precommercial-level surgical assist systems and decisional support tools. Furthermore, we briefly discuss potential applications of generative networks before highlighting some of the limitations of ML applications. We conclude that ML in spine imaging represents a significant addition to the neurosurgeon's armamentarium-it has the capacity to directly address and manifest clinical needs and improve diagnostic and procedural quality and safety-but is yet subject to challenges that must be addressed before widespread implementation.
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Affiliation(s)
- Paramesh Karandikar
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- T.H. Chan School of Medicine, University of Massachusetts, Worcester, Massachusetts, USA
| | - Elie Massaad
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Muhamed Hadzipasic
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Kiapour
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Rushikesh S Joshi
- Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan, USA
| | - Ganesh M Shankar
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - John H Shin
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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A multi-stage ensemble network system to diagnose adolescent idiopathic scoliosis. Eur Radiol 2022; 32:5880-5889. [DOI: 10.1007/s00330-022-08692-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 01/24/2022] [Accepted: 02/23/2022] [Indexed: 01/22/2023]
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Yao Y, Yu W, Gao Y, Dong J, Xiao Q, Huang B, Shi Z. W-Transformer: Accurate Cobb angles estimation by using a transformer-based hybrid structure. Med Phys 2022; 49:3246-3262. [PMID: 35194794 DOI: 10.1002/mp.15561] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 01/19/2022] [Accepted: 02/16/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Scoliosis is a type of spinal deformity, which is harmful to a person's health. In severe cases, it can trigger paralysis or death. The measurement of Cobb angle plays an essential role in assessing the severity of scoliosis. PURPOSE The aim of this paper is to propose an automatic system for landmark detection and Cobb angle estimation, which can effectively help clinicians diagnose and treat scoliosis. METHODS A novel hybrid framework was proposed to measure Cobb angle precisely for clinical diagnosis, which was referred as W-Transformer due to its w-shaped architecture. First, a convolutional neural network of cascade residual blocks as our backbone was designed. Then a transformer was fused to learn the dependency information between spine and landmarks. In addition, a reinforcement branch was designed to improve the overlap of landmarks, and an improved prediction module was proposed to fine-tune the final coordinates of landmarks in Cobb angles estimation. Besides, the public AASCE MICCAI 2019 challenge was served as dataset. It supplies 609 manually labeled spine AP X-ray images, each of which contains a total of 68 landmark labels and three Cobb Angles tags. RESULTS From the perspective of the AASCE MICCAI 2019 challenge, we achieved a lower symmetric mean absolute percentage error (SMAPE) of 8.26% for all Cobb angles and the lowest averaged detection error of 50.89 in terms of landmark detection, compared with many state-of-the-art methods. We also provided the SMAPEs for the Cobb angles of the Proximal-Thoracic (PT), the Main-Thoracic (MT) and the Thoracic-Lumbar (TL) area, which are 5.27%, 14.59% and 20.97% respectively, however, these data were not covered in most previous studies. Statistical analysis demonstrates that our model has obtained a high level of Pearson correlation coefficient of 0.9398 (p<0.001), which shows excellent reliability of our model. Our model can yield 0.9489 (p<0.001), 0.8817 (p<0.001) and 0.9149 (p<0.001$) for PT, MT and TL, respectively. The overall variability of Cobb angle measurement is less than 4°, implying clinical value. And the mean absolute deviation (Standard Deviation) for three regions is 3.64° (4.13°), 3.84° (4.66°) and 3.80° (4.19°). The results of Student paired t-test indicate that no statistically significant differences are observed between manual measurement and our automatic approach (p value is always > 0.05). Regarding the diagnosis of scoliosis (Cobb angle > 10°), the proposed method achieves a high sensitivity of 0.9577 and a specificity of 0.8475 for all spinal regions. CONCLUSIONS This study offers a brand-new automatic approach that is potentially of great benefit of the complex task of landmark detection and Cobb angle evaluation, which can provide helpful navigation information about the early diagnosis of scoliosis. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yifan Yao
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China
| | - Wenjun Yu
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China
| | - Yongbin Gao
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China
| | - Jiuqing Dong
- Division of Electronics Engineering, Jeonbuk National University, Jeonju, 54896, Republic of Korea
| | - Qiangqiang Xiao
- Department of Orthopedic Surgery, Changzheng Hospital, Second Military Medical University, Shanghai, 200003, China
| | - Bo Huang
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China
| | - Zhicai Shi
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China
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Nguyen TP, Jung JW, Yoo YJ, Choi SH, Yoon J. Intelligent Evaluation of Global Spinal Alignment by a Decentralized Convolutional Neural Network. J Digit Imaging 2022; 35:213-225. [PMID: 35064369 PMCID: PMC8921409 DOI: 10.1007/s10278-021-00533-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 09/30/2021] [Accepted: 10/31/2021] [Indexed: 01/12/2023] Open
Abstract
Degenerative changes of the spine can cause spinal misalignment, with part of the spine arching beyond normal limits or moving in an incorrect direction, potentially resulting in back pain and significantly limiting a person’s mobility. The most important parameters related to spinal misalignment include pelvic incidence, pelvic tilt, lumbar lordosis, thoracic kyphosis, and cervical lordosis. As a general rule, alignment of the spine for diagnosis and surgical treatment is estimated based on geometrical parameters measured manually by experienced doctors. However, these measurements consume the time and effort of experts to perform repetitive tasks that could be automated, especially with the powerful support of current artificial intelligence techniques. This paper focuses on creation of a decentralized convolutional neural network to precisely measure 12 spinal alignment parameters. Specifically, this method is based on detecting regions of interest with its dimensions that decrease by three orders of magnitude to focus on the necessary region to provide the output as key points. Using these key points, parameters representing spinal alignment are calculated. The quality of the method’s performance, which is the consistency of the measurement results with manual measurement, is validated by 30 test cases and shows 10 of 12 parameters with a correlation coefficient > 0.8, with pelvic tilt having the smallest absolute deviation of 1.156°.
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Affiliation(s)
- Thong Phi Nguyen
- Department of Mechanical Engineering, BK21 FOUR ERICA-ACE Centre, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan, Gyeonggi, 15588, Republic of Korea
| | - Ji Won Jung
- Department of Orthopaedic Surgery, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Yong Jin Yoo
- Department of Orthopaedic Surgery, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Sung Hoon Choi
- Department of Orthopaedic Surgery, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea.
| | - Jonghun Yoon
- Department of Mechanical Engineering, Hanyang University, 55, Hanyangdaehak-ro, Sangnok-gu, Gyeonggi-do, Ansan-si, 15588, Republic of Korea.
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Zhang K, Xu N, Guo C, Wu J. MPF-net: An effective framework for automated cobb angle estimation. Med Image Anal 2022; 75:102277. [PMID: 34753020 DOI: 10.1016/j.media.2021.102277] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 10/12/2021] [Accepted: 10/15/2021] [Indexed: 10/20/2022]
Abstract
In clinical practice, the Cobb angle is the gold standard for idiopathic scoliosis assessment, which can provide an important reference for clinicians to make surgical plan and give medical care to patients. Currently, the Cobb angle is measured manually on both anterior-posterior(AP) view X-rays and lateral(LAT) view X-rays. The clinicians first find four landmarks on each vertebra, and then they extend the line from landmarks and measure the Cobb angle by rules. The whole process is time-consuming and subjective, so that the automated Cobb angle estimation is required for efficient and reliable Cobb angle measurement. The noise in X-rays and the occlusion of vertebras are the main difficulties for automated Cobb angle estimation, and it is challenging to utilize the information between the multi-view X-rays of the same patient. Addressing these problems, in this paper, we propose an effective framework named MPF-net by using deep learning methods for automated Cobb angle estimation. We combine a vertebra detection branch and a landmark prediction branch based on the backbone convolutional neural network, which can provide the bounded area for landmark prediction. Then we propose a proposal correlation module to utilize the information between neighbor vertebras, so that we can find the vertebras hidden by ribcage and arms on LAT X-rays. We also design a feature fusion module to utilize the information in both AP and LAT X-rays for better performance. The experiment results on 2738 pair of X-rays show that our proposed MPF-net achieves precise vertebra detection and landmark prediction performance, and we get impressive 3.52 and 4.05 circular mean absolute errors on AP and LAT X-rays respectively, which is much better than previous methods. Therefore, we can provide clinicians with automated, efficient and reliable Cobb angle measurement.
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Affiliation(s)
- Kailai Zhang
- Department of Electronic Engineering, Tsinghua University, Beijing, China.
| | - Nanfang Xu
- Department of Orthopaedics, Peking University Third Hospital, Beijing, China; Engineering Research Center of Bone and Joint Precision Medicine, Beijing, China; Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Chenyi Guo
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Ji Wu
- Department of Electronic Engineering, Tsinghua University, Beijing, China.
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Meng N, Cheung JP, Wong KYK, Dokos S, Li S, Choy RW, To S, Li RJ, Zhang T. An artificial intelligence powered platform for auto-analyses of spine alignment irrespective of image quality with prospective validation. EClinicalMedicine 2022; 43:101252. [PMID: 35028544 PMCID: PMC8741432 DOI: 10.1016/j.eclinm.2021.101252] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/07/2021] [Accepted: 12/09/2021] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Assessment of spine alignment is crucial in the management of scoliosis, but current auto-analysis of spine alignment suffers from low accuracy. We aim to develop and validate a hybrid model named SpineHRNet+, which integrates artificial intelligence (AI) and rule-based methods to improve auto-alignment reliability and interpretability. METHODS From December 2019 to November 2020, 1,542 consecutive patients with scoliosis attending two local scoliosis clinics (The Duchess of Kent Children's Hospital at Sandy Bay in Hong Kong; Queen Mary Hospital in Pok Fu Lam on Hong Kong Island) were recruited. The biplanar radiographs of each patient were collected with our medical machine EOS™. The collected radiographs were recaptured using smartphones or screenshots, with deidentified images securely stored. Manually labelled landmarks and alignment parameters by a spine surgeon were considered as ground truth (GT). The data were split 8:2 to train and internally test SpineHRNet+, respectively. This was followed by a prospective validation on another 337 patients. Quantitative analyses of landmark predictions were conducted, and reliabilities of auto-alignment were assessed using linear regression and Bland-Altman plots. Deformity severity and sagittal abnormality classifications were evaluated by confusion matrices. FINDINGS SpineHRNet+ achieved accurate landmark detection with mean Euclidean distance errors of 2·78 and 5·52 pixels on posteroanterior and lateral radiographs, respectively. The mean angle errors between predictions and GT were 3·18° and 6·32° coronally and sagittally. All predicted alignments were strongly correlated with GT (p < 0·001, R2 > 0·97), with minimal overall difference visualised via Bland-Altman plots. For curve detections, 95·7% sensitivity and 88·1% specificity was achieved, and for severity classification, 88·6-90·8% sensitivity was obtained. For sagittal abnormalities, greater than 85·2-88·9% specificity and sensitivity were achieved. INTERPRETATION The auto-analysis provided by SpineHRNet+ was reliable and continuous and it might offer the potential to assist clinical work and facilitate large-scale clinical studies. FUNDING RGC Research Impact Fund (R5017-18F), Innovation and Technology Fund (ITS/404/18), and the AOSpine East Asia Fund (AOSEA(R)2019-06).
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Affiliation(s)
- Nan Meng
- Digital Health Laboratory, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 5/F, Professorial Block, Pokfulam, Hong Kong, China
| | - Jason P.Y. Cheung
- Digital Health Laboratory, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 5/F, Professorial Block, Pokfulam, Hong Kong, China
| | - Kwan-Yee K. Wong
- Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Socrates Dokos
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia
| | - Sofia Li
- Digital Health Laboratory, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 5/F, Professorial Block, Pokfulam, Hong Kong, China
| | - Richard W. Choy
- Digital Health Laboratory, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 5/F, Professorial Block, Pokfulam, Hong Kong, China
| | - Samuel To
- Digital Health Laboratory, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 5/F, Professorial Block, Pokfulam, Hong Kong, China
| | - Ricardo J. Li
- Digital Health Laboratory, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 5/F, Professorial Block, Pokfulam, Hong Kong, China
| | - Teng Zhang
- Digital Health Laboratory, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 5/F, Professorial Block, Pokfulam, Hong Kong, China
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Karpiel I, Ziębiński A, Kluszczyński M, Feige D. A Survey of Methods and Technologies Used for Diagnosis of Scoliosis. SENSORS (BASEL, SWITZERLAND) 2021; 21:8410. [PMID: 34960509 PMCID: PMC8707023 DOI: 10.3390/s21248410] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/04/2021] [Accepted: 12/09/2021] [Indexed: 02/07/2023]
Abstract
The purpose of this article is to present diagnostic methods used in the diagnosis of scoliosis in the form of a brief review. This article aims to point out the advantages of select methods. This article focuses on general issues without elaborating on problems strictly related to physiotherapy and treatment methods, which may be the subject of further discussions. By outlining and categorizing each method, we summarize relevant publications that may not only help introduce other researchers to the field but also be a valuable source for studying existing methods, developing new ones or choosing evaluation strategies.
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Affiliation(s)
- Ilona Karpiel
- Łukasiewicz Research Network—Institute of Medical Technology and Equipment, 118 Roosevelt, 41-800 Zabrze, Poland;
| | - Adam Ziębiński
- Department of Distributed Systems and Informatic Devices, Silesian University of Technology, 16 Akademicka, 44-100 Gliwice, Poland;
| | - Marek Kluszczyński
- Department of Health Sciences, Jan Dlugosz University, 4/8 Waszyngtona, 42-200 Częstochowa, Poland;
| | - Daniel Feige
- Łukasiewicz Research Network—Institute of Medical Technology and Equipment, 118 Roosevelt, 41-800 Zabrze, Poland;
- Department of Distributed Systems and Informatic Devices, Silesian University of Technology, 16 Akademicka, 44-100 Gliwice, Poland;
- PhD School, Silesian University of Technology, 2A Akademicka, 44-100 Gliwice, Poland
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Huang B, Wei Z, Tang X, Fujita H, Cai Q, Gao Y, Wu T, Zhou L. Deep learning network for medical volume data segmentation based on multi axial plane fusion. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 212:106480. [PMID: 34736168 DOI: 10.1016/j.cmpb.2021.106480] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 10/13/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE High-dimensional data generally contains more accurate information for medical image, e.g., computerized tomography (CT) data can depict the three dimensional structure of organs more precisely. However, the data in high-dimension often needs enormous computation and has high memory requirements in the deep learning convolution networks, while dimensional reduction usually leads to performance degradation. METHODS In this paper, a two-dimensional deep learning segmentation network was proposed for medical volume data based on multi-pinacoidal plane fusion to cover more information under the control of computation.This approach has conducive compatibility while using the model proposed to extract the global information between different inputs layers. RESULTS Our approach has worked in different backbone network. Using the approach, DeepUnet's Dice coefficient (Dice) and Positive Predictive Value (PPV) are 0.883 and 0.982 showing the satisfied progress. Various backbones can enjoy the profit of the method. CONCLUSIONS Through the comparison of different backbones, it can be found that the proposed network with multi-pinacoidal plane fusion can achieve better results both quantitively and qualitatively.
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Affiliation(s)
- Bo Huang
- Shanghai University of Engineering Science, 333 Longteng Road, Songjiang District, Shanghai, Shanghai, 201620, China.
| | - Ziran Wei
- Shanghai Changzheng Hospital, 415 Fengyang Road, Huangpu District, Shanghai, Shanghai, 200003, China
| | - Xianhua Tang
- Changzhou United Imaging Healthcare Surgical Technology Co.,Ltd, No.5 Longfan Road, Wujin High-Tech Industrial Development Zone, Changzhou, China
| | - Hamido Fujita
- Faculty of Information Technology, Ho Chi Minh City University of Technology(HUTECH), Ho Chi Minh City, Vietnam; i-SOMET.org Incorporated Association, Iwate 020-0104, Japan; Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI), University of Granada, Granada, Spain; College of Mathematical Sciences, Harbin Engineering University, Harbin 150001, China.
| | - Qingping Cai
- Shanghai Changzheng Hospital, 415 Fengyang Road, Huangpu District, Shanghai, Shanghai, 200003, China
| | - Yongbin Gao
- Shanghai University of Engineering Science, 333 Longteng Road, Songjiang District, Shanghai, Shanghai, 201620, China
| | - Tao Wu
- Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Liang Zhou
- Shanghai University of Medicine & Health Sciences, Shanghai, China
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Celiac trunk segmentation incorporating with additional contour constraint. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02221-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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The measurement of Cobb angle based on spine X-ray images using multi-scale convolutional neural network. Phys Eng Sci Med 2021; 44:809-821. [PMID: 34251603 DOI: 10.1007/s13246-021-01032-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 07/08/2021] [Indexed: 12/29/2022]
Abstract
Adolescent idiopathic scoliosis (AIS) is a structural spinal deformity mainly in the coronal plane and is among the most frequent deformities in children, adolescents, and young adults, with an overall prevalence of 0.47-5.2%. The Cobb angle is an objective measure to determine the progression of deformity and plays a critical role in the planning of surgical treatment. However, existing studies suggested that Cobb angle measurement is susceptible to inter- and intra-observer variability, as well as a high variability in the definition of the end vertebra. In this study, we proposed an automatic method for the spine vertebrae segmentation using Deeplab V3+, a powerful tool that has shown success in the image segmentation of other anatomical regions but spine, and Cobb angle measurement. The segmentation performance was compared to existing mainstay neural networks. Compared to U-Net, Residual U-Net and Dilated U-Net, our method using Deeplab V3+ showed the best performance in the Dice Similarity Coefficient (DSC), accuracy, sensitivity and Jaccard Index. An excellent correlation in the final Cobb angle calculation was achieved between the smallest distance point (SDP) method and two experts (> 0.95), with a small error in the angle estimation compared (MAE < 3°). The proposed method could provide a potential tool for the automatic estimation of the Cobb angle to improve the efficiency and accuracy of the treatment workflow for AIS.
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Schwartz JT, Cho BH, Tang P, Schefflein J, Arvind V, Kim JS, Doshi AH, Cho SK. Deep Learning Automates Measurement of Spinopelvic Parameters on Lateral Lumbar Radiographs. Spine (Phila Pa 1976) 2021; 46:E671-E678. [PMID: 33273436 DOI: 10.1097/brs.0000000000003830] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
STUDY DESIGN Cross-sectional database study. OBJECTIVE The objective of this study was to develop an algorithm for the automated measurement of spinopelvic parameters on lateral lumbar radiographs with comparable accuracy to surgeons. SUMMARY OF BACKGROUND DATA Sagittal alignment measurements are important for the evaluation of spinal disorders. Manual measurement methods are time-consuming and subject to rater-dependent error. Thus, a need exists to develop automated methods for obtaining sagittal measurements. Previous studies of automated measurement have been limited in accuracy, inapplicable to common plain films, or unable to measure pelvic parameters. METHODS Images from 816 patients receiving lateral lumbar radiographs were collected sequentially and used to develop a convolutional neural network (CNN) segmentation algorithm. A total of 653 (80%) of these radiographs were used to train and validate the CNN. This CNN was combined with a computer vision algorithm to create a pipeline for the fully automated measurement of spinopelvic parameters from lateral lumbar radiographs. The remaining 163 (20%) of radiographs were used to test this pipeline. Forty radiographs were selected from the test set and manually measured by three surgeons for comparison. RESULTS The CNN achieved an area under the receiver-operating curve of 0.956. Algorithm measurements of L1-S1 cobb angle, pelvic incidence, pelvic tilt, and sacral slope were not significantly different from surgeon measurement. In comparison to criterion standard measurement, the algorithm performed with a similar mean absolute difference to spine surgeons for L1-S1 Cobb angle (4.30° ± 4.14° vs. 4.99° ± 5.34°), pelvic tilt (2.14° ± 6.29° vs. 1.58° ± 5.97°), pelvic incidence (4.56° ± 5.40° vs. 3.74° ± 2.89°), and sacral slope (4.76° ± 6.93° vs. 4.75° ± 5.71°). CONCLUSION This algorithm measures spinopelvic parameters on lateral lumbar radiographs with comparable accuracy to surgeons. The algorithm could be used to streamline clinical workflow or perform large scale studies of spinopelvic parameters.Level of Evidence: 3.
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Affiliation(s)
- John T Schwartz
- Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Brian H Cho
- Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Peter Tang
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Javin Schefflein
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Varun Arvind
- Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Jun S Kim
- Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Amish H Doshi
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Samuel K Cho
- Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY
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Cina A, Bassani T, Panico M, Luca A, Masharawi Y, Brayda-Bruno M, Galbusera F. 2-step deep learning model for landmarks localization in spine radiographs. Sci Rep 2021; 11:9482. [PMID: 33947917 PMCID: PMC8096829 DOI: 10.1038/s41598-021-89102-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 04/20/2021] [Indexed: 11/25/2022] Open
Abstract
In this work we propose to use Deep Learning to automatically calculate the coordinates of the vertebral corners in sagittal x-rays images of the thoracolumbar spine and, from those landmarks, to calculate relevant radiological parameters such as L1–L5 and L1–S1 lordosis and sacral slope. For this purpose, we used 10,193 images annotated with the landmarks coordinates as the ground truth. We realized a model that consists of 2 steps. In step 1, we trained 2 Convolutional Neural Networks to identify each vertebra in the image and calculate the landmarks coordinates respectively. In step 2, we refined the localization using cropped images of a single vertebra as input to another convolutional neural network and we used geometrical transformations to map the corners to the original image. For the localization tasks, we used a differentiable spatial to numerical transform (DSNT) as the top layer. We evaluated the model both qualitatively and quantitatively on a set of 195 test images. The median localization errors relative to the vertebrae dimensions were 1.98% and 1.68% for x and y coordinates respectively. All the predicted angles were highly correlated with the ground truth, despite non-negligible absolute median errors of 1.84°, 2.43° and 1.98° for L1–L5, L1–S1 and SS respectively. Our model is able to calculate with good accuracy the coordinates of the vertebral corners and has a large potential for improving the reliability and repeatability of measurements in clinical tasks.
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Affiliation(s)
- Andrea Cina
- IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi 4, 20161, Milan, Italy.
| | - Tito Bassani
- IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi 4, 20161, Milan, Italy
| | - Matteo Panico
- IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi 4, 20161, Milan, Italy
| | - Andrea Luca
- Department of Spine Surgery III, IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi 4, 20161, Milan, Italy
| | - Youssef Masharawi
- Department of Physiotherapy, Sackler Faculty of Medicine, The Stanley Steyer School of Health Professions, Tel Aviv University, Tel Aviv, Israel
| | - Marco Brayda-Bruno
- Department of Spine Surgery III, IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi 4, 20161, Milan, Italy
| | - Fabio Galbusera
- IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi 4, 20161, Milan, Italy
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Durand WM, Lafage R, Hamilton DK, Passias PG, Kim HJ, Protopsaltis T, Lafage V, Smith JS, Shaffrey C, Gupta M, Kelly MP, Klineberg EO, Schwab F, Gum JL, Mundis G, Eastlack R, Kebaish K, Soroceanu A, Hostin RA, Burton D, Bess S, Ames C, Hart RA, Daniels AH. Artificial intelligence clustering of adult spinal deformity sagittal plane morphology predicts surgical characteristics, alignment, and outcomes. 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 2021; 30:2157-2166. [PMID: 33856551 DOI: 10.1007/s00586-021-06799-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 12/12/2020] [Accepted: 02/24/2021] [Indexed: 02/04/2023]
Abstract
PURPOSE AI algorithms have shown promise in medical image analysis. Previous studies of ASD clusters have analyzed alignment metrics-this study sought to complement these efforts by analyzing images of sagittal anatomical spinopelvic landmarks. We hypothesized that an AI algorithm would cluster preoperative lateral radiographs into groups with distinct morphology. METHODS This was a retrospective review of a multicenter, prospectively collected database of adult spinal deformity. A total of 915 patients with adult spinal deformity and preoperative lateral radiographs were included. A 2 × 3, self-organizing map-a form of artificial neural network frequently employed in unsupervised classification tasks-was developed. The mean spine shape was plotted for each of the six clusters. Alignment, surgical characteristics, and outcomes were compared. RESULTS Qualitatively, clusters C and D exhibited only mild sagittal plane deformity. Clusters B, E, and F, however, exhibited marked positive sagittal balance and loss of lumbar lordosis. Cluster A had mixed characteristics, likely representing compensated deformity. Patients in clusters B, E, and F disproportionately underwent 3-CO. PJK and PJF were particularly prevalent among clusters A and E. Among clusters B and F, patients who experienced PJK had significantly greater positive sagittal balance than those who did not. CONCLUSIONS This study clustered preoperative lateral radiographs of ASD patients into groups with highly distinct overall spinal morphology and association with sagittal alignment parameters, baseline HRQOL, and surgical characteristics. The relationship between SVA and PJK differed by cluster. This study represents significant progress toward incorporation of computer vision into clinically relevant classification systems in adult spinal deformity. LEVEL OF EVIDENCE IV Diagnostic: individual cross-sectional studies with the consistently applied reference standard and blinding.
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Affiliation(s)
- Wesley M Durand
- Department of Orthopaedic Surgery, Warren Alpert Medical School of Brown University, Alpert Medical School, Providence, Rhode Island, 1 Kettle Point Avenue, East Providence, RI, 02914, USA
| | | | - D Kojo Hamilton
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Peter G Passias
- Langone Medical Center, New York University, New York City, NY, USA
| | - Han Jo Kim
- Hospital for Special Surgery, Newyork city, NY, USA
| | | | | | - Justin S Smith
- University of Virginia Health System, Charlottesville, VA, USA
| | | | | | | | - Eric O Klineberg
- University of California, UC Davis Medical Center, Sacramento, CA, USA
| | - Frank Schwab
- Hospital for Special Surgery, Newyork city, NY, USA
| | | | | | | | - Khaled Kebaish
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | | | - Doug Burton
- Medical Center, University of Kansas, Kansas City, KS, USA
| | - Shay Bess
- Denver International Spine Center, Denver, CO, USA
| | | | - Robert A Hart
- Swedish Neuroscience Institute, Swedish Medical Center, Seattle, WA, USA
| | - Alan H Daniels
- Department of Orthopaedic Surgery, Warren Alpert Medical School of Brown University, Alpert Medical School, Providence, Rhode Island, 1 Kettle Point Avenue, East Providence, RI, 02914, USA.
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Yeh YC, Weng CH, Huang YJ, Fu CJ, Tsai TT, Yeh CY. Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs. Sci Rep 2021; 11:7618. [PMID: 33828159 PMCID: PMC8027006 DOI: 10.1038/s41598-021-87141-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 03/18/2021] [Indexed: 12/21/2022] Open
Abstract
Human spinal balance assessment relies considerably on sagittal radiographic parameter measurement. Deep learning could be applied for automatic landmark detection and alignment analysis, with mild to moderate standard errors and favourable correlations with manual measurement. In this study, based on 2210 annotated images of various spinal disease aetiologies, we developed deep learning models capable of automatically locating 45 anatomic landmarks and subsequently generating 18 radiographic parameters on a whole-spine lateral radiograph. In the assessment of model performance, the localisation accuracy and learning speed were the highest for landmarks in the cervical area, followed by those in the lumbosacral, thoracic, and femoral areas. All the predicted radiographic parameters were significantly correlated with ground truth values (all p < 0.001). The human and artificial intelligence comparison revealed that the deep learning model was capable of matching the reliability of doctors for 15/18 of the parameters. The proposed automatic alignment analysis system was able to localise spinal anatomic landmarks with high accuracy and to generate various radiographic parameters with favourable correlations with manual measurements.
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Affiliation(s)
- Yu-Cheng Yeh
- Department of Orthopaedic Surgery, Spine Division, Bone and Joint Research Center, Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Taoyuan, Taiwan, ROC
| | - Chi-Hung Weng
- aetherAI Co., Ltd., 9F., No.3-2, Yuanqu St., Nangang Dist., Taipei City, 115, Taiwan, ROC
| | - Yu-Jui Huang
- Department of Orthopaedic Surgery, Spine Division, Bone and Joint Research Center, Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Taoyuan, Taiwan, ROC
| | - Chen-Ju Fu
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Taoyuan, Taiwan, ROC
| | - Tsung-Ting Tsai
- Department of Orthopaedic Surgery, Spine Division, Bone and Joint Research Center, Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Taoyuan, Taiwan, ROC
| | - Chao-Yuan Yeh
- aetherAI Co., Ltd., 9F., No.3-2, Yuanqu St., Nangang Dist., Taipei City, 115, Taiwan, ROC.
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Merali ZA, Colak E, Wilson JR. Applications of Machine Learning to Imaging of Spinal Disorders: Current Status and Future Directions. Global Spine J 2021; 11:23S-29S. [PMID: 33890805 PMCID: PMC8076811 DOI: 10.1177/2192568220961353] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES We aim to describe current progress in the application of artificial intelligence and machine learning technology to provide automated analysis of imaging in patients with spinal disorders. METHODS A literature search utilizing the PubMed database was performed. Relevant studies from all the evidence levels have been included. RESULTS Within spine surgery, artificial intelligence and machine learning technologies have achieved near-human performance in narrow image classification tasks on specific datasets in spinal degenerative disease, spinal deformity, spine trauma, and spine oncology. CONCLUSION Although substantial challenges remain to be overcome it is clear that artificial intelligence and machine learning technology will influence the practice of spine surgery in the future.
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Affiliation(s)
- Zamir A. Merali
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Errol Colak
- Department of Medical Imaging, University of Toronto, St. Michael’s Hospital, 30 Bond St, Toronto, ON, M5B 1W8, Canada
| | - Jefferson R. Wilson
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Neurosurgery, St. Michael’s Hospital, Toronto, Ontario, Canada
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Mallow GM, Siyaji ZK, Galbusera F, Espinoza-Orías AA, Giers M, Lundberg H, Ames C, Karppinen J, Louie PK, Phillips FM, Pourzal R, Schwab J, Sciubba DM, Wang JC, Wilke HJ, Williams FMK, Mohiuddin SA, Makhni MC, Shepard NA, An HS, Samartzis D. Intelligence-Based Spine Care Model: A New Era of Research and Clinical Decision-Making. Global Spine J 2021; 11:135-145. [PMID: 33251858 PMCID: PMC7882816 DOI: 10.1177/2192568220973984] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- G. Michael Mallow
- Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA
- The International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Zakariah K. Siyaji
- Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA
- The International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | | | - Alejandro A. Espinoza-Orías
- Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA
- The International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Morgan Giers
- School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, OR, USA
| | - Hannah Lundberg
- Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Christopher Ames
- Department of Neurosurgery, University of California San Francisco, CA, USA
| | - Jaro Karppinen
- Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
| | | | - Frank M. Phillips
- Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA
- The International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Robin Pourzal
- Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Joseph Schwab
- Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA
| | - Daniel M. Sciubba
- Department of Neurosurgery, Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey C. Wang
- Department of Orthopaedic Surgery, University of Southern California, Los Angeles, CA, USA
| | - Hans-Joachim Wilke
- Institute of Orthopaedic Research and Biomechanics, Centre for Trauma Research Ulm, Ulm University Medical Centre, Ulm, Germany
| | - Frances M. K. Williams
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | | | - Melvin C. Makhni
- Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA
| | - Nicholas A. Shepard
- Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA
- The International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Howard S. An
- Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA
- The International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
| | - Dino Samartzis
- Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA
- The International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA
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Nguyen TP, Chae DS, Park SJ, Kang KY, Yoon J. Deep learning system for Meyerding classification and segmental motion measurement in diagnosis of lumbar spondylolisthesis. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102371] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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He Z, Wang Y, Qin X, Yin R, Qiu Y, He K, Zhu Z. Classification of neurofibromatosis-related dystrophic or nondystrophic scoliosis based on image features using Bilateral CNN. Med Phys 2021; 48:1571-1583. [PMID: 33438284 DOI: 10.1002/mp.14719] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 12/29/2020] [Accepted: 12/30/2020] [Indexed: 12/14/2022] Open
Abstract
PURPOSE We developed a system that can automatically classify cases of scoliosis secondary to neurofibromatosis type 1 (NF1-S) using deep learning algorithms (DLAs) and improve the accuracy and effectiveness of classification, thereby assisting surgeons with the auxiliary diagnosis. METHODS Comprehensive experiments in NF1 classification were performed based on a dataset consisting 211 NF1-S (131 dystrophic and 80 nondystrophic NF1-S) patients. Additionally, 100 congenital scoliosis (CS), 100 adolescent idiopathic scoliosis (AIS) patients, and 114 normal controls were used for experiments in primary classification. For identification of NF1-S with nondystrophic or dystrophic curves, we devised a novel network (i.e., Bilateral convolutional neural network [CNN]) utilizing a bilinear-like operation to discover the similar interest features between whole spine AP and lateral x-ray images. The performance of Bilateral CNN was compared with spine surgeons, conventional DLAs (i.e., VGG-16, ResNet-50, and Bilinear CNN [BCNN]), recently proposed DLAs (i.e., ShuffleNet, MobileNet, and EfficientNet), and Two-path BCNN which was the extension of BCNN using AP and lateral x-ray images as inputs. RESULTS In NF1 classification, our proposed Bilateral CNN with 80.36% accuracy outperformed the other seven DLAs ranging from 61.90% to 76.19% with fivefold cross-validation. It also outperformed the spine surgeons (with an average accuracy of 77.5% for the senior surgeons and 65.0% for the junior surgeons). Our method is highly generalizable due to the proposed methodology and data augmentation. Furthermore, the heatmaps extracted by Bilateral CNN showed curve pattern and morphology of ribs and vertebrae contributing most to the classification results. In primary classification, our proposed method with an accuracy of 87.92% also outperformed all the other methods with varied accuracies between 52.58% and 83.35% with fivefold cross-validation. CONCLUSIONS The proposed Bilateral CNN can automatically capture representative features for classifying NF1-S utilizing AP and lateral x-ray images, leading to a relatively good performance. Moreover, the proposed method can identify other spine deformities for auxiliary diagnosis.
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Affiliation(s)
- Zhong He
- Department of Spine Surgery, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Yimu Wang
- Department of Computer Science and Technology, Nanjing University, Nanjing, China
| | - Xiaodong Qin
- Department of Spine Surgery, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Rui Yin
- Department of Spine Surgery, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Yong Qiu
- Department of Spine Surgery, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Kelei He
- Medical School of Nanjing University, Nanjing, China.,National Institute of Healthcare Data Science at Nanjing University, Nanjing, China
| | - Zezhang Zhu
- Department of Spine Surgery, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
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Chen K, Zhai X, Sun K, Wang H, Yang C, Li M. A narrative review of machine learning as promising revolution in clinical practice of scoliosis. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:67. [PMID: 33553360 PMCID: PMC7859734 DOI: 10.21037/atm-20-5495] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Machine learning (ML), as an advanced domain of artificial intelligence (AI), is progressively changing our view of the world. By implementing its algorithms, our ability to detect previously undiscoverable patterns in data has the potential to revolutionize predictive analytics. Scoliosis, as a relatively specialized branch in the spine field, mainly covers the pediatric, adult and the elderly populations, and its diagnosis and treatment remain difficult. With recent efforts and interdisciplinary cooperation, ML has been widely applied to investigate issues related to scoliosis, and surprisingly augment a surgeon's ability in clinical practice related to scoliosis. Meanwhile, ML models penetrate in every stage of the clinical practice procedure of scoliosis. In this review, we first present a brief description of the application of ML in the clinical practice procedures regarding scoliosis, including screening, diagnosis and classification, surgical decision making, intraoperative manipulation, complication prediction, prognosis prediction and rehabilitation. Meanwhile, the ML models and specific applications adopted are presented. Additionally, current limitations and future directions are briefly discussed regarding its use in the field of scoliosis. We believe that the implementation of ML is a promising revolution to assist surgeons in all aspects of clinical practice related to scoliosis in the near future.
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Affiliation(s)
- Kai Chen
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
| | - Xiao Zhai
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
| | - Kaiqiang Sun
- Department of Orthopedics, Shanghai Changzheng Hospital, Shanghai, China
| | - Haojue Wang
- Basic medicine college, Navy Medical University, Shanghai, China
| | - Changwei Yang
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
| | - Ming Li
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, China
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