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Chang BC, Renslo J, Dong Q, Johnston SK, Perry J, Haynor DR, Luo G, Lane NE, Jarvik JG, Cross NM. Using an Ensemble of Segmentation Methods to Detect Vertebral Bodies on Radiographs. AJNR Am J Neuroradiol 2024; 45:1512-1520. [PMID: 39209486 PMCID: PMC11448993 DOI: 10.3174/ajnr.a8343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 05/03/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND AND PURPOSE Vertebral compression fractures may indicate osteoporosis but are underdiagnosed and underreported by radiologists. We have developed an ensemble of vertebral body (VB) segmentation models for lateral radiographs as a critical component of an automated, opportunistic screening tool. Our goal is to detect the approximate location of thoracic and lumbar VBs, including fractured vertebra, on lateral radiographs. MATERIALS AND METHODS The Osteoporotic Fractures in Men Study (MrOS) data set includes spine radiographs of 5994 men aged ≥65 years from 6 clinical centers. Two segmentation models, U-Net and Mask-RCNN (Region-based Convolutional Neural Network), were independently trained on the MrOS data set retrospectively, and an ensemble was created by combining them. Primary performance metrics for VB detection success included precision, recall, and F1 score for object detection on a held-out test set. Intersection over union (IoU) and Dice coefficient were also calculated as secondary metrics of performance for the test set. A separate external data set from a quaternary health care enterprise was acquired to test generalizability, comprising diagnostic clinical radiographs from men and women aged ≥65 years. RESULTS The trained models achieved F1 score of U-Net = 83.42%, Mask-RCNN = 86.30%, and ensemble = 88.34% in detecting all VBs, and F1 score of U-Net = 87.88%, Mask-RCNN = 92.31%, and ensemble = 97.14% in detecting severely fractured vertebrae. The trained models achieved an average IoU per VB of 0.759 for U-Net and 0.709 for Mask-RCNN. The trained models achieved F1 score of U-Net = 81.11%, Mask-RCNN = 79.24%, and ensemble = 87.72% in detecting all VBs in the external data set. CONCLUSIONS An ensemble model combining predictions from U-Net and Mask-RCNN resulted in the best performance in detecting VBs on lateral radiographs and generalized well to an external data set. This model could be a key component of a pipeline to detect fractures on all vertebrae in a radiograph in an automated, opportunistic screening tool under development.
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Affiliation(s)
- Brian C Chang
- From the Departments of Biomedical Informatics and Medical Education (B.C.C., Q.D., G.L.), University of Washington, Seattle, Washington
| | - Jonathan Renslo
- Keck School of Medicine (J.R.), University of Southern California, Los Angeles, California
| | - Qifei Dong
- From the Departments of Biomedical Informatics and Medical Education (B.C.C., Q.D., G.L.), University of Washington, Seattle, Washington
| | - Sandra K Johnston
- Departments of Radiology (S.K.J., D.R.H., J.G.J., N.M.C.), University of Washington, Seattle, Washington
| | - Jessica Perry
- Departments of Biostatistics (J.P.), University of Washington, Seattle, Washington
| | - David R Haynor
- Departments of Radiology (S.K.J., D.R.H., J.G.J., N.M.C.), University of Washington, Seattle, Washington
| | - Gang Luo
- From the Departments of Biomedical Informatics and Medical Education (B.C.C., Q.D., G.L.), University of Washington, Seattle, Washington
| | - Nancy E Lane
- Department of Medicine (N.E.L.), Rheumatology, University of California Davis, Davis, California
| | - Jeffrey G Jarvik
- Departments of Radiology (S.K.J., D.R.H., J.G.J., N.M.C.), University of Washington, Seattle, Washington
- Departments of Neurological Surgery (J.G.J.), University of Washington, Seattle, Washington
| | - Nathan M Cross
- Departments of Radiology (S.K.J., D.R.H., J.G.J., N.M.C.), University of Washington, Seattle, Washington
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Zhang W, Guo C, Yao D, Lan W, Zhou M, Huang Z, Kong Q. Effects and mechanisms of berberine-loaded tetrahedral framework nucleic acids on osteoporosis. J Mater Chem B 2024. [PMID: 39319556 DOI: 10.1039/d4tb01691b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2024]
Abstract
Osteoporosis is a disease that plagues the world, the complications of which, spinal fractures and hip fractures, seriously endanger the health of the elderly. At present, the main treatment for osteoporosis is anti-osteoporosis drugs, some of which, however, have poor efficacy and side effects. Thus, the development of new drugs for the treatment of osteoporosis is of clinical significance. Berberine (BBR) has a good therapeutic effect on osteoporosis, but its poor solubility and instability limit its effect. Tetrahedral framework nucleic acids (tFNAs) are a novel nano-drug delivery tool, and their good cell entry ability and stability make them an excellent carrier. Therefore, a new nanocomplex T-BBR was synthesized by carrying BBR with tFNAs. Compared with BBR, T-BBR showed better osteogenic and anti-osteoporosis abilities in both in vivo and in vitro experiments, the mechanism of which was realized through the Wnt signaling pathway.
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Affiliation(s)
- Weifei Zhang
- Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China, 610041.
| | - Chuan Guo
- Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China, 610041.
| | - Dengbo Yao
- Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China, 610041.
| | - Weiqiang Lan
- Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China, 610041.
| | - Mi Zhou
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, P. R. China.
| | - Zhangheng Huang
- Department of Orthopaedics (spine surgery), The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Qingquan Kong
- Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China, 610041.
- Department of Orthopedics Surgery, Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region, Chengdu, Sichuan, China
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Cross NM, Perry J, Dong Q, Luo G, Renslo J, Chang BC, Lane NE, Marshall L, Johnston SK, Haynor DR, Jarvik JG, Heagerty PJ. Subject-level spinal osteoporotic fracture prediction combining deep learning vertebral outputs and limited demographic data. Arch Osteoporos 2024; 19:87. [PMID: 39256211 DOI: 10.1007/s11657-024-01433-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 07/26/2024] [Indexed: 09/12/2024]
Abstract
Automated screening for vertebral fractures could improve outcomes. We achieved an AUC-ROC = 0.968 for the prediction of moderate to severe fracture using a GAM with age and three maximal vertebral body scores of fracture from a convolutional neural network. Maximal fracture scores resulted in a performant model for subject-level fracture prediction. Combining individual deep learning vertebral body fracture scores and demographic covariates for subject-level classification of osteoporotic fracture achieved excellent performance (AUC-ROC of 0.968) on a large dataset of radiographs with basic demographic data. PURPOSE Osteoporotic vertebral fractures are common and morbid. Automated opportunistic screening for incidental vertebral fractures from radiographs, the highest volume imaging modality, could improve osteoporosis detection and management. We consider how to form patient-level fracture predictions and summarization to guide management, using our previously developed vertebral fracture classifier on segmented radiographs from a prospective cohort study of US men (MrOS). We compare the performance of logistic regression (LR) and generalized additive models (GAM) with combinations of individual vertebral scores and basic demographic covariates. METHODS Subject-level LR and GAM models were created retrospectively using all fracture predictions or summary variables such as order statistics, adjacent vertebral interactions, and demographic covariates (age, race/ethnicity). The classifier outputs for 8663 vertebrae from 1176 thoracic and lumbar radiographs in 669 subjects were divided by subject to perform stratified fivefold cross-validation. Models were assessed using multiple metrics, including receiver operating characteristic (ROC) and precision-recall (PR) curves. RESULTS The best model (AUC-ROC = 0.968) was a GAM using the top three maximum vertebral fracture scores and age. Using top-ranked scores only, rather than all vertebral scores, improved performance for both model classes. Adding age, but not ethnicity, to the GAMs improved performance slightly. CONCLUSION Maximal vertebral fracture scores resulted in the highest-performing models. While combining multiple vertebral body predictions risks decreasing specificity, our results demonstrate that subject-level models maintain good predictive performance. Thresholding strategies can be used to control sensitivity and specificity as clinically appropriate.
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Affiliation(s)
- Nathan M Cross
- Department of Radiology, University of Washington, 1959 NE Pacific Street, Box 357115, Seattle, WA, 98195-7115, USA.
| | - Jessica Perry
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Qifei Dong
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98195, USA
| | - Gang Luo
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98195, USA
| | - Jonathan Renslo
- Department of Medical Education, University of Southern California, Los Angeles, CA, 90042, USA
| | - Brian C Chang
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98195, USA
| | - Nancy E Lane
- Department of Medicine, University of California -Davis, Sacramento, CA, 95817, USA
| | - Lynn Marshall
- School of Public Health, Oregon Health and Science University-Portland State University, Portland, OR, 97239, USA
| | - Sandra K Johnston
- Department of Radiology, University of Washington, 1959 NE Pacific Street, Box 357115, Seattle, WA, 98195-7115, USA
| | - David R Haynor
- Department of Radiology, University of Washington, 1959 NE Pacific Street, Box 357115, Seattle, WA, 98195-7115, USA
| | - Jeffrey G Jarvik
- Departments of Radiology and Neurological Surgery, University of Washington, Seattle, WA, 98104-2499, USA
| | - Patrick J Heagerty
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
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Li Y, Liang Z, Li Y, Cao Y, Zhang H, Dong B. Machine learning value in the diagnosis of vertebral fractures: A systematic review and meta-analysis. Eur J Radiol 2024; 181:111714. [PMID: 39241305 DOI: 10.1016/j.ejrad.2024.111714] [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: 06/04/2024] [Revised: 07/28/2024] [Accepted: 08/30/2024] [Indexed: 09/09/2024]
Abstract
PURPOSE To evaluate the diagnostic accuracy of machine learning (ML) in detecting vertebral fractures, considering varying fracture classifications, patient populations, and imaging approaches. METHOD A systematic review and meta-analysis were conducted by searching PubMed, Embase, Cochrane Library, and Web of Science up to December 31, 2023, for studies using ML for vertebral fracture diagnosis. Bias risk was assessed using QUADAS-2. A bivariate mixed-effects model was used for the meta-analysis. Meta-analyses were performed according to five task types (vertebral fractures, osteoporotic vertebral fractures, differentiation of benign and malignant vertebral fractures, differentiation of acute and chronic vertebral fractures, and prediction of vertebral fractures). Subgroup analyses were conducted by different ML models (including ML and DL) and modeling methods (including CT, X-ray, MRI, and clinical features). RESULTS Eighty-one studies were included. ML demonstrated a diagnostic sensitivity of 0.91 and specificity of 0.95 for vertebral fractures. Subgroup analysis showed that DL (SROC 0.98) and CT (SROC 0.98) performed best overall. For osteoporotic fractures, ML showed a sensitivity of 0.93 and specificity of 0.96, with DL (SROC 0.99) and X-ray (SROC 0.99) performing better. For differentiating benign from malignant fractures, ML achieved a sensitivity of 0.92 and specificity of 0.93, with DL (SROC 0.96) and MRI (SROC 0.97) performing best. For differentiating acute from chronic vertebral fractures, ML showed a sensitivity of 0.92 and specificity of 0.93, with ML (SROC 0.96) and CT (SROC 0.97) performing best. For predicting vertebral fractures, ML had a sensitivity of 0.76 and specificity of 0.87, with ML (SROC 0.80) and clinical features (SROC 0.86) performing better. CONCLUSIONS ML, especially DL models applied to CT, MRI, and X-ray, shows high diagnostic accuracy for vertebral fractures. ML also effectively predicts osteoporotic vertebral fractures, aiding in tailored prevention strategies. Further research and validation are required to confirm ML's clinical efficacy.
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Affiliation(s)
- Yue Li
- Pain Ward of Rehabilitation Department, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710054, PR China
| | - Zhuang Liang
- Pain Ward of Rehabilitation Department, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710054, PR China
| | - Yingchun Li
- Pain Ward of Rehabilitation Department, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710054, PR China
| | - Yang Cao
- Pain Ward of Rehabilitation Department, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710054, PR China
| | - Hui Zhang
- Pain Ward of Rehabilitation Department, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710054, PR China
| | - Bo Dong
- Pain Ward of Rehabilitation Department, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710054, PR China.
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Curl PK, Jacob A, Bresnahan B, Cross NM, Jarvik JG. Cost-Effectiveness of Artificial Intelligence-Based Opportunistic Compression Fracture Screening of Existing Radiographs. J Am Coll Radiol 2024; 21:1489-1496. [PMID: 38527641 PMCID: PMC11381181 DOI: 10.1016/j.jacr.2023.11.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/28/2023] [Accepted: 11/22/2023] [Indexed: 03/27/2024]
Abstract
PURPOSE Osteoporotic vertebral compression fractures (OVCFs) are a highly prevalent source of morbidity and mortality, and preventive treatment has been demonstrated to be both effective and cost effective. To take advantage of the information available on existing chest and abdominal radiographs, the authors' study group has developed software to access these radiographs for OVCFs with high sensitivity and specificity using an established artificial intelligence deep learning algorithm. The aim of this analysis was to assess the potential cost-effectiveness of implementing this software. METHODS A deterministic expected-value cost-utility model was created, combining a tree model and a Markov model, to compare the strategies of opportunistic screening for OVCFs against usual care. Total costs and total quality-adjusted life-years were calculated for each strategy. Screening and treatment costs were considered from a limited societal perspective, at 2022 prices. RESULTS In the base case, assuming a cost of software implantation of $10 per patient screened, the screening strategy dominated the nonscreening strategy: it resulted in lower cost and increased quality-adjusted life-years. The lower cost was due primarily to the decreased costs associated with fracture treatment and decreased probability of requiring long-term care in patients who received preventive treatment. The screening strategy was dominant up to a cost of $46 per patient screened. CONCLUSIONS Artificial intelligence-based opportunistic screening for OVCFs on existing radiographs can be cost effective from a societal perspective.
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Affiliation(s)
- Patti K Curl
- Neuroradiology Medical Director, Harborview Medical Center, University of Washington, Seattle, Washington.
| | - Ayden Jacob
- University of Washington, Seattle, Washington
| | | | - Nathan M Cross
- Interim Vice Chair of Informatics, Radiology, VA Ventures AI & Informatics Specialist, University of Washington, Seattle, Washington
| | - Jeffrey G Jarvik
- Co-Director, Comparative Effectiveness, Cost and Outcomes Research Center, and Director, University of Washington Clinical Learning, Evidence, and Research Center for Musculoskeletal Disorders, University of Washington School of Medicine, Seattle, Washington
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Zhu X, Liu D, Liu L, Guo J, Li Z, Zhao Y, Wu T, Liu K, Liu X, Pan X, Qi L, Zhang Y, Cheng L, Chen B. Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi-Center Study. Orthop Surg 2024; 16:2052-2065. [PMID: 38952050 PMCID: PMC11293932 DOI: 10.1111/os.14155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/06/2024] [Accepted: 06/09/2024] [Indexed: 07/03/2024] Open
Abstract
BACKGROUND The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X-ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicators related to osteoporosis and imaging factors that can better reflect bone quality, such as computed tomography (CT). OBJECTIVE To construct a novel predicting model based on bone turn-over markers and CT to identify patients who were more inclined to suffer spine refracture. METHODS CT images and clinical information of 383 patients (training set = 240 cases of osteoporotic vertebral compression fractures (OVCF), validation set = 63, test set = 80) were retrospectively collected from January 2015 to October 2022 at three medical centers. The U-net model was adopted to automatically segment ROI. Three-dimensional (3D) cropping of all spine regions was used to achieve the final ROI regions including 3D_Full and 3D_RoiOnly. We used the Densenet 121-3D model to model the cropped region and simultaneously build a T-NIPT prediction model. Diagnostics of deep learning models were assessed by constructing ROC curves. We generated calibration curves to assess the calibration performance. Additionally, decision curve analysis (DCA) was used to assess the clinical utility of the predictive models. RESULTS The performance of the test model is comparable to its performance on the training set (dice coefficients of 0.798, an mIOU of 0.755, an SA of 0.767, and an OS of 0.017). Univariable and multivariable analysis indicate that T_P1NT was an independent risk factor for refracture. The performance of predicting refractures in different ROI regions showed that 3D_Full model exhibits the highest calibration performance, with a Hosmer-Lemeshow goodness-of-fit (HL) test statistic exceeding 0.05. The analysis of the training and test sets showed that the 3D_Full model, which integrates clinical and deep learning results, demonstrated superior performance with significant improvement (p-value < 0.05) compared to using clinical features independently or using only 3D_RoiOnly. CONCLUSION T_P1NT was an independent risk factor of refracture. Our 3D-FULL model showed better performance in predicting high-risk population of spine refracture than other models and junior doctors do. This model can be applicable to real-world translation due to its automatic segmentation and detection.
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Affiliation(s)
- Xuetao Zhu
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Dejian Liu
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Lian Liu
- Department of Emergency SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Jingxuan Guo
- Department of anesthesiologyAffiliated Hospital of Shandong University of Traditional Chinese MedicineJinanChina
| | - Zedi Li
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Yixiang Zhao
- Department of Orthopaedic SurgeryYantaishan HospitalYantaiChina
| | - Tianhao Wu
- Department of Hepatopancreatobiliary SurgeryGraduate School of Dalian Medical UniversityDalianChina
| | - Kaiwen Liu
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Xinyu Liu
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Xin Pan
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Lei Qi
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Yuanqiang Zhang
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Lei Cheng
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
| | - Bin Chen
- Department of Orthopaedic SurgeryQilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong UniversityJinanP. R. China
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Yıldız Potter İ, Yeritsyan D, Rodriguez EK, Wu JS, Nazarian A, Vaziri A. Detection and Localization of Spine Disorders from Plain Radiography. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01175-x. [PMID: 38937344 DOI: 10.1007/s10278-024-01175-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/16/2024] [Accepted: 06/09/2024] [Indexed: 06/29/2024]
Abstract
Spine disorders can cause severe functional limitations, including back pain, decreased pulmonary function, and increased mortality risk. Plain radiography is the first-line imaging modality to diagnose suspected spine disorders. Nevertheless, radiographical appearance is not always sufficient due to highly variable patient and imaging parameters, which can lead to misdiagnosis or delayed diagnosis. Employing an accurate automated detection model can alleviate the workload of clinical experts, thereby reducing human errors, facilitating earlier detection, and improving diagnostic accuracy. To this end, deep learning-based computer-aided diagnosis (CAD) tools have significantly outperformed the accuracy of traditional CAD software. Motivated by these observations, we proposed a deep learning-based approach for end-to-end detection and localization of spine disorders from plain radiographs. In doing so, we took the first steps in employing state-of-the-art transformer networks to differentiate images of multiple spine disorders from healthy counterparts and localize the identified disorders, focusing on vertebral compression fractures (VCF) and spondylolisthesis due to their high prevalence and potential severity. The VCF dataset comprised 337 images, with VCFs collected from 138 subjects and 624 normal images collected from 337 subjects. The spondylolisthesis dataset comprised 413 images, with spondylolisthesis collected from 336 subjects and 782 normal images collected from 413 subjects. Transformer-based models exhibited 0.97 Area Under the Receiver Operating Characteristic Curve (AUC) in VCF detection and 0.95 AUC in spondylolisthesis detection. Further, transformers demonstrated significant performance improvements against existing end-to-end approaches by 4-14% AUC (p-values < 10-13) for VCF detection and by 14-20% AUC (p-values < 10-9) for spondylolisthesis detection.
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Affiliation(s)
| | - Diana Yeritsyan
- Beth Israel Deaconess Medical Center (BIDMC), Carl J. Shapiro Department of Orthopedic Surgery, Harvard Medical School, 330 Brookline Avenue, Stoneman 10, Boston, MA, 02215, USA
- Musculoskeletal Translational Innovation Initiative, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, RN123, USA
| | - Edward K Rodriguez
- Beth Israel Deaconess Medical Center (BIDMC), Carl J. Shapiro Department of Orthopedic Surgery, Harvard Medical School, 330 Brookline Avenue, Stoneman 10, Boston, MA, 02215, USA
- Musculoskeletal Translational Innovation Initiative, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, RN123, USA
| | - Jim S Wu
- Department of Radiology, Massachusetts General Brigham (MGB), Harvard Medical School, 75 Francis Street, Boston, MA, 02215, USA
| | - Ara Nazarian
- Beth Israel Deaconess Medical Center (BIDMC), Carl J. Shapiro Department of Orthopedic Surgery, Harvard Medical School, 330 Brookline Avenue, Stoneman 10, Boston, MA, 02215, USA
- Musculoskeletal Translational Innovation Initiative, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, RN123, USA
- Department of Orthopaedics Surgery, Yerevan State University, 0025, Yerevan, Armenia
| | - Ashkan Vaziri
- BioSensics, LLC, 57 Chapel Street, Newton, MA, 02458, USA
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Kim YR, Yoon YS, Cha JG. Opportunistic Screening for Acute Vertebral Fractures on a Routine Abdominal or Chest Computed Tomography Scans Using an Automated Deep Learning Model. Diagnostics (Basel) 2024; 14:781. [PMID: 38611694 PMCID: PMC11011775 DOI: 10.3390/diagnostics14070781] [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: 03/13/2024] [Revised: 03/31/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024] Open
Abstract
OBJECTIVES To develop an opportunistic screening model based on a deep learning algorithm to detect recent vertebral fractures in abdominal or chest CTs. MATERIALS AND METHODS A total of 1309 coronal reformatted images (504 with a recent fracture from 119 patients, and 805 without fracture from 115 patients), from torso CTs, performed from September 2018 to April 2022, on patients who also had a spine MRI within two months, were included. Two readers participated in image selection and manually labeled the fractured segment on each selected image with Neuro-T (version 2.3.3; Neurocle Inc.) software. We split the images randomly into the training and internal test set (labeled: unlabeled = 480:700) and the secondary interval validation set (24:105). For the observer study, three radiologists reviewed the CT images in the external test set with and without deep learning assistance and scored the likelihood of an acute fracture in each image independently. RESULTS For the training and internal test sets, the AI achieved a 99.86% test accuracy, 91.22% precision, and 89.18% F1 score for detection of recent fracture. Then, in the secondary internal validation set, it achieved 99.90%, 74.93%, and 78.30%, respectively. In the observer study, with the assistance of the deep learning algorithm, a significant improvement was observed in the radiology resident's accuracy, from 92.79% to 98.2% (p = 0.04). CONCLUSION The model showed a high level of accuracy in the test set and also the internal validation set. If this algorithm is applied opportunistically to daily torso CT evaluation, it will be helpful for the early detection of fractures that require treatment.
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Affiliation(s)
- Ye Rin Kim
- Department of Radiology, College of Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University, Bucheon 14584, Republic of Korea
| | - Yu Sung Yoon
- Department of Radiology, School of Medicine, Kyungpook National University Hospital, Kyungpook National University, Daegu 41944, Republic of Korea
| | - Jang Gyu Cha
- Department of Radiology, College of Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University, Bucheon 14584, Republic of Korea
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Zhang J, Xia L, Liu J, Niu X, Tang J, Xia J, Liu Y, Zhang W, Liang Z, Zhang X, Tang G, Zhang L. Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images. Front Endocrinol (Lausanne) 2024; 15:1370838. [PMID: 38606087 PMCID: PMC11007145 DOI: 10.3389/fendo.2024.1370838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 03/15/2024] [Indexed: 04/13/2024] Open
Abstract
Purpose To develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs). Material and methods The study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the "One-vs-Rest" strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model. Results Following pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P<0.05). Utilizing the binary "One-vs-Rest" strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. Predicting Class 1 yielded an AUC of 0.945 and accuracy of 0.875, while for Class 2, the AUC and accuracy were 0.809 and 0.692, respectively. Conclusion The DLR model, based on the RadImageNet dataset, outperformed the ImageNet model in predicting the classification of OVFs, with generalizability confirmed in the prospective validation set.
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Affiliation(s)
- Jun Zhang
- Department of Radiology, Shanghai Tenth People’s Hospital, Clinical Medical College of Nanjing Medical University, Shanghai, China
- Department of Radiology, Sir RunRun Hospital, Nanjing Medical University, Nanjing, China
| | - Liang Xia
- Department of Radiology, Sir RunRun Hospital, Nanjing Medical University, Nanjing, China
| | - Jiayi Liu
- Department of Radiology, Sir RunRun Hospital, Nanjing Medical University, Nanjing, China
| | - Xiaoying Niu
- Department of Neonates, Dongfeng General Hospital of National Medicine, Hubei University of Medicine, Shiyan, China
| | - Jun Tang
- Department of Radiology, The Affiliated Taizhou People’s Hospital of Nanjing Medical University, Taizhou, China
| | - Jianguo Xia
- Department of Radiology, The Affiliated Taizhou People’s Hospital of Nanjing Medical University, Taizhou, China
| | - Yongkang Liu
- Department of Radiology, Jiangsu Provincial Hospital of Traditional Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Weixiao Zhang
- Department of Radiology, Sir RunRun Hospital, Nanjing Medical University, Nanjing, China
| | - Zhipeng Liang
- Department of Radiology, Sir RunRun Hospital, Nanjing Medical University, Nanjing, China
| | - Xueli Zhang
- Department of Radiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Guangyu Tang
- Department of Radiology, Shanghai Tenth People’s Hospital, Clinical Medical College of Nanjing Medical University, Shanghai, China
- Department of Radiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Lin Zhang
- Department of Radiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
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10
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Li Z, Zhao W, Lin X, Li F. AI algorithms for accurate prediction of osteoporotic fractures in patients with diabetes: an up-to-date review. J Orthop Surg Res 2023; 18:956. [PMID: 38087332 PMCID: PMC10714483 DOI: 10.1186/s13018-023-04446-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 12/05/2023] [Indexed: 12/18/2023] Open
Abstract
Osteoporotic fractures impose a substantial burden on patients with diabetes due to their unique characteristics in bone metabolism, limiting the efficacy of conventional fracture prediction tools. Artificial intelligence (AI) algorithms have shown great promise in predicting osteoporotic fractures. This review aims to evaluate the application of traditional fracture prediction tools (FRAX, QFracture, and Garvan FRC) in patients with diabetes and osteoporosis, review AI-based fracture prediction achievements, and assess the potential efficiency of AI algorithms in this population. This comprehensive literature search was conducted in Pubmed and Web of Science. We found that conventional prediction tools exhibit limited accuracy in predicting fractures in patients with diabetes and osteoporosis due to their distinct bone metabolism characteristics. Conversely, AI algorithms show remarkable potential in enhancing predictive precision and improving patient outcomes. However, the utilization of AI algorithms for predicting osteoporotic fractures in diabetic patients is still in its nascent phase, further research is required to validate their efficacy and assess the potential advantages of their application in clinical practice.
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Affiliation(s)
- Zeting Li
- Department of Endocrinology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Wen Zhao
- The Reproductive Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiahong Lin
- Department of Endocrinology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
| | - Fangping Li
- Department of Endocrinology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
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11
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Wu Y, Chao J, Bao M, Zhang N. Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis. BMJ Open 2023; 13:e071430. [PMID: 38070927 PMCID: PMC10728980 DOI: 10.1136/bmjopen-2022-071430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 11/06/2023] [Indexed: 12/18/2023] Open
Abstract
OBJECTIVES Early identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis. METHODS Relevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models and fracture sites. RESULTS Fifty-three studies were included in our meta-analysis, involving 15 209 268 patients, 86 prediction models specifically developed for the osteoporosis population and 41 validation sets. The most commonly used predictors in these models encompassed age, BMI, past fracture history, bone mineral density T-score, history of falls, BMD, radiomics data, weight, height, gender and other chronic diseases. Overall, the pooled C-index of ML was 0.75 (95% CI: 0.72, 0.78) and 0.75 (95% CI: 0.71, 0.78) in the training set and validation set, respectively; the pooled sensitivity was 0.79 (95% CI: 0.72, 0.84) and 0.76 (95% CI: 0.80, 0.81) in the training set and validation set, respectively; and the pooled specificity was 0.81 (95% CI: 0.75, 0.86) and 0.83 (95% CI: 0.72, 0.90) in the training set and validation set, respectively. CONCLUSIONS ML has a favourable predictive performance for fracture risk in patients with osteoporosis. However, most current studies lack external validation. Thus, external validation is required to verify the reliability of ML models. PROSPERO REGISTRATION NUMBER CRD42022346896.
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Affiliation(s)
- Yanqian Wu
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Jianqian Chao
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Min Bao
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Na Zhang
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
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12
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Dong Q, Luo G, Lane NE, Lui LY, Marshall LM, Johnston SK, Dabbous H, O'Reilly M, Linnau KF, Perry J, Chang BC, Renslo J, Haynor D, Jarvik JG, Cross NM. Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria. Acad Radiol 2023; 30:2973-2987. [PMID: 37438161 PMCID: PMC10776803 DOI: 10.1016/j.acra.2023.04.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/13/2023] [Accepted: 04/20/2023] [Indexed: 07/14/2023]
Abstract
RATIONALE AND OBJECTIVES Spinal osteoporotic compression fractures (OCFs) can be an early biomarker for osteoporosis but are often subtle, incidental, and underreported. To ensure early diagnosis and treatment of osteoporosis, we aimed to build a deep learning vertebral body classifier for OCFs as a critical component of our future automated opportunistic screening tool. MATERIALS AND METHODS We retrospectively assembled a local dataset, including 1790 subjects and 15,050 vertebral bodies (thoracic and lumbar). Each vertebral body was annotated using an adaption of the modified-2 algorithm-based qualitative criteria. The Osteoporotic Fractures in Men (MrOS) Study dataset provided thoracic and lumbar spine radiographs of 5994 men from six clinical centers. Using both datasets, five deep learning algorithms were trained to classify each individual vertebral body of the spine radiographs. Classification performance was compared for these models using multiple metrics, including the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and positive predictive value (PPV). RESULTS Our best model, built with ensemble averaging, achieved an AUC-ROC of 0.948 and 0.936 on the local dataset's test set and the MrOS dataset's test set, respectively. After setting the cutoff threshold to prioritize PPV, this model achieved a sensitivity of 54.5% and 47.8%, a specificity of 99.7% and 99.6%, and a PPV of 89.8% and 94.8%. CONCLUSION Our model achieved an AUC-ROC>0.90 on both datasets. This testing shows some generalizability to real-world clinical datasets and a suitable performance for a future opportunistic osteoporosis screening tool.
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Affiliation(s)
- Qifei Dong
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington (Q.D., G.L., B.C.C.)
| | - Gang Luo
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington (Q.D., G.L., B.C.C.)
| | - Nancy E Lane
- Department of Medicine, University of California - Davis, Sacramento, California (N.E.L.)
| | - Li-Yung Lui
- Research Institute, California Pacific Medical Center, San Francisco, California (L.-Y.L.)
| | - Lynn M Marshall
- Epidemiology Programs, Oregon Health and Science University-Portland State University School of Public Health, Portland, Oregon (L.M.M.)
| | - Sandra K Johnston
- Department of Radiology, University of Washington, Seattle, Washington (S.K.J., K.F.L., D.H., N.M.C)
| | - Howard Dabbous
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia (H.D.)
| | - Michael O'Reilly
- Department of Radiology, University of Limerick Hospital Group, Limerick, Ireland (M.O.)
| | - Ken F Linnau
- Department of Radiology, University of Washington, Seattle, Washington (S.K.J., K.F.L., D.H., N.M.C)
| | - Jessica Perry
- Department of Biostatistics, University of Washington, Seattle, Washington (J.P.)
| | - Brian C Chang
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington (Q.D., G.L., B.C.C.)
| | - Jonathan Renslo
- Keck School of Medicine, University of Southern California, Los Angeles, California (J.R.)
| | - David Haynor
- Department of Radiology, University of Washington, Seattle, Washington (S.K.J., K.F.L., D.H., N.M.C)
| | - Jeffrey G Jarvik
- Departments of Radiology and Neurological Surgery, University of Washington, Seattle, Washington (J.G.J)
| | - Nathan M Cross
- Department of Radiology, University of Washington, Seattle, Washington (S.K.J., K.F.L., D.H., N.M.C).
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13
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Wu Y, Chen X, Dong F, He L, Cheng G, Zheng Y, Ma C, Yao H, Zhou S. Performance evaluation of a deep learning-based cascaded HRNet model for automatic measurement of X-ray imaging parameters of lumbar sagittal curvature. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023:10.1007/s00586-023-07937-5. [PMID: 37787781 DOI: 10.1007/s00586-023-07937-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 04/03/2023] [Accepted: 08/30/2023] [Indexed: 10/04/2023]
Abstract
PURPOSE To develop a deep learning-based cascaded HRNet model, in order to automatically measure X-ray imaging parameters of lumbar sagittal curvature and to evaluate its prediction performance. METHODS A total of 3730 lumbar lateral digital radiography (DR) images were collected from picture archiving and communication system (PACS). Among them, 3150 images were randomly selected as the training dataset and validation dataset, and 580 images as the test dataset. The landmarks of the lumbar curve index (LCI), lumbar lordosis angle (LLA), sacral slope (SS), lumbar lordosis index (LLI), and the posterior edge tangent angle of the vertebral body (PTA) were identified and marked. The measured results of landmarks on the test dataset were compared with the mean values of manual measurement as the reference standard. Percentage of correct key-points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root-mean-square error (RMSE), and Bland-Altman plot were used to evaluate the performance of the cascade HRNet model. RESULTS The PCK of the cascaded HRNet model was 97.9-100% in the 3 mm distance threshold. The mean differences between the reference standard and the predicted values for LCI, LLA, SS, LLI, and PTA were 0.43 mm, 0.99°, 1.11°, 0.01 mm, and 0.23°, respectively. There were strong correlation and consistency of the five parameters between the cascaded HRNet model and manual measurements (ICC = 0.989-0.999, R = 0.991-0.999, MAE = 0.63-1.65, MSE = 0.61-4.06, RMSE = 0.78-2.01). CONCLUSION The cascaded HRNet model based on deep learning algorithm could accurately identify the sagittal curvature-related landmarks on lateral lumbar DR images and automatically measure the relevant parameters, which is of great significance in clinical application.
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Affiliation(s)
- Yuhua Wu
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Xiaofei Chen
- Department of Radiology, Gansu Provincial Hospital of Traditional Chinese Medicine (The first affiliated hospital of Gansu University of Traditional Chinese Medicine), Lanzhou, 730050, Gansu, China
| | - Fuwen Dong
- Department of Radiology, Gansu Provincial Hospital of Traditional Chinese Medicine (The first affiliated hospital of Gansu University of Traditional Chinese Medicine), Lanzhou, 730050, Gansu, China
| | - Linyang He
- Hangzhou Jianpei Technology Company Ltd, Hangzhou, 311200, Zhejiang, China
| | - Guohua Cheng
- Hangzhou Jianpei Technology Company Ltd, Hangzhou, 311200, Zhejiang, China
| | - Yuwen Zheng
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Chunyu Ma
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Hongyan Yao
- Department of Radiology, Gansu Provincial Hospital, No. 204, Donggang West Road, Lanzhou, 730000, Gansu, China
| | - Sheng Zhou
- Department of Radiology, Gansu Provincial Hospital, No. 204, Donggang West Road, Lanzhou, 730000, Gansu, China.
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14
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Zhang J, Liu F, Xu J, Zhao Q, Huang C, Yu Y, Yuan H. Automated detection and classification of acute vertebral body fractures using a convolutional neural network on computed tomography. Front Endocrinol (Lausanne) 2023; 14:1132725. [PMID: 37051194 PMCID: PMC10083489 DOI: 10.3389/fendo.2023.1132725] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 03/14/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Acute vertebral fracture is usually caused by low-energy injury with osteoporosis and high-energy trauma. The AOSpine thoracolumbar spine injury classification system (AO classification) plays an important role in the diagnosis and treatment of the disease. The diagnosis and description of vertebral fractures according to the classification scheme requires a great deal of time and energy for radiologists. PURPOSE To design and validate a multistage deep learning system (multistage AO system) for the automatic detection, localization and classification of acute thoracolumbar vertebral body fractures according to AO classification on computed tomography. MATERIALS AND METHODS The CT images of 1,217 patients who came to our hospital from January 2015 to December 2019 were collected retrospectively. The fractures were marked and classified by 2 junior radiology residents according to the type A standard in the AO classification. Marked fracture sites included the upper endplate, lower endplate and posterior wall. When there were inconsistent opinions on classification labels, the final result was determined by a director radiologist. We integrated different networks into different stages of the overall framework. U-net and a graph convolutional neural network (U-GCN) are used to realize the location and classification of the thoracolumbar spine. Next, a classification network is used to detect whether the thoracolumbar spine has a fracture. In the third stage, we detect fractures in different parts of the thoracolumbar spine by using a multibranch output network and finally obtain the AO types. RESULTS The mean age of the patients was 61.87 years with a standard deviation of 17.04 years, consisting of 760 female patients and 457 male patients. On vertebrae level, sensitivity for fracture detection was 95.23% in test dataset, with an accuracy of 97.93% and a specificity of 98.35%. For the classification of vertebral body fractures, the balanced accuracy was 79.56%, with an AUC of 0.904 for type A1, 0.945 for type A2, 0.878 for type A3 and 0.942 for type A4. CONCLUSION The multistage AO system can automatically detect and classify acute vertebral body fractures in the thoracolumbar spine on CT images according to AO classification with high accuracy.
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Affiliation(s)
- Jianlun Zhang
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | | | | | - Qingqing Zhao
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | | | | | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
- *Correspondence: Huishu Yuan,
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The Role of Sympathetic Nerves in Osteoporosis: A Narrative Review. Biomedicines 2022; 11:biomedicines11010033. [PMID: 36672541 PMCID: PMC9855775 DOI: 10.3390/biomedicines11010033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/09/2022] [Accepted: 12/14/2022] [Indexed: 12/25/2022] Open
Abstract
Osteoporosis, a systemic bone disease, is characterized by decreased bone density due to various reasons, destructed bone microstructure, and increased bone fragility. The incidence of osteoporosis is very high among the elderly, and patients with osteoporosis are prone to suffer from spine fractures and hip fractures, which cause great harm to patients. Meanwhile, osteoporosis is mainly treated with anti-osteoporosis drugs that have side effects. Therefore, the development of new treatment modalities has a significant clinical impact. Sympathetic nerves play an important role in various physiological activities and the regulation of osteoporosis as well. Therefore, the role of sympathetic nerves in osteoporosis was reviewed, aiming to provide information for future targeting of sympathetic nerves in osteoporosis.
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