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Zhang J, Xia L, Zhang X, Liu J, Tang J, Xia J, Liu Y, Zhang W, Liang Z, Tang G, Zhang L. Development and validation of a predictive model for vertebral fracture risk in osteoporosis patients. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2024:10.1007/s00586-024-08235-4. [PMID: 38955868 DOI: 10.1007/s00586-024-08235-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/22/2024] [Accepted: 03/17/2024] [Indexed: 07/04/2024]
Abstract
OBJECTIVE This study aimed to develop and validate a predictive model for osteoporotic vertebral fractures (OVFs) risk by integrating demographic, bone mineral density (BMD), CT imaging, and deep learning radiomics features from CT images. METHODS A total of 169 osteoporosis-diagnosed patients from three hospitals were randomly split into OVFs (n = 77) and Non-OVFs (n = 92) groups for training (n = 135) and test (n = 34). Demographic data, BMD, and CT imaging details were collected. Deep transfer learning (DTL) using ResNet-50 and radiomics features were fused, with the best model chosen via logistic regression. Cox proportional hazards models identified clinical factors. Three models were constructed: clinical, radiomics-DTL, and fusion (clinical-radiomics-DTL). Performance was assessed using AUC, C-index, Kaplan-Meier, and calibration curves. The best model was depicted as a nomogram, and clinical utility was evaluated using decision curve analysis (DCA). RESULTS BMD, CT values of paravertebral muscles (PVM), and paravertebral muscles' cross-sectional area (CSA) significantly differed between OVFs and Non-OVFs groups (P < 0.05). No significant differences were found between training and test cohort. Multivariate Cox models identified BMD, CT values of PVM, and CSAPS reduction as independent OVFs risk factors (P < 0.05). The fusion model exhibited the highest predictive performance (C-index: 0.839 in training, 0.795 in test). DCA confirmed the nomogram's utility in OVFs risk prediction. CONCLUSION This study presents a robust predictive model for OVFs risk, integrating BMD, CT data, and radiomics-DTL features, offering high sensitivity and specificity. The model's visualizations can inform OVFs prevention and treatment strategies.
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Affiliation(s)
- Jun Zhang
- Department of Radiology, Shanghai Tenth People's Hospital, Clinical Medical College of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, People's Republic of China
- Department of Radiology, Sir Run Run Hospital, Nanjing Medical University, 109 Longmian Road, Nanjing, 211002, Jiangsu, People's Republic of China
| | - Liang Xia
- Department of Radiology, Sir Run Run Hospital, Nanjing Medical University, 109 Longmian Road, Nanjing, 211002, Jiangsu, People's Republic of China.
| | - Xueli Zhang
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, People's Republic of China
| | - Jiayi Liu
- Department of Radiology, Sir Run Run Hospital, Nanjing Medical University, 109 Longmian Road, Nanjing, 211002, Jiangsu, People's Republic of China
| | - Jun Tang
- Department of Radiology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, 366 Taihu Road, Taizhou, 225300, Jiangsu, People's Republic of China
| | - Jianguo Xia
- Department of Radiology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, 366 Taihu Road, Taizhou, 225300, Jiangsu, People's Republic of China.
| | - Yongkang Liu
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong Road, Nanjing, 210004, Jiangsu, People's Republic of China
| | - Weixiao Zhang
- Department of Radiology, Sir Run Run Hospital, Nanjing Medical University, 109 Longmian Road, Nanjing, 211002, Jiangsu, People's Republic of China
| | - Zhipeng Liang
- Department of Radiology, Sir Run Run Hospital, Nanjing Medical University, 109 Longmian Road, Nanjing, 211002, Jiangsu, People's Republic of China
| | - Guangyu Tang
- Department of Radiology, Shanghai Tenth People's Hospital, Clinical Medical College of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, People's Republic of China.
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, People's Republic of China.
| | - Lin Zhang
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, People's Republic of China.
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Isleem UN, Zaidat B, Ren R, Geng EA, Burapachaisri A, Tang JE, Kim JS, Cho SK. Can generative artificial intelligence pass the orthopaedic board examination? J Orthop 2024; 53:27-33. [PMID: 38450060 PMCID: PMC10912220 DOI: 10.1016/j.jor.2023.10.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 10/24/2023] [Accepted: 10/26/2023] [Indexed: 03/08/2024] Open
Abstract
Background Resident training programs in the US use the Orthopaedic In-Training Examination (OITE) developed by the American Academy of Orthopaedic Surgeons (AAOS) to assess the current knowledge of their residents and to identify the residents at risk of failing the Amerian Board of Orthopaedic Surgery (ABOS) examination. Optimal strategies for OITE preparation are constantly being explored. There may be a role for Large Language Models (LLMs) in orthopaedic resident education. ChatGPT, an LLM launched in late 2022 has demonstrated the ability to produce accurate, detailed answers, potentially enabling it to aid in medical education and clinical decision-making. The purpose of this study is to evaluate the performance of ChatGPT on Orthopaedic In-Training Examinations using Self-Assessment Exams from the AAOS database and approved literature as a proxy for the Orthopaedic Board Examination. Methods 301 SAE questions from the AAOS database and associated AAOS literature were input into ChatGPT's interface in a question and multiple-choice format and the answers were then analyzed to determine which answer choice was selected. A new chat was used for every question. All answers were recorded, categorized, and compared to the answer given by the OITE and SAE exams, noting whether the answer was right or wrong. Results Of the 301 questions asked, ChatGPT was able to correctly answer 183 (60.8%) of them. The subjects with the highest percentage of correct questions were basic science (81%), oncology (72.7%, shoulder and elbow (71.9%), and sports (71.4%). The questions were further subdivided into 3 groups: those about management, diagnosis, or knowledge recall. There were 86 management questions and 47 were correct (54.7%), 45 diagnosis questions with 32 correct (71.7%), and 168 knowledge recall questions with 102 correct (60.7%). Conclusions ChatGPT has the potential to provide orthopedic educators and trainees with accurate clinical conclusions for the majority of board-style questions, although its reasoning should be carefully analyzed for accuracy and clinical validity. As such, its usefulness in a clinical educational context is currently limited but rapidly evolving. Clinical relevance ChatGPT can access a multitude of medical data and may help provide accurate answers to clinical questions.
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Affiliation(s)
- Ula N. Isleem
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bashar Zaidat
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Renee Ren
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric A. Geng
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Aonnicha Burapachaisri
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Justin E. Tang
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jun S. Kim
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Samuel K. Cho
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Qiu X, Zhu T, Zhao Z, Cui Z, Deng H, Tang S, Sechi LA, Caggiari G, Zhao C, Xiong Z. Muscle texture features on preoperative MRI for diagnosis and assessment of severity of congenital muscular torticollis. J Orthop Surg Res 2024; 19:367. [PMID: 38902712 PMCID: PMC11191279 DOI: 10.1186/s13018-024-04827-4] [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: 03/27/2024] [Accepted: 05/31/2024] [Indexed: 06/22/2024] Open
Abstract
OBJECTIVES To develop an objective method based on texture analysis on MRI for diagnosis of congenital muscular torticollis (CMT). MATERIAL AND METHODS The T1- and T2-weighted imaging, Q-dixon, and T1-mapping MRI data of 38 children with CMT were retrospectively analyzed. The region of interest (ROI) was manually drawn at the level of the largest cross-sectional area of the SCM on the affected side. MaZda software was used to obtain the texture features of the T2WI sequences of the ROI in healthy and affected SCM. A radiomics diagnostic model based on muscle texture features was constructed using logistic regression analysis. Fatty infiltration grade was calculated by hematoxylin and eosin staining, and fibrosis ratio by Masson staining. Correlation between the MRI parameters and pathological indicators was analyzed. RESULTS There was positive correlation between fatty infiltration grade and mean value, standard deviation, and maximum value of the Q-dixon sequence of the affected SCM (correlation coefficients, 0.65, 0.59, and 0.58, respectively, P < 0.05).Three muscle texture features-S(2,2)SumAverg, S(3,3)SumVarnc, and T2WI extreme difference-were selected to construct the diagnostic model. The model showed significant diagnostic value for CMT (P < 0.05). The area under the curve of the multivariate conditional logistic regression model was 0.828 (95% confidence interval 0.735-0.922); the sensitivity was 0.684 and the specificity 0.868. CONCLUSION The radiomics diagnostic model constructed using T2WI muscle texture features and MRI signal values appears to have good diagnostic efficiency. Q-dixon sequence can reflect the fatty infiltration grade of CMT.
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Affiliation(s)
- Xin Qiu
- Shenzhen Children's Hospital, Shenzhen, People's Republic of China.
| | - Tianfeng Zhu
- Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Zhenhui Zhao
- Shenzhen Children's Hospital, Shenzhen, People's Republic of China
- China Medical University, Shenyang, People's Republic of China
| | - Zhiwen Cui
- Shenzhen Children's Hospital, Shenzhen, People's Republic of China
- Nanshan District Medical Group Headquarters, Shenzhen, People's Republic of China
| | - Hansheng Deng
- Shenzhen Children's Hospital, Shenzhen, People's Republic of China
- Orthopaedic Department, Sassari University Hospital, 07100, Sassari, Italy
- Department of Biomedical Sciences, University of Sassari, 07100, Sassari, Italy
| | - Shengping Tang
- Shenzhen Children's Hospital, Shenzhen, People's Republic of China.
| | | | | | - Cailei Zhao
- Shenzhen Children's Hospital, Shenzhen, People's Republic of China.
| | - Zhu Xiong
- Shenzhen Children's Hospital, Shenzhen, People's Republic of China.
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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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Zhang YY, Xie N, Sun XD, Nice EC, Liou YC, Huang C, Zhu H, Shen Z. Insights and implications of sexual dimorphism in osteoporosis. Bone Res 2024; 12:8. [PMID: 38368422 PMCID: PMC10874461 DOI: 10.1038/s41413-023-00306-4] [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: 06/21/2023] [Revised: 11/04/2023] [Accepted: 11/27/2023] [Indexed: 02/19/2024] Open
Abstract
Osteoporosis, a metabolic bone disease characterized by low bone mineral density and deterioration of bone microarchitecture, has led to a high risk of fatal osteoporotic fractures worldwide. Accumulating evidence has revealed that sexual dimorphism is a notable feature of osteoporosis, with sex-specific differences in epidemiology and pathogenesis. Specifically, females are more susceptible than males to osteoporosis, while males are more prone to disability or death from the disease. To date, sex chromosome abnormalities and steroid hormones have been proven to contribute greatly to sexual dimorphism in osteoporosis by regulating the functions of bone cells. Understanding the sex-specific differences in osteoporosis and its related complications is essential for improving treatment strategies tailored to women and men. This literature review focuses on the mechanisms underlying sexual dimorphism in osteoporosis, mainly in a population of aging patients, chronic glucocorticoid administration, and diabetes. Moreover, we highlight the implications of sexual dimorphism for developing therapeutics and preventive strategies and screening approaches tailored to women and men. Additionally, the challenges in translating bench research to bedside treatments and future directions to overcome these obstacles will be discussed.
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Affiliation(s)
- Yuan-Yuan Zhang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu, 610041, China
| | - Na Xie
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Xiao-Dong Sun
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Edouard C Nice
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, 3800, Australia
| | - Yih-Cherng Liou
- Department of Biological Sciences, Faculty of Science, National University of Singapore, Singapore, 117543, Republic of Singapore
| | - Canhua Huang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Huili Zhu
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, Department of Reproductive Medicine, West China Second University Hospital of Sichuan University, Chengdu, China.
| | - Zhisen Shen
- Department of Otorhinolaryngology and Head and Neck Surgery, The Affiliated Lihuili Hospital, Ningbo University, 315040, Ningbo, Zhejiang, China.
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Zhang Y, Hu M, Zhao W, Liu X, Peng Q, Meng B, Yang S, Feng X, Zhang L. A Bibliometric Analysis of Artificial Intelligence Applications in Spine Care. J Neurol Surg A Cent Eur Neurosurg 2024; 85:62-73. [PMID: 36640757 DOI: 10.1055/a-2013-3149] [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: 01/15/2023]
Abstract
BACKGROUND With the rapid development of science and technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis of various spine diseases. It has been proved that AI has a broad prospect in accurate diagnosis and treatment of spine disorders. METHODS On May 7, 2022, the Web of Science (WOS) Core Collection database was used to identify the documents on the application of AI in the field of spine care. HistCite and VOSviewer were used for citation analysis and visualization mapping. RESULTS A total of 693 documents were included in the final analysis. The most prolific authors were Karhade A.V. and Schwab J.H. United States was the most productive country. The leading journal was Spine. The most frequently used keyword was spinal. The most prolific institution was Northwestern University in Illinois, USA. Network visualization map showed that United States was the largest network of international cooperation. The keyword "machine learning" had the strongest total link strengths (TLS) and largest number of occurrences. The latest trends suggest that AI for the diagnosis of spine diseases may receive widespread attention in the future. CONCLUSIONS AI has a wide range of application in the field of spine care, and an increasing number of scholars are committed to research on the use of AI in the field of spine care. Bibliometric analysis in the field of AI and spine provides an overall perspective, and the appreciation and research of these influential publications are useful for future research.
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Affiliation(s)
- Yu Zhang
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Man Hu
- Graduate School of Dalian Medical University, Dalian, China
| | - Wenjie Zhao
- Graduate School of Dalian Medical University, Dalian, China
| | - Xin Liu
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Qing Peng
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Bo Meng
- Graduate School of Dalian Medical University, Dalian, China
| | - Sheng Yang
- Graduate School of Dalian Medical University, Dalian, China
| | - Xinmin Feng
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Liang Zhang
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
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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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Goller SS, Foreman SC, Rischewski JF, Weißinger J, Dietrich AS, Schinz D, Stahl R, Luitjens J, Siller S, Schmidt VF, Erber B, Ricke J, Liebig T, Kirschke JS, Dieckmeyer M, Gersing AS. Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023; 32:4314-4320. [PMID: 37401945 DOI: 10.1007/s00586-023-07838-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/25/2023] [Accepted: 06/20/2023] [Indexed: 07/05/2023]
Abstract
PURPOSE To assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs). METHODS A total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference. Automated detection, labelling, and segmentation of the vertebrae were performed using a CNN-based framework ( https://anduin.bonescreen.de ). Eight TFs were extracted: Varianceglobal, Skewnessglobal, energy, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP). Multivariate regression models adjusted for age and sex were used to compare TFs between benign and malignant VFs. RESULTS Skewnessglobal showed a significant difference between the two groups when analyzing fractured vertebrae from T1 to L6 (benign fracture group: 0.70 [0.64-0.76]; malignant fracture group: 0.59 [0.56-0.63]; and p = 0.017), suggesting a higher skewness in benign VFs compared to malignant VFs. CONCLUSION Three-dimensional CT-based global TF skewness assessed using a CNN-based framework showed significant difference between benign and malignant thoracolumbar VFs and may therefore contribute to the clinical diagnostic work-up of patients with VFs.
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Affiliation(s)
- Sophia S Goller
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.
| | - Sarah C Foreman
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jon F Rischewski
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Jürgen Weißinger
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Anna-Sophia Dietrich
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - David Schinz
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Robert Stahl
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Johanna Luitjens
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Sebastian Siller
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
| | - Vanessa F Schmidt
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Bernd Erber
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Thomas Liebig
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University Hospital, University of Bern, Bern, Switzerland
| | - Alexandra S Gersing
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
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Allam AK, Anand A, Flores AR, Ropper AE. Computer Vision in Osteoporotic Vertebral Fracture Risk Prediction: A Systematic Review. Neurospine 2023; 20:1112-1123. [PMID: 38171281 PMCID: PMC10762393 DOI: 10.14245/ns.2347022.511] [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: 09/30/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
Osteoporotic vertebral fractures (OVFs) are a significant health concern linked to increased morbidity, mortality, and diminished quality of life. Traditional OVF risk assessment tools like bone mineral density (BMD) only capture a fraction of the risk profile. Artificial intelligence, specifically computer vision, has revolutionized other fields of medicine through analysis of videos, histopathology slides and radiological scans. In this review, we provide an overview of computer vision algorithms and current computer vision models used in predicting OVF risk. We highlight the clinical applications, future directions and limitations of computer vision in OVF risk prediction.
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Affiliation(s)
- Anthony K. Allam
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Adrish Anand
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Alex R. Flores
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
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10
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Cai J, Shen C, Yang T, Jiang Y, Ye H, Ruan Y, Zhu X, Liu Z, Liu Q. MRI-based radiomics assessment of the imminent new vertebral fracture after vertebral augmentation. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023; 32:3892-3905. [PMID: 37624438 DOI: 10.1007/s00586-023-07887-y] [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: 03/05/2023] [Revised: 07/13/2023] [Accepted: 08/06/2023] [Indexed: 08/26/2023]
Abstract
BACKGROUND Imminent new vertebral fracture (NVF) is highly prevalent after vertebral augmentation (VA). An accurate assessment of the imminent risk of NVF could help to develop prompt treatment strategies. PURPOSE To develop and validate predictive models that integrated the radiomic features and clinical risk factors based on machine learning algorithms to evaluate the imminent risk of NVF. MATERIALS AND METHODS In this retrospective study, a total of 168 patients with painful osteoporotic vertebral compression fractures treated with VA were evaluated. Radiomic features of L1 vertebrae based on lumbar T2-weighted images were obtained. Univariate and LASSO-regression analyses were applied to select the optimal features and construct radiomic signature. The radiomic signature and clinical signature were integrated to develop a predictive model by using machine learning algorithms including LR, RF, SVM, and XGBoost. Receiver operating characteristic curve and calibration curve analyses were used to evaluate the predictive performance of the models. RESULTS The radiomic-XGBoost model with the highest AUC of 0.93 of the training cohort and 0.9 of the test cohort among the machine learning algorithms. The combined-XGBoost model with the best performance with an AUC of 0.9 in the training cohort and 0.9 in the test cohort. The radiomic-XGBoost model and combined-XGBoost model achieved better performance to assess the imminent risk of NVF than that of the clinical risk factors alone (p < 0.05). CONCLUSION Radiomic and machine learning modeling based on T2W images of preoperative lumbar MRI had an excellent ability to evaluate the imminent risk of NVF after VA.
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Affiliation(s)
- Jinhui Cai
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, 628 Zhenyuan Road, Xinhu Street, Guangming New District, Shenzhen, 518107, Guangdong, China
- Department of Radiology, The Fourth Affiliated Hospital of Guangzhou Medical University, 1 Guangming East Road, Zengjiang Street, Zengcheng District, Guangzhou, 511300, Guangdong, China
| | - Chen Shen
- Department of Radiology, The Fourth Affiliated Hospital of Guangzhou Medical University, 1 Guangming East Road, Zengjiang Street, Zengcheng District, Guangzhou, 511300, Guangdong, China
| | - Tingqian Yang
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, 628 Zhenyuan Road, Xinhu Street, Guangming New District, Shenzhen, 518107, Guangdong, China
| | - Yang Jiang
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, 628 Zhenyuan Road, Xinhu Street, Guangming New District, Shenzhen, 518107, Guangdong, China
| | - Haoyi Ye
- Department of Radiology, The Fourth Affiliated Hospital of Guangzhou Medical University, 1 Guangming East Road, Zengjiang Street, Zengcheng District, Guangzhou, 511300, Guangdong, China
| | - Yaoqin Ruan
- Department of Radiology, The Fourth Affiliated Hospital of Guangzhou Medical University, 1 Guangming East Road, Zengjiang Street, Zengcheng District, Guangzhou, 511300, Guangdong, China
| | - Xuemin Zhu
- Department of Spine Surgery, The Fourth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 511300, China
| | - Zhifeng Liu
- Department of Radiology, The Fourth Affiliated Hospital of Guangzhou Medical University, 1 Guangming East Road, Zengjiang Street, Zengcheng District, Guangzhou, 511300, Guangdong, China.
| | - Qingyu Liu
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, 628 Zhenyuan Road, Xinhu Street, Guangming New District, Shenzhen, 518107, Guangdong, China.
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11
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Li WG, Zeng R, Lu Y, Li WX, Wang TT, Lin H, Peng Y, Gong LG. The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures. BMC Musculoskelet Disord 2023; 24:819. [PMID: 37848859 PMCID: PMC10580519 DOI: 10.1186/s12891-023-06939-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 10/05/2023] [Indexed: 10/19/2023] Open
Abstract
PURPOSE To develop and evaluate the performance of radiomics-based computed tomography (CT) combined with machine learning algorithms in detecting occult vertebral fractures (OVFs). MATERIALS AND METHODS 128 vertebrae including 64 with OVF confirmed by magnetic resonance imaging and 64 corresponding control vertebrae from 57 patients who underwent chest/abdominal CT scans, were included. The CT radiomics features on mid-axial and mid-sagittal plane of each vertebra were extracted. The fractured and normal vertebrae were randomly divided into training set and validation set at a ratio of 8:2. Pearson correlation analyses and least absolute shrinkage and selection operator were used for selecting sagittal and axial features, respectively. Three machine-learning algorithms were used to construct the radiomics models based on the residual features. Receiver operating characteristic (ROC) analysis was used to verify the performance of model. RESULTS For mid-axial CT imaging, 6 radiomics parameters were obtained and used for building the models. The logistic regression (LR) algorithm showed the best performance with area under the ROC curves (AUC) of training and validation sets of 0.682 and 0.775. For mid-sagittal CT imaging, 5 parameters were selected, and LR algorithms showed the best performance with AUC of training and validation sets of 0.832 and 0.882. The LR model based on sagittal CT yielded the best performance, with an accuracy of 0.846, sensitivity of 0.846, and specificity of 0.846. CONCLUSION Machine learning based on CT radiomics features allows for the detection of OVFs, especially the LR model based on the radiomics of sagittal imaging, which indicates it is promising to further combine with deep learning to achieve automatic recognition of OVFs to reduce the associated secondary injury.
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Affiliation(s)
- Wu-Gen Li
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, No. 1Minde Road, Nanchang, Jiangxi, 330006, China
| | - Rou Zeng
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, No. 1Minde Road, Nanchang, Jiangxi, 330006, China
| | - Yong Lu
- Department of Radiology, Xinjian County People's Hospital, Nanchang, 330103, China
| | - Wei-Xiang Li
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, No. 1Minde Road, Nanchang, Jiangxi, 330006, China
| | - Tong-Tong Wang
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, No. 1Minde Road, Nanchang, Jiangxi, 330006, China
| | - Huashan Lin
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Changsha, Hunan, 410000, China
| | - Yun Peng
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, No. 1Minde Road, Nanchang, Jiangxi, 330006, China
| | - Liang-Geng Gong
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, No. 1Minde Road, Nanchang, Jiangxi, 330006, China.
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Zhang F, Wang H, Liu L, Su T, Ji B. Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters. BMC Infect Dis 2023; 23:675. [PMID: 37817106 PMCID: PMC10566101 DOI: 10.1186/s12879-023-08602-4] [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: 06/27/2023] [Accepted: 09/12/2023] [Indexed: 10/12/2023] Open
Abstract
BACKGROUND Bacterial bloodstream infection is responsible for the majority of cases of sepsis and septic shock. Early recognition of the causative pathogen is pivotal for administration of adequate empiric antibiotic therapy and for the survival of the patients. In this study, we developed a feasible machine learning (ML) model to predict gram-positive and gram-negative bacteremia based on routine laboratory parameters. METHODS Data for 2118 patients with bacteremia were obtained from the Medical Information Mart for Intensive Care dataset. Patients were randomly split into the training set and test set by stratified sampling, and 374 routine laboratory blood test variables were retrieved. Variables with missing values in more than 40% of the patients were excluded. Pearson correlation test was employed to eliminate redundant features. Five ML algorithms were used to build the model based on the selected features. Additionally, 132 patients with bacteremia who were treated at Qilu Hospital of Shandong University were included in an independent test cohort to evaluate the model. RESULTS After feature selection, 32 variables remained. All the five ML algorithms performed well in terms of discriminating between gram-positive and gram-negative bacteremia, but the performance of convolutional neural network (CNN) and random forest (RF) were better than other three algorithms. Consider of the interpretability of models, RF was chosen for further test (ROC-AUC = 0.768; 95%CI = 0.715-0.798, with a sensitivity of 75.20% and a specificity of 63.79%). To expand the application of the model, a decision tree (DT) was built utilizing the major variables, and it achieved an AUC of 0.679 (95%CI = 0.632-0.723), a sensitivity of 66%, and a specificity of 67.82% in the test cohort. When tested in the Qilu Hospital cohort, the ROC-AUC of the RF and DT models were 0.666 (95%CI = 0.579-0.746) and 0.615 (95%CI = 0.526-0.698), respectively. Finally, a software was developed to make the RF- and DT-based prediction models easily accessible. CONCLUSION The present ML-based models could effectively discriminate between gram-positive and gram-negative bacteremia based on routine laboratory blood test results. This simple model would be beneficial in terms of guiding timely antibiotic selection and administration in critically ill patients with bacteremia before their pathogen test results are available.
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Affiliation(s)
- Fan Zhang
- Department of Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Hao Wang
- Department of Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Liyu Liu
- School of Control Science and Engineering, Shandong University, Jinan, 250061, Shandong, China
| | - Teng Su
- School of Control Science and Engineering, Shandong University, Jinan, 250061, Shandong, China
| | - Bing Ji
- School of Control Science and Engineering, Shandong University, Jinan, 250061, Shandong, China.
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13
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Shahait M, Usamentiaga R, Tong Y, Sandberg A, Lee DI, Udupa JK, Torigian DA. MRI-Based Radiomics Analysis of Levator Ani Muscle for Predicting Urine Incontinence after Robot-Assisted Radical Prostatectomy. Diagnostics (Basel) 2023; 13:2913. [PMID: 37761280 PMCID: PMC10528635 DOI: 10.3390/diagnostics13182913] [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: 07/10/2023] [Revised: 09/08/2023] [Accepted: 09/09/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND The exact role of the levator ani (LA) muscle in male continence remains unclear, and so this study aims to shed light on the topic by characterizing MRI-derived radiomic features of LA muscle and their association with postoperative incontinence in men undergoing prostatectomy. METHOD In this retrospective study, 140 patients who underwent robot-assisted radical prostatectomy (RARP) for prostate cancer using preoperative MRI were identified. A biomarker discovery approach based on the optimal biomarker (OBM) method was used to extract features from MRI images, including morphological, intensity-based, and texture-based features of the LA muscle, along with clinical variables. Mathematical models were created using subsets of features and were evaluated based on their ability to predict continence outcomes. RESULTS Univariate analysis showed that the best discriminators between continent and incontinent patients were patients age and features related to LA muscle texture. The proposed feature selection approach found that the best classifier used six features: age, LA muscle texture properties, and the ratio between LA size descriptors. This configuration produced a classification accuracy of 0.84 with a sensitivity of 0.90, specificity of 0.75, and an area under the ROC curve of 0.89. CONCLUSION This study found that certain patient factors, such as increased age and specific texture properties of the LA muscle, can increase the odds of incontinence after RARP. The results showed that the proposed approach was highly effective and could distinguish and predict continents from incontinent patients with high accuracy.
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Affiliation(s)
- Mohammed Shahait
- Department of Surgery, Clemenceau Medical Center, Dubai P.O. Box 124412, United Arab Emirates;
| | - Ruben Usamentiaga
- Department of Computer Science and Engineering, University of Oviedo, 33204 Gijon, Spain;
| | - Yubing Tong
- Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; (Y.T.); (J.K.U.)
| | - Alex Sandberg
- Temple Medical School, Temple University, Philadelphia, PA 19140, USA;
| | - David I. Lee
- Department of Urology, University of California Irvine, Irvine, CA 92868, USA;
| | - Jayaram K. Udupa
- Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; (Y.T.); (J.K.U.)
| | - Drew A. Torigian
- Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; (Y.T.); (J.K.U.)
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Guenoun D, Champsaur P. Opportunistic Computed Tomography Screening for Osteoporosis and Fracture. Semin Musculoskelet Radiol 2023; 27:451-456. [PMID: 37748468 DOI: 10.1055/s-0043-1771037] [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: 09/27/2023]
Abstract
Osteoporosis is underdiagnosed and undertreated, leading to loss of treatment for the patient and high costs for the health care system. Routine thoracic and/or abdominal computed tomography (CT) performed for other indications can screen opportunistically for osteoporosis with no extra cost, time, or irradiation. Various methods can quantify fracture risk on opportunistic clinical CT: vertebral Hounsfield unit bone mineral density (BMD), usually of L1; BMD measurement with asynchronous or internal calibration; quantitative CT; bone texture assessment; and finite element analysis. Screening for osteoporosis and vertebral fractures on opportunistic CT is a promising approach, providing automated fracture risk scores by means of artificial intelligence, thus enabling earlier management.
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Affiliation(s)
- Daphne Guenoun
- APHM, Sainte-Marguerite Hospital, Institute for Locomotion, Department of Radiology, Marseille, France
- Aix-Marseille University, CNRS, Institut des Sciences du Mouvement, Marseille, France
| | - Pierre Champsaur
- APHM, Sainte-Marguerite Hospital, Institute for Locomotion, Department of Radiology, Marseille, France
- Aix-Marseille University, CNRS, Institut des Sciences du Mouvement, Marseille, France
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Goller SS, Rischewski JF, Liebig T, Ricke J, Siller S, Schmidt VF, Stahl R, Kulozik J, Baum T, Kirschke JS, Foreman SC, Gersing AS. Automated Opportunistic Trabecular Volumetric Bone Mineral Density Extraction Outperforms Manual Measurements for the Prediction of Vertebral Fractures in Routine CT. Diagnostics (Basel) 2023; 13:2119. [PMID: 37371014 DOI: 10.3390/diagnostics13122119] [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: 05/09/2023] [Revised: 06/16/2023] [Accepted: 06/18/2023] [Indexed: 06/29/2023] Open
Abstract
Opportunistic osteoporosis screening using multidetector CT-scans (MDCT) and convolutional neural network (CNN)-derived segmentations of the spine to generate volumetric bone mineral density (vBMD) bears the potential to improve incidental osteoporotic vertebral fracture (VF) prediction. However, the performance compared to the established manual opportunistic vBMD measures remains unclear. Hence, we investigated patients with a routine MDCT of the spine who had developed a new osteoporotic incidental VF and frequency matched to patients without incidental VFs as assessed on follow-up MDCT images after 1.5 years. Automated vBMD was generated using CNN-generated segmentation masks and asynchronous calibration. Additionally, manual vBMD was sampled by two radiologists. Automated vBMD measurements in patients with incidental VFs at 1.5-years follow-up (n = 53) were significantly lower compared to patients without incidental VFs (n = 104) (83.6 ± 29.4 mg/cm3 vs. 102.1 ± 27.7 mg/cm3, p < 0.001). This comparison was not significant for manually assessed vBMD (99.2 ± 37.6 mg/cm3 vs. 107.9 ± 33.9 mg/cm3, p = 0.30). When adjusting for age and sex, both automated and manual vBMD measurements were significantly associated with incidental VFs at 1.5-year follow-up, however, the associations were stronger for automated measurements (β = -0.32; 95% confidence interval (CI): -20.10, 4.35; p < 0.001) compared to manual measurements (β = -0.15; 95% CI: -11.16, 5.16; p < 0.03). In conclusion, automated opportunistic measurements are feasible and can be useful for bone mineral density assessment in clinical routine.
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Affiliation(s)
- Sophia S Goller
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Jon F Rischewski
- Institute for Diagnostic and Interventional Neuroradiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Thomas Liebig
- Institute for Diagnostic and Interventional Neuroradiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Sebastian Siller
- Department of Neurosurgery, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Vanessa F Schmidt
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Robert Stahl
- Institute for Diagnostic and Interventional Neuroradiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Julian Kulozik
- Institute of Micro Technology and Medical Device Technology (MIMED), Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Sarah C Foreman
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Alexandra S Gersing
- Institute for Diagnostic and Interventional Neuroradiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
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Jiang Y, Cai J, Zeng Y, Ye H, Yang T, Liu Z, Liu Q. Development and validation of a machine learning model to predict imminent new vertebral fractures after vertebral augmentation. BMC Musculoskelet Disord 2023; 24:472. [PMID: 37296426 PMCID: PMC10251538 DOI: 10.1186/s12891-023-06557-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND Accurately predicting the occurrence of imminent new vertebral fractures (NVFs) in patients with osteoporotic vertebral compression fractures (OVCFs) undergoing vertebral augmentation (VA) is challenging with yet no effective approach. This study aim to examine a machine learning model based on radiomics signature and clinical factors in predicting imminent new vertebral fractures after vertebral augmentation. METHODS A total of 235 eligible patients with OVCFs who underwent VA procedures were recruited from two independent institutions and categorized into three groups, including training set (n = 138), internal validation set (n = 59), and external validation set (n = 38). In the training set, radiomics features were computationally retrieved from L1 or adjacent vertebral body (T12 or L2) on T1-w MRI images, and a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm (LASSO). Predictive radiomics signature and clinical factors were fitted into two final prediction models using the random survival forest (RSF) algorithm or COX proportional hazard (CPH) analysis. Independent internal and external validation sets were used to validate the prediction models. RESULTS The two prediction models were integrated with radiomics signature and intravertebral cleft (IVC). The RSF model with C-indices of 0.763, 0.773, and 0.731 and time-dependent AUC (2 years) of 0.855, 0.907, and 0.839 (p < 0.001 for all) was found to be better predictive than the CPH model in training, internal and external validation sets. The RSF model provided better calibration, larger net benefits (determined by decision curve analysis), and lower prediction error (time-dependent brier score of 0.156, 0.151, and 0.146, respectively) than the CPH model. CONCLUSIONS The integrated RSF model showed the potential to predict imminent NVFs following vertebral augmentation, which will aid in postoperative follow-up and treatment.
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Affiliation(s)
- Yang Jiang
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Jinhui Cai
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Yurong Zeng
- Department of Radiology, Huizhou Central People's Hospital, Huizhou, China
| | - Haoyi Ye
- Department of Radiology, The Fourth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Tingqian Yang
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Zhifeng Liu
- Department of Radiology, The Fourth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Qingyu Liu
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.
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Zhang J, Liu J, Liang Z, Xia L, Zhang W, Xing Y, Zhang X, Tang G. Differentiation of acute and chronic vertebral compression fractures using conventional CT based on deep transfer learning features and hand-crafted radiomics features. BMC Musculoskelet Disord 2023; 24:165. [PMID: 36879285 PMCID: PMC9987077 DOI: 10.1186/s12891-023-06281-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND We evaluated the diagnostic efficacy of deep learning radiomics (DLR) and hand-crafted radiomics (HCR) features in differentiating acute and chronic vertebral compression fractures (VCFs). METHODS A total of 365 patients with VCFs were retrospectively analysed based on their computed tomography (CT) scan data. All patients completed MRI examination within 2 weeks. There were 315 acute VCFs and 205 chronic VCFs. Deep transfer learning (DTL) features and HCR features were extracted from CT images of patients with VCFs using DLR and traditional radiomics, respectively, and feature fusion was performed to establish the least absolute shrinkage and selection operator. The MRI display of vertebral bone marrow oedema was used as the gold standard for acute VCF, and the model performance was evaluated using the receiver operating characteristic (ROC).To separately evaluate the effectiveness of DLR, traditional radiomics and feature fusion in the differential diagnosis of acute and chronic VCFs, we constructed a nomogram based on the clinical baseline data to visualize the classification evaluation. The predictive power of each model was compared using the Delong test, and the clinical value of the nomogram was evaluated using decision curve analysis (DCA). RESULTS Fifty DTL features were obtained from DLR, 41 HCR features were obtained from traditional radiomics, and 77 features fusion were obtained after feature screening and fusion of the two. The area under the curve (AUC) of the DLR model in the training cohort and test cohort were 0.992 (95% confidence interval (CI), 0.983-0.999) and 0.871 (95% CI, 0.805-0.938), respectively. While the AUCs of the conventional radiomics model in the training cohort and test cohort were 0.973 (95% CI, 0.955-0.990) and 0.854 (95% CI, 0.773-0.934), respectively. The AUCs of the features fusion model in the training cohort and test cohort were 0.997 (95% CI, 0.994-0.999) and 0.915 (95% CI, 0.855-0.974), respectively. The AUCs of nomogram constructed by the features fusion in combination with clinical baseline data were 0.998 (95% CI, 0.996-0.999) and 0.946 (95% CI, 0.906-0.987) in the training cohort and test cohort, respectively. The Delong test showed that the differences between the features fusion model and the nomogram in the training cohort and the test cohort were not statistically significant (P values were 0.794 and 0.668, respectively), and the differences in the other prediction models in the training cohort and the test cohort were statistically significant (P < 0.05). DCA showed that the nomogram had high clinical value. CONCLUSION The features fusion model can be used for the differential diagnosis of acute and chronic VCFs, and its differential diagnosis ability is improved when compared with that when either radiomics is used alone. At the same time, the nomogram has a high predictive value for acute and chronic VCFs and can be a potential decision-making tool to assist clinicians, especially when a patient is unable to undergo spinal MRI examination.
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Affiliation(s)
- Jun Zhang
- Department of Radiology, Clinical Medical College of Shanghai Tenth People's Hospital of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China.,Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Jiayi Liu
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Zhipeng Liang
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Liang Xia
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Weixiao Zhang
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Yanfen Xing
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Xueli Zhang
- Department of Radiology, Shanghai TenthPeople's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China
| | - Guangyu Tang
- Department of Radiology, Clinical Medical College of Shanghai Tenth People's Hospital of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China. .,Department of Radiology, Shanghai TenthPeople's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China.
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Lu S, Fuggle NR, Westbury LD, Ó Breasail M, Bevilacqua G, Ward KA, Dennison EM, Mahmoodi S, Niranjan M, Cooper C. Machine learning applied to HR-pQCT images improves fracture discrimination provided by DXA and clinical risk factors. Bone 2023; 168:116653. [PMID: 36581259 DOI: 10.1016/j.bone.2022.116653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 12/16/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022]
Abstract
BACKGROUND Traditional analysis of High Resolution peripheral Quantitative Computed Tomography (HR-pQCT) images results in a multitude of cortical and trabecular parameters which would be potentially cumbersome to interpret for clinicians compared to user-friendly tools utilising clinical parameters. A computer vision approach (by which the entire scan is 'read' by a computer algorithm) to ascertain fracture risk, would be far simpler. We therefore investigated whether a computer vision and machine learning technique could improve upon selected clinical parameters in assessing fracture risk. METHODS Participants of the Hertfordshire Cohort Study (HCS) attended research visits at which height and weight were measured; fracture history was determined via self-report and vertebral fracture assessment. Bone microarchitecture was assessed via HR-pQCT scans of the non-dominant distal tibia (Scanco XtremeCT), and bone mineral density measurement and lateral vertebral assessment were performed using dual-energy X-ray absorptiometry (DXA) (Lunar Prodigy Advanced). Images were cropped, pre-processed and texture analysis was performed using a three-dimensional local binary pattern method. These image data, together with age, sex, height, weight, BMI, dietary calcium and femoral neck BMD, were used in a random-forest classification algorithm. Receiver operating characteristic (ROC) analysis was used to compare fracture risk identification methods. RESULTS Overall, 180 males and 165 females were included in this study with a mean age of approximately 76 years and 97 (28 %) participants had sustained a previous fracture. Using clinical risk factors alone resulted in an area under the curve (AUC) of 0.70 (95 % CI: 0.56-0.84), which improved to 0.71 (0.57-0.85) with the addition of DXA-measured BMD. The addition of HR-pQCT image data to the machine learning classifier with clinical risk factors and DXA-measured BMD as inputs led to an improved AUC of 0.90 (0.83-0.96) with a sensitivity of 0.83 and specificity of 0.74. CONCLUSION These results suggest that using a three-dimensional computer vision method to HR-pQCT scanning may enhance the identification of those at risk of fracture beyond that afforded by clinical risk factors and DXA-measured BMD. This approach has the potential to make the information offered by HR-pQCT more accessible (and therefore) applicable to healthcare professionals in the clinic if the technology becomes more widely available.
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Affiliation(s)
- Shengyu Lu
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, UK.
| | - Nicholas R Fuggle
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK; The Alan Turing Institute, London, UK.
| | - Leo D Westbury
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK.
| | - Mícheál Ó Breasail
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Gregorio Bevilacqua
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK.
| | - Kate A Ward
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK; NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK.
| | - Elaine M Dennison
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK; Victoria University of Wellington, Wellington, New Zealand.
| | - Sasan Mahmoodi
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, UK.
| | - Mahesan Niranjan
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, UK.
| | - Cyrus Cooper
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK; NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
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19
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Wang J, Zhou S, Chen S, He Y, Gao H, Yan L, Hu X, Li P, Shen H, Luo M, You T, Li J, Zhong Z, Zhang K. Prediction of osteoporosis using radiomics analysis derived from single source dual energy CT. BMC Musculoskelet Disord 2023; 24:100. [PMID: 36750927 PMCID: PMC9903590 DOI: 10.1186/s12891-022-06096-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 12/15/2022] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND With the aging population of society, the incidence rate of osteoporosis is increasing year by year. Early diagnosis of osteoporosis plays a significant role in the progress of disease prevention. As newly developed technology, computed tomography (CT) radiomics could discover radiomic features difficult to recognize visually, providing convenient, comprehensive and accurate osteoporosis diagnosis. This study aimed to develop and validate a clinical-radiomics model based on the monochromatic imaging of single source dual-energy CT for osteoporosis prediction. METHODS One hundred sixty-four participants who underwent both single source dual-energy CT and quantitative computed tomography (QCT) lumbar-spine examination were enrolled in a study cohort including training datasets (n = 114 [30 osteoporosis and 84 non-osteoporosis]) and validation datasets (n = 50 [12 osteoporosis and 38 non-osteoporosis]). One hundred seven radiomics features were extracted from 70-keV monochromatic CT images. With QCT as the reference standard, a radiomics signature was built by using least absolute shrinkage and selection operator (LASSO) regression on the basis of reproducible features. A clinical-radiomics model was constructed by incorporating the radiomics signature and a significant clinical predictor (age) using multivariate logistic regression analysis. Model performance was assessed by its calibration, discrimination and clinical usefulness. RESULTS The radiomics signature comprised 14 selected features and showed good calibration and discrimination in both training and validation cohorts. The clinical-radiomics model, which incorporated the radiomics signature and a significant clinical predictor (age), also showed good discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.938 (95% confidence interval, 0.903-0.952) in the training cohort and an AUC of 0.988 (95% confidence interval, 0.967-0.998) in the validation cohort, and good calibration. The clinical-radiomics model stratified participants into groups with osteoporosis and non-osteoporosis with an accuracy of 94.0% in the validation cohort. Decision curve analysis (DCA) demonstrated that the radiomics signature and the clinical-radiomics model were clinically useful. CONCLUSIONS The clinical-radiomics model incorporating the radiomics signature and a clinical parameter had a good ability to predict osteoporosis based on dual-energy CT monoenergetic imaging.
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Affiliation(s)
- Jinling Wang
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China ,grid.488482.a0000 0004 1765 5169College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, 300 Xueshi Road, Yuelu District, Changsha, 410208 People’s Republic of China
| | - Shuwei Zhou
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China ,grid.488482.a0000 0004 1765 5169College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, 300 Xueshi Road, Yuelu District, Changsha, 410208 People’s Republic of China
| | - Suping Chen
- GE Healthcare (Shanghai) Co., Ltd., Shanghai, 201203 People’s Republic of China
| | - Yewen He
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Hui Gao
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Luyou Yan
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Xiaoli Hu
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Ping Li
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Hongrong Shen
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Muqing Luo
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Tian You
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Jianyu Li
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Zeya Zhong
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Kun Zhang
- Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007, People's Republic of China. .,College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, 300 Xueshi Road, Yuelu District, Changsha, 410208, People's Republic of China.
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20
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Vertebral trabecular bone texture analysis in opportunistic MRI and CT scan can distinguish patients with and without osteoporotic vertebral fracture: A preliminary study. Eur J Radiol 2023; 158:110642. [PMID: 36527774 DOI: 10.1016/j.ejrad.2022.110642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022]
Abstract
PURPOSE To investigate the potential of texture parameters from opportunistic MRI and CT for the detection of patients with vertebral fragility fracture, to design a decision tree and to compute a Random Forest analysis for the prediction of fracture risk. METHODS One hundred and eighty vertebrae of sixty patients with at least one (30) or without (30) a fragility fracture were retrospectively assessed. Patients had a DXA, an MRI and a CT scan from the three first lumbar vertebrae. Vertebrae texture analysis was performed in routine abdominal or lumbar CT and lumbar MRI using 1st and 2nd order texture parameters. Hounsfield Unit Bone density (HU BD) was also measured on CT-scan images. RESULTS Twelve texture parameters, Z-score and HU BD were significantly different between the two groups whereas T score and BMD were not. The inter observer reproducibility was good to excellent. Decision tree showed that age and HU BD were the most relevant factors to predict the fracture risk with a 93 % sensitivity and 56 % specificity. AUC was 0.91 in MRI and 0.92 in CT-scan using the Random Forest analysis. The corresponding sensitivity and specificity were 72 % and 93 % in MRI and 83 and 89 % in CT. CONCLUSIONS This study is the first to compare texture indices computed from opportunistic CT and MR images. Age and HU-BD together with selected texture parameters could be used to assess risk fracture. Machine learning algorithm can detect fracture risk in opportunistic CT and MR imaging and might be of high interest for the diagnosis of osteoporosis.
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21
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Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology: An Overview of Novel MRI- and CT-Based Approaches. Invest Radiol 2023; 58:3-13. [PMID: 36070548 DOI: 10.1097/rli.0000000000000907] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
ABSTRACT Radiomics and machine learning-based methods offer exciting opportunities for improving diagnostic performance and efficiency in musculoskeletal radiology for various tasks, including acute injuries, chronic conditions, spinal abnormalities, and neoplasms. While early radiomics-based methods were often limited to a smaller number of higher-order image feature extractions, applying machine learning-based analytic models, multifactorial correlations, and classifiers now permits big data processing and testing thousands of features to identify relevant markers. A growing number of novel deep learning-based methods describe magnetic resonance imaging- and computed tomography-based algorithms for diagnosing anterior cruciate ligament tears, meniscus tears, articular cartilage defects, rotator cuff tears, fractures, metastatic skeletal disease, and soft tissue tumors. Initial radiomics and deep learning techniques have focused on binary detection tasks, such as determining the presence or absence of a single abnormality and differentiation of benign versus malignant. Newer-generation algorithms aim to include practically relevant multiclass characterization of detected abnormalities, such as typing and malignancy grading of neoplasms. So-called delta-radiomics assess tumor features before and after treatment, with temporal changes of radiomics features serving as surrogate markers for tumor responses to treatment. New approaches also predict treatment success rates, surgical resection completeness, and recurrence risk. Practice-relevant goals for the next generation of algorithms include diagnostic whole-organ and advanced classification capabilities. Important research objectives to fill current knowledge gaps include well-designed research studies to understand how diagnostic performances and suggested efficiency gains of isolated research settings translate into routine daily clinical practice. This article summarizes current radiomics- and machine learning-based magnetic resonance imaging and computed tomography approaches for musculoskeletal disease detection and offers a perspective on future goals and objectives.
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22
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Wu S, Wei Y, Li H, Zhou C, Chen T, Zhu J, Liu L, Wu S, Ma F, Ye Z, Deng G, Yao Y, Fan B, Liao S, Huang S, Sun X, Chen L, Guo H, Chen W, Zhan X, Liu C. A Predictive Clinical-Radiomics Nomogram for Differentiating Tuberculous Spondylitis from Pyogenic Spondylitis Using CT and Clinical Risk Factors. Infect Drug Resist 2022; 15:7327-7338. [PMID: 36536861 PMCID: PMC9758984 DOI: 10.2147/idr.s388868] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/02/2022] [Indexed: 10/30/2023] Open
Abstract
OBJECTIVE The study aimed to develop and validate a nomogram model with clinical risk factors and radiomic features for differentiating tuberculous spondylitis (TS) from pyogenic spondylitis (PS). METHODS A total of 254 patients with TS (n = 141) or PS (n = 113) were randomly divided into training (n = 180) and validation (n = 74) groups. In addition, 43 patients (TS = 22 and PS = 21) were collected to construct a test cohort. t-test analysis, de-redundancy analysis, and minimum absolute shrinkage and selection operator (lasso) algorithm were utilized on the training set to obtain the optimal radiomics features from computed tomography (CT) for constructing the radiomics model and determine the radiomics score (Rad-score). Eight clinical risk predictors were identified to develop the clinical model. Combined with clinical risk predictors and Rad-scores, a nomogram model was constructed using multivariate logistic regression analysis. RESULTS A total of 1781 features were extracted, and 12 optimal radiomic features were utilized to construct the radiomic model and determine the Rad-score. The combined clinical radiomics model revealed good discrimination performance in both the training cohort and the validation cohort (AUC = 0.891 and 0.830) and was superior to the clinical (AUC = 0.807 and 0.785) and radiomics (AUC = 0.796 and 0.811) models. The calibration curve and DCA also depicted that the nomogram had better clinical efficacy. The discriminative performance of the model is well validated in the test cohort (AUC=0.877). CONCLUSION The clinical radiomic nomogram could serve as a promising predictive tool for differentiating TS from PS, which could be helpful for clinical decision-making.
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Affiliation(s)
- Shaofeng Wu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Yating Wei
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Hao Li
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Chenxing Zhou
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Tianyou Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Jichong Zhu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Lu Liu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Siling Wu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Fengzhi Ma
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Zhen Ye
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Guobing Deng
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Yuanlin Yao
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Binguang Fan
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Shian Liao
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Shengsheng Huang
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Xuhua Sun
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Liyi Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Hao Guo
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Wuhua Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Xinli Zhan
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Chong Liu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
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23
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Wang M, Chen X, Cui W, Wang X, Hu N, Tang H, Zhang C, Shen J, Xie C, Chen X. A computed tomography-based radiomics nomogram for predicting osteoporotic vertebral fractures: A longitudinal study. J Clin Endocrinol Metab 2022; 108:e283-e294. [PMID: 36494103 DOI: 10.1210/clinem/dgac722] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 11/09/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022]
Abstract
CONTEXT Fractures are serious consequence of osteoporosis in old adults. However, few longitudinal studies showed the role of computed tomography (CT)-based radiomics in predicting osteoporotic fractures. OBJECTIVE We evaluated the performance of CT radiomics-based model for osteoporotic vertebral fractures (OVF) in a longitudinal study. METHODS 7906 subjects without OVF who were aged over 50 years, and underwent CT scans between 2016 and 2019 were enrolled and followed up until 2021. Seventy-two cases of new OVF were identified. One hundred and forty-four people without OVF during follow-up were selected as control. Radiomics features were extracted from baseline CT images. CT values of trabecular bone, and area and density of erector spinae were determined. Cox regression analysis was used to identify the independent associated factors. The predictive performance of the nomogram was assessed using the receiver operating characteristic (ROC) curve, calibration curve and decision curve. RESULTS CT value of vertebra (adjusted hazard ratio (aHR) = 2.04, 95% confidence interval (CI): 1.07, 3.89), radiomics score (aHR = 6.56, 95%CI:3.47, 12.38) and area of erector spinae (aHR = 1.68, 95%CI: 1.02, 2.78) were independently associated with OVF. Radscore was associated with severe OVF (aHR = 6.00, 95% CI:2.78-12.93). The nomogram showed good discrimination with a C-index of 0.82 (95%CI: 0.77, 0.87). The area under the curve of nomogram and radscore were both higher than osteoporosis + muscle area for 3-year and 4-year risk of fractures (p < 0.05). Decision curve also demonstrated that the radiomics nomogram was useful. CONCLUSIONS Bone radiomics is associated with OVF and the nomogram based on radiomics signature and muscle provides a tool for the prediction of OVF.
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Affiliation(s)
- Miaomiao Wang
- Department of Radiology, the Second Affiliated Hospital of Soochow University, 1055 Sanxiang road, Suzhou 215008, China
- Department of Radiology, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Xin Chen
- Department of Radiology, Shanghai Sixth People's Hospital, Shanghai 200233, China
| | - Wenjing Cui
- Department of Radiology, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Xinru Wang
- Department of Radiology, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Nandong Hu
- Department of Radiology, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Hongye Tang
- Department of Radiology, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Chao Zhang
- Department of Orthopaedics, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Jirong Shen
- Department of Orthopaedics, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Chao Xie
- Department of Orthopaedics, University of Rochester School of Medicine, NY 14642, USA
| | - Xiao Chen
- Department of Radiology, the Second Affiliated Hospital of Soochow University, 1055 Sanxiang road, Suzhou 215008, China
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Medical optimization of osteoporosis for adult spinal deformity surgery: a state-of-the-art evidence-based review of current pharmacotherapy. Spine Deform 2022; 11:579-596. [PMID: 36454531 DOI: 10.1007/s43390-022-00621-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 11/19/2022] [Indexed: 12/03/2022]
Abstract
PURPOSE Osteoporosis is a common, but challenging phenomenon to overcome in adult spinal deformity (ASD) surgery. Several pharmacological agents are at the surgeon's disposal to optimize the osteoporotic patient prior to undergoing extensive reconstruction. Familiarity with these medications will allow the surgeon to make informed decisions on selecting the most appropriate adjuncts for each individual patient. METHODS A comprehensive literature review was conducted in PubMed from September 2021 to April 2022. Studies were selected that contained combinations of various terms including osteoporosis, specific medications, spine surgery, fusion, cage subsidence, screw loosening, pull-out, junctional kyphosis/failure. RESULTS Bisphosphonates, denosumab, selective estrogen receptor modulators, teriparatide, abaloparatide and romosozumab are all pharmacological agents currently available for adjunctive use. While these medications have been shown to have beneficial effects on improving bone mineral density in the osteoporotic patient, varying evidence is available on their specific effects in the context of extensive spine surgery. There is still a lack of human studies with use of the newer agents. CONCLUSION Bisphosphonates are first-line agents due to their low cost and robust evidence behind their utility. However, in the absence of contraindications, optimizing bone quality with anabolic medications should be strongly considered in preparation for spinal deformity surgeries due to their beneficial and favorable effects on fusion and hardware compared to the anti-resorptive medications.
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25
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Zhou S, Chen S, Zhu X, You T, Li P, Shen H, Gao H, He Y, Zhang K. Associations between paraspinal muscles fatty infiltration and lumbar vertebral bone mineral density - An investigation by fast kVp switching dual-energy CT and QCT. Eur J Radiol Open 2022; 9:100447. [PMID: 36277658 PMCID: PMC9579482 DOI: 10.1016/j.ejro.2022.100447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 10/09/2022] [Accepted: 10/11/2022] [Indexed: 10/27/2022] Open
Abstract
Purpose To investigate the relationship between paraspinal muscles fat content and lumbar bone mineral density (BMD). Methods A total of 119 participants were enrolled in our study (60 males, age: 50.88 ± 17.79 years, BMI: 22.80 ± 3.80 kg·m-2; 59 females, age: 49.41 ± 17.69 years, BMI: 22.22 ± 3.12 kg·m-2). Fat content of paraspinal muscles (erector spinae (ES), multifidus (MS), and psoas (PS)) were measured at (ES L1/2-L4/5; MS L2/3-L5/S1; PS L2/3-L5/S1) levels using dual-energy computed tomography (DECT). Quantitative computed tomography (QCT) was used to assess BMD of L1 and L2. Linear regression analysis was used to assess the relationship between BMD of the lumbar spine and paraspinal muscles fat content with age, sex, and BMI. The variance inflation factor (VIF) was used to detect the degree of multicollinearity among the variables. P < .05 was considered to indicate a statistically significant difference. Results The paraspinal muscles fat content had a fairly significant inverse association with lumbar BMD after controlling for age, sex, and BMI (adjusted R 2 = 0.584-0.630, all P < .05). Conclusion Paraspinal muscles fat content was negatively associated with BMD.Paraspinal muscles fatty infiltration may be considered as a potential marker to identify BMD loss.
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Key Words
- ASiR-V, Adaptive statistical iterative reconstruction-Veo
- BIA, Bioimpedance analysis
- BMD, Bone mineral density
- Bone density
- CNR, Contrast-to-noise ratio
- DECT, Dual-energy computed tomography
- DXA, Dual-energy x-ray absorptiometry
- EMCL, extramyocellular lipids
- ES, Erector spinae
- FF, fat fraction
- FI %, Fatty infiltration ratio
- FM, Fat mass
- GSI, Gemstone spectral imaging
- IMCL, intramyocellular lipids
- LM, Lean mass
- MD, Material decomposition
- MRI, Magnetic resonance imaging
- MS, Multifidus
- MSK, Musculoskeletal
- Osteoporosis
- PDFF, Proton density fat fractions
- PS, Psoas
- Paraspinal muscles
- QCT, Quantitative computed tomography
- Tomography
- VIF, Variance inflation factor
- X-Ray computed
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Affiliation(s)
- Shuwei Zhou
- Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha 410007, PR China,The College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, 300 Xueshi Road, Yuelu District, Changsha 410208, PR China
| | - Suping Chen
- GE Healthcare (Shanghai) Co., Ltd., Shanghai 201203, PR China
| | - Xu Zhu
- The College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, 300 Xueshi Road, Yuelu District, Changsha 410208, PR China
| | - Tian You
- Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha 410007, PR China
| | - Ping Li
- Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha 410007, PR China
| | - Hongrong Shen
- Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha 410007, PR China
| | - Hui Gao
- Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha 410007, PR China
| | - Yewen He
- Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha 410007, PR China
| | - Kun Zhang
- Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha 410007, PR China,The College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, 300 Xueshi Road, Yuelu District, Changsha 410208, PR China,Corresponding author at: Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha 410007 PR China.
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Meena T, Roy S. Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift. Diagnostics (Basel) 2022; 12:diagnostics12102420. [PMID: 36292109 PMCID: PMC9600559 DOI: 10.3390/diagnostics12102420] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/04/2022] [Accepted: 10/05/2022] [Indexed: 01/16/2023] Open
Abstract
Bone diseases are common and can result in various musculoskeletal conditions (MC). An estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from musculoskeletal fractures, femoral neck injuries, knee osteoarthritis, and fractures are very common bone diseases, and the rate is expected to double in the next 30 years. Therefore, proper and timely diagnosis and treatment of a fractured patient are crucial. Contrastingly, missed fractures are a common prognosis failure in accidents and emergencies. This causes complications and delays in patients’ treatment and care. These days, artificial intelligence (AI) and, more specifically, deep learning (DL) are receiving significant attention to assist radiologists in bone fracture detection. DL can be widely used in medical image analysis. Some studies in traumatology and orthopaedics have shown the use and potential of DL in diagnosing fractures and diseases from radiographs. In this systematic review, we provide an overview of the use of DL in bone imaging to help radiologists to detect various abnormalities, particularly fractures. We have also discussed the challenges and problems faced in the DL-based method, and the future of DL in bone imaging.
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A Classification Method for Thoracolumbar Vertebral Fractures due to Basketball Sports Injury Based on Deep Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8747487. [PMID: 36245837 PMCID: PMC9556197 DOI: 10.1155/2022/8747487] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 08/30/2022] [Accepted: 09/15/2022] [Indexed: 11/17/2022]
Abstract
Objective There are more and more basketball competitions, to propose a classification method of thoracolumbar fractures to assist in the diagnosis of basketball injuries, to analyze the feasibility of its clinical application, and to improve the recovery rate. Methods From February 2015 to May 2022, 1130 CT images of thoracolumbar fractures admitted to our hospital and affiliated hospital units due to basketball injuries were collected, and the image labeling system uniformly labeled them. All CT images were classified according to the AO spine classification of thoracolumbar injuries. In the ABC-type classification, 935 CT images were used for training and validation to optimize the deep learning system, including 815 training sets and 120 validation sets; the remaining 198 CT images were used as test sets for comparing the deep learning system and clinician's diagnosis. In the classification of subtype A, a total of 523 CT scans can be performed for training and validation to optimize the deep learning system, including 500 training sets and 23 validation sets; the remaining 94 CT images are used as test sets for comparing depth learning systems and clinicians' diagnostic results. Results The deep learning system had a correct rate of ABC classification of fractures in 86.4%, with a kappa coefficient of 0.850 (P < 0.001); the correct rate of subtype A was 85.3%, with a kappa coefficient of 0.815 (P < 0.001). Conclusion The classification accuracy of thoracolumbar fractures based on deep learning is high. The method can assist in diagnosing CT images of thoracolumbar fractures and improve the current manual and complex diagnosis process.
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Kim AY, Yoon MA, Ham SJ, Cho YC, Ko Y, Park B, Kim S, Lee E, Lee RW, Chee CG, Lee MH, Lee SH, Chung HW. Prediction of the Acuity of Vertebral Compression Fractures on CT Using Radiologic and Radiomic Features. Acad Radiol 2022; 29:1512-1520. [PMID: 34998683 DOI: 10.1016/j.acra.2021.12.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/08/2021] [Accepted: 12/08/2021] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES To develop and validate prediction models to differentiate acute and chronic vertebral compression fractures based on radiologic and radiomic features on CT. MATERIALS AND METHODS This study included acute and chronic compression fractures in patients who underwent both spine CT and MRI examinations. For each fractured vertebra, three CT findings ([1] cortical disruption, [2] hypoattenuating cleft or sclerotic line, and [3] relative bone marrow attenuation) were assessed by two radiologists. A radiomic score was built from 280 radiomic features extracted from non-contrast-enhanced CT images. Weighted multivariable logistic regression analysis was performed to build a radiologic model based on CT findings and an integrated model combining the radiomic score and CT findings. Model performance was evaluated and compared. Models were externally validated using an independent test cohort. RESULTS A total to 238 fractures (159 acute and 79 chronic) in 122 patients and 58 fractures (39 acute and 19 chronic) in 32 patients were included in the training and test cohorts, respectively. The AUC of the radiomic score was 0.95 in the training and 0.93 in the test cohorts. The AUC of the radiologic model was 0.89 in the training and 0.83 in the test cohorts. The discriminatory performance of the integrated model was significantly higher than the radiologic model in both the training (AUC, 0.97; p<0.01) and the test (AUC, 0.95; p=0.01) cohorts. CONCLUSION Combining radiomics with radiologic findings significantly improved the performance of CT in determining the acuity of vertebral compression fractures.
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Affiliation(s)
- A Yeon Kim
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Min A Yoon
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea.
| | - Su Jung Ham
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Young Chul Cho
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Yousun Ko
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Bumwoo Park
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Seonok Kim
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Eugene Lee
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Ro Woon Lee
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Choong Guen Chee
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Min Hee Lee
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Sang Hoon Lee
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Hye Won Chung
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
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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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Curtis EM, Dennison EM, Cooper C, Harvey NC. Osteoporosis in 2022: Care gaps to screening and personalised medicine. Best Pract Res Clin Rheumatol 2022; 36:101754. [PMID: 35691824 PMCID: PMC7614114 DOI: 10.1016/j.berh.2022.101754] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Osteoporosis care has evolved markedly over the last 50 years, such that there are now an established clinical definition, validated methods of fracture risk assessment, and a range of effective pharmacological agents. However, it is apparent that both in the context of primary and secondary fracture prevention, there is a considerable gap between the population at high fracture risk and those actually receiving appropriate antiosteoporosis treatment. In this narrative review article, we document recent work describing the burden of disease, approaches to management, and service provision across Europe, emerging data on gaps in care, and existing/new ways in which these gaps may be addressed at the level of healthcare systems and policy. We conclude that although the field has come a long way in recent decades, there is still a long way to go, and a concerted, integrated effort is now required from all of us involved in this field to address these urgent issues to ensure the best possible outcomes for our patients.
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Affiliation(s)
- Elizabeth M Curtis
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton General Hospital, Southampton, UK
| | - Elaine M Dennison
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton General Hospital, Southampton, UK
| | - Cyrus Cooper
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton General Hospital, Southampton, UK; NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospitals Southampton NHS Foundation Trust, Southampton, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton General Hospital, Southampton, UK; NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospitals Southampton NHS Foundation Trust, Southampton, UK.
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Wang R, Xi Y, Yang M, Zhu M, Yang F, Xu H. Whole-volume ADC histogram of the brain as an image biomarker in evaluating disease severity of neonatal hypoxic-ischemic encephalopathy. Front Neurol 2022; 13:918554. [PMID: 35989925 PMCID: PMC9381875 DOI: 10.3389/fneur.2022.918554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/07/2022] [Indexed: 11/19/2022] Open
Abstract
Purpose To examine the diagnostic significance of the apparent diffusion coefficient (ADC) histogram in quantifying neonatal hypoxic ischemic encephalopathy (HIE). Methods An analysis was conducted on the MRI data of 90 HIE patients, 49 in the moderate-to-severe group, and the other in the mild group. The 3D Slicer software was adopted to delineate the whole brain region as the region of interest, and 22 ADC histogram parameters were obtained. The interobserver consistency of the two radiologists was assessed by the interclass correlation coefficient (ICC). The difference in parameters (ICC > 0.80) between the two groups was compared by performing the independent sample t-test or the Mann–Whitney U test. In addition, an investigation was conducted on the correlation between parameters and the neonatal behavioral neurological assessment (NBNA) score. The ROC curve was adopted to assess the efficacy of the respective significant parameters. Furthermore, the binary logistic regression was employed to screen out the independent risk factors for determining the severity of HIE. Results The ADCmean, ADCmin, ADCmax,10th−70th, 90th percentile of ADC values of the moderate-to-severe group were smaller than those of the mild group, while the group's variance, skewness, kurtosis, heterogeneity, and mode-value were higher than those of the mild group (P < 0.05). All the mentioned parameters, the ADCmean, ADCmin, and 10th−70th and 90th percentile of ADC displayed positive correlations with the NBNA score, mode-value and ADCmax displayed no correlations with the NBNA score, the rest showed negative correlations with the NBNA score (P < 0.05). The area under the curve (AUC) of variance was the largest (AUC = 0.977; cut-off 972.5, sensitivity 95.1%; specificity 87.8%). According to the logistic regression analysis, skewness, kurtosis, variance, and heterogeneity were independent risk factors for determining the severity of HIE (OR > 1, P < 0.05). Conclusions The ADC histogram contributes to the HIE diagnosis and is capable of indicating the diffusion information of the brain objectively and quantitatively. It refers to a vital method for assessing the severity of HIE.
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Hornung AL, Hornung CM, Mallow GM, Barajas JN, Rush A, Sayari AJ, Galbusera F, Wilke HJ, Colman M, Phillips FM, An HS, Samartzis D. Artificial intelligence in spine care: current applications and future utility. 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; 31:2057-2081. [PMID: 35347425 DOI: 10.1007/s00586-022-07176-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/18/2022] [Accepted: 03/08/2022] [Indexed: 01/20/2023]
Abstract
PURPOSE The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefore, the current review sought to synthesize findings from the literature at the interface of artificial intelligence and spine research. METHODS A narrative review was performed based on the literature of three databases (MEDLINE, CINAHL, and Scopus) from January 2015 to March 2021 that examined historical and recent advancements in the understanding of artificial intelligence and machine learning in spine research. Studies were appraised for their role in, or description of, advancements within image recognition and predictive modeling for spinal research. Only English articles that fulfilled inclusion criteria were ultimately incorporated in this review. RESULTS This review briefly summarizes the history and applications of artificial intelligence and machine learning in spine. Three basic machine learning training paradigms: supervised learning, unsupervised learning, and reinforced learning are also discussed. Artificial intelligence and machine learning have been utilized in almost every facet of spine ranging from localization and segmentation techniques in spinal imaging to pathology specific algorithms which include but not limited to; preoperative risk assessment of postoperative complications, screening algorithms for patients at risk of osteoporosis and clustering analysis to identify subgroups within adolescent idiopathic scoliosis. The future of artificial intelligence and machine learning in spine surgery is also discussed with focusing on novel algorithms, data collection techniques and increased utilization of automated systems. CONCLUSION Improvements to modern-day computing and accessibility to various imaging modalities allow for innovative discoveries that may arise, for example, from management. Given the imminent future of AI in spine surgery, it is of great importance that practitioners continue to inform themselves regarding AI, its goals, use, and progression. In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.
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Affiliation(s)
- Alexander L Hornung
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | | | - G Michael Mallow
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - J Nicolás Barajas
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Augustus Rush
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Arash J Sayari
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | | | - Hans-Joachim Wilke
- Institute of Orthopaedic Research and Biomechanics, Trauma Research Center Ulm, Ulm University, Ulm, Germany
| | - Matthew Colman
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Frank M Phillips
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Howard S An
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Dino Samartzis
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA.
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Kong SH, Lee JW, Bae BU, Sung JK, Jung KH, Kim JH, Shin CS. Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm. Endocrinol Metab (Seoul) 2022; 37:674-683. [PMID: 35927066 PMCID: PMC9449110 DOI: 10.3803/enm.2022.1461] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/20/2022] [Indexed: 11/20/2022] Open
Abstract
BACKGRUOUND Since image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data. METHODS This study included 1,595 participants aged 50 to 75 years with at least two lumbosacral radiographs without baseline fractures from 2010 to 2015 at Seoul National University Hospital. Positive and negative cases were defined according to whether vertebral fractures developed during follow-up. The cases were divided into training (n=1,416) and test (n=179) sets. A convolutional neural network (CNN)-based prediction algorithm, DeepSurv, was trained with images and baseline clinical information (age, sex, body mass index, glucocorticoid use, and secondary osteoporosis). The concordance index (C-index) was used to compare performance between DeepSurv and the Fracture Risk Assessment Tool (FRAX) and Cox proportional hazard (CoxPH) models. RESULTS Of the total participants, 1,188 (74.4%) were women, and the mean age was 60.5 years. During a mean follow-up period of 40.7 months, vertebral fractures occurred in 7.5% (120/1,595) of participants. In the test set, when DeepSurv learned with images and clinical features, it showed higher performance than FRAX and CoxPH in terms of C-index values (DeepSurv, 0.612; 95% confidence interval [CI], 0.571 to 0.653; FRAX, 0.547; CoxPH, 0.594; 95% CI, 0.552 to 0.555). Notably, the DeepSurv method without clinical features had a higher C-index (0.614; 95% CI, 0.572 to 0.656) than that of FRAX in women. CONCLUSION DeepSurv, a CNN-based prediction algorithm using baseline image and clinical information, outperformed the FRAX and CoxPH models in predicting osteoporotic fracture from spine radiographs in a longitudinal cohort.
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Affiliation(s)
- Sung Hye Kong
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | | | | | | | | | - Jung Hee Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Corresponding author: Jung Hee Kim. Department of Internal Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea Tel: +82-2-2072-4839, Fax: +82-2-2072-7246, E-mail:
| | - Chan Soo Shin
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
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Liu J, Tang J, Xia B, Gu Z, Yin H, Zhang H, Yang H, Song B. Novel Radiomics-Clinical Model for the Noninvasive Prediction of New Fractures After Vertebral Augmentation. Acad Radiol 2022; 30:1092-1100. [PMID: 35915030 DOI: 10.1016/j.acra.2022.06.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/25/2022] [Accepted: 06/26/2022] [Indexed: 11/16/2022]
Abstract
PURPOSE To investigate the noninvasive prediction model for new fractures after percutaneous vertebral augmentation (PVA) based on radiomics signature and clinical parameters. METHODS Data from patients who were diagnosed with osteoporotic vertebral compression fracture (OVCF) and treated with PVA in our hospital between May 2014 and April 2019 were retrospectively analyzed. Radiomics features were extracted from T1-weighted magnetic resonance imaging (MRI) of the T11-L5 segments taken before PVA. Different radiomics models was developed by using linear discriminant analysis (LDA), multilayer perceptron (MLP), and stochastic gradient descent (SGD) classifiers. A nomogram was constructed by integrating clinical parameters and Radscore that calculated by the best radiomics model. The model performance was quantified in terms of discrimination, calibration and clinical usefulness. RESULT Four clinical parameters and 16 selected radiomics features were used for model development. The clinical model showed poor discrimination capability with area under the curves (AUCs) yielding of 0.522 in the training dataset and 0.517 in the validation dataset. The LDA, MLP and SGD classifier-based radiomics model had achieved AUCs of 0.793, 0.810, and 0.797 in the training dataset, and 0.719, 0.704, and 0.725 in the validation dataset, respectively. The nomogram showed the best performance with AUCs achieving 0.810 and 0.754 in the training and validation datasets, respectively. The decision curve analysis demonstrated the net benefit of the nomogram was higher than that of other models. CONCLUSION Our findings indicate that combining clinical features with radiomics features from pre-augmentation T1-weighted MRI can be used to develop a nomogram that can predict new fractures in patients after PVA.
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Sollmann N, Kirschke JS, Kronthaler S, Boehm C, Dieckmeyer M, Vogele D, Kloth C, Lisson CG, Carballido-Gamio J, Link TM, Karampinos DC, Karupppasamy S, Beer M, Krug R, Baum T. Imaging of the Osteoporotic Spine - Quantitative Approaches in Diagnostics and for the Prediction of the Individual Fracture Risk. ROFO-FORTSCHR RONTG 2022; 194:1088-1099. [PMID: 35545103 DOI: 10.1055/a-1770-4626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Osteoporosis is a highly prevalent systemic skeletal disease that is characterized by low bone mass and microarchitectural bone deterioration. It predisposes to fragility fractures that can occur at various sites of the skeleton, but vertebral fractures (VFs) have been shown to be particularly common. Prevention strategies and timely intervention depend on reliable diagnosis and prediction of the individual fracture risk, and dual-energy X-ray absorptiometry (DXA) has been the reference standard for decades. Yet, DXA has its inherent limitations, and other techniques have shown potential as viable add-on or even stand-alone options. Specifically, three-dimensional (3 D) imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), are playing an increasing role. For CT, recent advances in medical image analysis now allow automatic vertebral segmentation and value extraction from single vertebral bodies using a deep-learning-based architecture that can be implemented in clinical practice. Regarding MRI, a variety of methods have been developed over recent years, including magnetic resonance spectroscopy (MRS) and chemical shift encoding-based water-fat MRI (CSE-MRI) that enable the extraction of a vertebral body's proton density fat fraction (PDFF) as a promising surrogate biomarker of bone health. Yet, imaging data from CT or MRI may be more efficiently used when combined with advanced analysis techniques such as texture analysis (TA; to provide spatially resolved assessments of vertebral body composition) or finite element analysis (FEA; to provide estimates of bone strength) to further improve fracture prediction. However, distinct and experimentally validated diagnostic criteria for osteoporosis based on CT- and MRI-derived measures have not yet been achieved, limiting broad transfer to clinical practice for these novel approaches. KEY POINTS:: · DXA is the reference standard for diagnosis and fracture prediction in osteoporosis, but it has important limitations.. · CT- and MRI-based methods are increasingly used as (opportunistic) approaches.. · For CT, particularly deep-learning-based automatic vertebral segmentation and value extraction seem promising.. · For MRI, multiple techniques including spectroscopy and chemical shift imaging are available to extract fat fractions.. · Texture and finite element analyses can provide additional measures for vertebral body composition and bone strength.. CITATION FORMAT: · Sollmann N, Kirschke JS, Kronthaler S et al. Imaging of the Osteoporotic Spine - Quantitative Approaches in Diagnostics and for the Prediction of the Individual Fracture Risk. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1770-4626.
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Affiliation(s)
- Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States.,Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan Stefan Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Sophia Kronthaler
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Christof Boehm
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Daniel Vogele
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Christopher Kloth
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | | | - Julio Carballido-Gamio
- Department of Radiology, University of Colorado - Anschutz Medical Campus, Aurora, CO, United States
| | - Thomas Marc Link
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States
| | - Dimitrios Charalampos Karampinos
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Subburaj Karupppasamy
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design, Singapore.,Sobey School of Business, Saint Mary's University, Halifax, NS, Canada
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Roland Krug
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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Automated segmentation of the fractured vertebrae on CT and its applicability in a radiomics model to predict fracture malignancy. Sci Rep 2022; 12:6735. [PMID: 35468985 PMCID: PMC9038736 DOI: 10.1038/s41598-022-10807-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 04/13/2022] [Indexed: 11/08/2022] Open
Abstract
Although CT radiomics has shown promising results in the evaluation of vertebral fractures, the need for manual segmentation of fractured vertebrae limited the routine clinical implementation of radiomics. Therefore, automated segmentation of fractured vertebrae is needed for successful clinical use of radiomics. In this study, we aimed to develop and validate an automated algorithm for segmentation of fractured vertebral bodies on CT, and to evaluate the applicability of the algorithm in a radiomics prediction model to differentiate benign and malignant fractures. A convolutional neural network was trained to perform automated segmentation of fractured vertebral bodies using 341 vertebrae with benign or malignant fractures from 158 patients, and was validated on independent test sets (internal test, 86 vertebrae [59 patients]; external test, 102 vertebrae [59 patients]). Then, a radiomics model predicting fracture malignancy on CT was constructed, and the prediction performance was compared between automated and human expert segmentations. The algorithm achieved good agreement with human expert segmentation at testing (Dice similarity coefficient, 0.93-0.94; cross-sectional area error, 2.66-2.97%; average surface distance, 0.40-0.54 mm). The radiomics model demonstrated good performance in the training set (AUC, 0.93). In the test sets, automated and human expert segmentations showed comparable prediction performances (AUC, internal test, 0.80 vs 0.87, p = 0.044; external test, 0.83 vs 0.80, p = 0.37). In summary, we developed and validated an automated segmentation algorithm that showed comparable performance to human expert segmentation in a CT radiomics model to predict fracture malignancy, which may enable more practical clinical utilization of radiomics.
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Park H, Lee SY, Lee J, Pak J, Lee K, Lee SE, Jung JY. Detecting Multiple Myeloma Infiltration of the Bone Marrow on CT Scans in Patients with Osteopenia: Feasibility of Radiomics Analysis. Diagnostics (Basel) 2022; 12:diagnostics12040923. [PMID: 35453971 PMCID: PMC9025143 DOI: 10.3390/diagnostics12040923] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 03/30/2022] [Accepted: 04/04/2022] [Indexed: 02/04/2023] Open
Abstract
It is difficult to detect multiple myeloma (MM) infiltration of the bone marrow on computed tomography (CT) scans of patients with osteopenia. Our aim is to determine the feasibility of using radiomics analysis to detect MM infiltration of the bone marrow on CT scans of patients with osteopenia. The contrast-enhanced thoracic CT scans of 104 patients with MM and 104 age- and sex-matched controls were retrospectively evaluated. All individuals had decreased bone density on radiography. The study group was divided into development (n = 160) and temporal validation sets (n = 48). The radiomics model was developed using 805 texture features extracted from the bone marrow for a development set, using a Random Forest algorithm. The developed models were applied to evaluate a temporal validation set. For comparison, three radiologists evaluated the CTs for the possibility of MM infiltration in the bone marrow. The diagnostic performances were assessed and compared using an area under the receiver operating characteristic curve (AUC) analysis. The AUC of the radiomics model was not significantly different from those of the radiologists (p = 0.056–0.821). The radiomics analysis results showed potential for detecting MM infiltration in the bone marrow on CT scans of patients with osteopenia.
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Affiliation(s)
- Hyerim Park
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (H.P.); (J.L.); (K.L.); (S.-E.L.); (J.-Y.J.)
- Department of Radiology, Soonchunhyang University Cheoan Hospital, Cheonan 31151, Korea;
| | - So-Yeon Lee
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (H.P.); (J.L.); (K.L.); (S.-E.L.); (J.-Y.J.)
- Correspondence: ; Tel.: +82-2-2258-6743
| | - Jooyeon Lee
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (H.P.); (J.L.); (K.L.); (S.-E.L.); (J.-Y.J.)
- Department of Applied Statistics, Hanyang University, Seoul 04763, Korea
| | - Juyoung Pak
- Department of Radiology, Soonchunhyang University Cheoan Hospital, Cheonan 31151, Korea;
| | - Koeun Lee
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (H.P.); (J.L.); (K.L.); (S.-E.L.); (J.-Y.J.)
| | - Seung-Eun Lee
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (H.P.); (J.L.); (K.L.); (S.-E.L.); (J.-Y.J.)
| | - Joon-Yong Jung
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (H.P.); (J.L.); (K.L.); (S.-E.L.); (J.-Y.J.)
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38
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Abdali SH, Afzali F, Baseri S, Abdalvand N, Abdollahi H. Bone radiomics reproducibility: a three-centered study on the impacts of image contrast, edge enhancement, and latitude variations. Phys Eng Sci Med 2022; 45:497-511. [PMID: 35389137 DOI: 10.1007/s13246-022-01116-4] [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: 08/02/2021] [Accepted: 03/01/2022] [Indexed: 11/25/2022]
Abstract
This study aims to measure the reproducibility of radiomics features in ankle bone radiography over changes in post-processing parameters including contrast, edge enhancement and latitude. Lateral ankle bone radiographies for sixty patients were obtained from three digital radiology centers. All images were acquired by same image acquisition settings. A two-dimensional region of interest was drawn in any image and 93 features from 6 feature sets including first and second order were extracted. The coefficient of variation (COV) and intraclass correlation coefficient (ICC) were calculated to assess feature reproducibility for each center and among all centers in three scenarios: Adams (Nat Rev Endocrinol 9(1):28, 2013) ten different contrast Brown et al. (J Med Imaging 5(1):011017, 2018) ten different edge enhancement and Hirvasniemi et al. (Osteoarthr Cartilage 27(6):906-914, 2019) ten different image latitude parameters. Based on ICC analysis, it is observed that 46-100-44% of Histogram, 54-72-42% of GLCM, 43-76-36% of GLDM, 60-90-17% of GLRLM, 33-19-21% of GLSZM and 13-20-0% of NGTDM radiomics features had 90% < ICC < 100% over changes in contrast-edge enhancement-latitude changes respectively. Based on COV, GLRLM was only feature set that 100% of their features had COV ≤ 5% over changes in contrast and edge enhancement. The results presented here, indicating that radiomics features extracted are vulnerable over changes in contrast, edge enhancement and latitude. The most reproducible features that introduced in this study could be used for further clinical decision making.
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Affiliation(s)
- Seyed Hamid Abdali
- Student Research Committee, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Firoozeh Afzali
- Student Research Committee, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Saeid Baseri
- Student Research Committee, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Neda Abdalvand
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, P.O. Box: 15785 - 6171, Junction of Shahid Hemmat & Shahid Chamran Expressways, 14496, Tehran, Iran.
| | - Hamid Abdollahi
- Student Research Committee, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran.,Department of Radiologic Technology, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
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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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40
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Scheyerer MJ, Spiegl UJA, Grueninger S, Hartmann F, Katscher S, Osterhoff G, Perl M, Pumberger M, Schmeiser G, Ullrich BW, Schnake KJ. Risk Factors for Failure in Conservatively Treated Osteoporotic Vertebral Fractures: A Systematic Review. Global Spine J 2022; 12:289-297. [PMID: 33541142 PMCID: PMC8907647 DOI: 10.1177/2192568220982279] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
STUDY DESIGN Systematic review. OBJECTIVES Osteoporosis is one of the most common diseases of the elderly, whereby vertebral body fractures are in many cases the first manifestation. Even today, the consequences for patients are underestimated. Therefore, early identification of therapy failures is essential. In this context, the aim of the present systematic review was to evaluate the current literature with respect to clinical and radiographic findings that might predict treatment failure. METHODS We conducted a comprehensive, systematic review of the literature according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) checklist and algorithm. RESULTS After the literature search, 724 potentially eligible investigations were identified. In total, 24 studies with 3044 participants and a mean follow-up of 11 months (range 6-27.5 months) were included. Patient-specific risk factors were age >73 years, bone mineral density with a t-score <-2.95, BMI >23 and a modified frailty index >2.5. The following radiological and fracture-specific risk factors could be identified: involvement of the posterior wall, initial height loss, midportion type fracture, development of an intravertebral cleft, fracture at the thoracolumbar junction, fracture involvement of both endplates, different morphological types of fractures, and specific MRI findings. Further, a correlation between sagittal spinal imbalance and treatment failure could be demonstrated. CONCLUSION In conclusion, this systematic review identified various factors that predict treatment failure in conservatively treated osteoporotic fractures. In these cases, additional treatment options and surgical treatment strategies should be considered in addition to follow-up examinations.
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Affiliation(s)
- Max J. Scheyerer
- Department of Orthopedic and Trauma
Surgery, Medical Faculty, University of Cologne, Cologne, Germany,Max J. Scheyerer, PD Dr., Department of
Orthopedic and Trauma Surgery, Medical Faculty, University of Cologne, Kerpener
Straße 62, 50937 Cologne, Germany.
| | - Ulrich J. A. Spiegl
- Department of Orthopaedics, Trauma
Surgery and Plastic Surgery, University Hospital Leipzig, Sachsen Germany
| | - Sebastian Grueninger
- Department of Orthopaedics and
Trauma Surgery, University Hospital, Paracelsus University, Hospital Nürnberg,
Nuernberg, Germany
| | - Frank Hartmann
- Department of Orthopaedics and
Trauma Surgery, Ev.Stift St. Martin, Hospital Mittelrhein, Koblenz,
Germany
| | | | - Georg Osterhoff
- Department of Orthopaedics, Trauma
Surgery and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Mario Perl
- Department of Trauma Surgery,
University Hospital Erlangen, Erlangen, Germany
| | - Matthias Pumberger
- Spine Department, Center for
Musculoskeletal Surgery, Charité University Medicine Berlin, Berlin,
Germany
| | - Gregor Schmeiser
- Center for Spine Therapy, Schön
Klinik Hamburg Eilbeck, Hamburg, Germany
| | - Bernhard W. Ullrich
- Department of Trauma and Plastic
Surgery, University Hospital Jena, Jena, Germany
| | - Klaus J. Schnake
- Center for Spine and Scoliosis
Therapy, Malteser Waldkrankenhaus St. Marien, Erlangen, Bayern, Germany,Department of Orthopedics and
Traumatology, Paracelsus Private Medical University Nuremberg, Nuremberg,
Germany
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41
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Liu L, Si M, Ma H, Cong M, Xu Q, Sun Q, Wu W, Wang C, Fagan MJ, Mur LAJ, Yang Q, Ji B. A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images. BMC Bioinformatics 2022; 23:63. [PMID: 35144529 PMCID: PMC8829991 DOI: 10.1186/s12859-022-04596-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 02/02/2022] [Indexed: 01/10/2023] Open
Abstract
Background Osteoporosis is a common metabolic skeletal disease and usually lacks obvious symptoms. Many individuals are not diagnosed until osteoporotic fractures occur. Bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis detection. However, only a limited percentage of people with osteoporosis risks undergo the DXA test. As a result, it is vital to develop methods to identify individuals at-risk based on methods other than DXA. Results We proposed a hierarchical model with three layers to detect osteoporosis using clinical data (including demographic characteristics and routine laboratory tests data) and CT images covering lumbar vertebral bodies rather than DXA data via machine learning. 2210 individuals over age 40 were collected retrospectively, among which 246 individuals’ clinical data and CT images are both available. Irrelevant and redundant features were removed via statistical analysis. Consequently, 28 features, including 16 clinical data and 12 texture features demonstrated statistically significant differences (p < 0.05) between osteoporosis and normal groups. Six machine learning algorithms including logistic regression (LR), support vector machine with radial-basis function kernel, artificial neural network, random forests, eXtreme Gradient Boosting and Stacking that combined the above five classifiers were employed as classifiers to assess the performances of the model. Furthermore, to diminish the influence of data partitioning, the dataset was randomly split into training and test set with stratified sampling repeated five times. The results demonstrated that the hierarchical model based on LR showed better performances with an area under the receiver operating characteristic curve of 0.818, 0.838, and 0.962 for three layers, respectively in distinguishing individuals with osteoporosis and normal BMD. Conclusions The proposed model showed great potential in opportunistic screening for osteoporosis without additional expense. It is hoped that this model could serve to detect osteoporosis as early as possible and thereby prevent serious complications of osteoporosis, such as osteoporosis fractures. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04596-z.
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Affiliation(s)
- Liyu Liu
- School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong, People's Republic of China
| | - Meng Si
- Department of Orthopedics, Qilu Hospital of Shandong University, Jinan, Shandong, People's Republic of China
| | - Hecheng Ma
- Department of Orthopedics, Qilu Hospital of Shandong University, Jinan, Shandong, People's Republic of China
| | - Menglin Cong
- Department of Orthopedics, Qilu Hospital of Shandong University, Jinan, Shandong, People's Republic of China
| | - Quanzheng Xu
- School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong, People's Republic of China
| | - Qinghua Sun
- School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong, People's Republic of China
| | - Weiming Wu
- School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong, People's Republic of China
| | - Cong Wang
- School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong, People's Republic of China
| | - Michael J Fagan
- School of Engineering, University of Hull, Hull, HU6 7RX, UK
| | - Luis A J Mur
- Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, Aberystwyth, Wales, UK
| | - Qing Yang
- Department of Breast and Thyroid, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China
| | - Bing Ji
- School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong, People's Republic of China.
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Huber FA, Guggenberger R. AI MSK clinical applications: spine imaging. Skeletal Radiol 2022; 51:279-291. [PMID: 34263344 PMCID: PMC8692301 DOI: 10.1007/s00256-021-03862-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 06/28/2021] [Accepted: 07/03/2021] [Indexed: 02/02/2023]
Abstract
Recent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain in spine imaging, independent from modality or specific application. This review aims to summarize the status quo and future perspective regarding utilization of AI for spine imaging. First, the basics of AI concepts are clarified. Second, the different tasks and use cases for AI applications in spine imaging are discussed and illustrated by examples. Finally, the authors of this review present their personal perception of AI in daily imaging and discuss future chances and challenges that come along with AI-based solutions.
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Affiliation(s)
- Florian A. Huber
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland
| | - Roman Guggenberger
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland
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Use of machine learning to select texture features in investigating the effects of axial loading on T 2-maps from magnetic resonance imaging of the lumbar discs. 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; 31:1979-1991. [PMID: 34718864 DOI: 10.1007/s00586-021-07036-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 09/20/2021] [Accepted: 10/18/2021] [Indexed: 11/27/2022]
Abstract
BACKGROUND Recent advances in texture analysis and machine learning offer new opportunities to improve the application of imaging to intervertebral disc biomechanics. This study employed texture analysis and machine learning on MRIs to investigate the lumbar disc's response to loading. METHODS Thirty-five volunteers (30 (SD 11) yrs.) with and without chronic back pain spent 20 min lying in a relaxed unloaded supine position, followed by 20 min loaded in compression, and then 20 min with traction applied. T2-weighted MR images were acquired during the last 5 min of each loading condition. Custom image analysis software was used to segment discs from adjacent tissues semi-automatically and segment each disc into the nucleus, anterior and posterior annulus automatically. A grey-level, co-occurrence matrix with one to four pixels offset in four directions (0°, 45°, 90° and 135°) was then constructed (320 feature/tissue). The Random Forest Algorithm was used to select the most promising classifiers. Linear mixed-effect models and Cohen's d compared loading conditions. FINDINGS All statistically significant differences (p < 0.001) were observed in the nucleus and posterior annulus in the 135° offset direction at the L4-5 level between lumbar compression and traction. Correlation (P2-Offset, P4-Offset) and information measure of correlation 1 (P3-Offset, P4-Offset) detected significant changes in the nucleus. Statistically significant changes were also observed for homogeneity (P2-Offset, P3-Offset), contrast (P2-Offset), and difference variance (P4-Offset) of the posterior annulus. INTERPRETATION MRI textural features may have the potential of identifying the disc's response to loading, particularly in the nucleus and posterior annulus, which appear most sensitive to loading. LEVEL OF EVIDENCE Diagnostic: individual cross-sectional studies with consistently applied reference standard and blinding.
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Kong SH, Shin CS. Applications of Machine Learning in Bone and Mineral Research. Endocrinol Metab (Seoul) 2021; 36:928-937. [PMID: 34674509 PMCID: PMC8566132 DOI: 10.3803/enm.2021.1111] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 08/23/2021] [Accepted: 09/09/2021] [Indexed: 12/26/2022] Open
Abstract
In this unprecedented era of the overwhelming volume of medical data, machine learning can be a promising tool that may shed light on an individualized approach and a better understanding of the disease in the field of osteoporosis research, similar to that in other research fields. This review aimed to provide an overview of the latest studies using machine learning to address issues, mainly focusing on osteoporosis and fractures. Machine learning models for diagnosing and classifying osteoporosis and detecting fractures from images have shown promising performance. Fracture risk prediction is another promising field of research, and studies are being conducted using various data sources. However, these approaches may be biased due to the nature of the techniques or the quality of the data. Therefore, more studies based on the proposed guidelines are needed to improve the technical feasibility and generalizability of artificial intelligence algorithms.
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Affiliation(s)
- Sung Hye Kong
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul,
Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam,
Korea
| | - Chan Soo Shin
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul,
Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul,
Korea
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Dieckmeyer M, Rayudu NM, Yeung LY, Löffler M, Sekuboyina A, Burian E, Sollmann N, Kirschke JS, Baum T, Subburaj K. Prediction of incident vertebral fractures in routine MDCT: Comparison of global texture features, 3D finite element parameters and volumetric BMD. Eur J Radiol 2021; 141:109827. [PMID: 34225250 DOI: 10.1016/j.ejrad.2021.109827] [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: 04/21/2021] [Revised: 06/06/2021] [Accepted: 06/14/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE In this case-control study, we evaluated different quantitative parameters derived from routine multi-detector computed tomography (MDCT) scans with respect to their ability to predict incident osteoporotic vertebral fractures of the thoracolumbar spine. METHODS 16 patients who received baseline and follow-up contrast-enhanced MDCT and were diagnosed with an incident osteoporotic vertebral fracture at follow-up, and 16 age-, sex-, and follow-up-time-matched controls were included in the study. Vertebrae were labelled and segmented using a fully automated pipeline. Volumetric bone mineral density (vBMD), finite element analysis (FEA)-based failure load (FL) and failure displacement (FD), as well as 24 texture features were extracted from L1 - L3 and averaged. Odds ratios (OR) with 95% confidence intervals (CI), expressed per standard deviation decrease, receiver operating characteristic (ROC) area under the curve (AUC), as well as logistic regression models, including all analyzed parameters as independent variables, were used to assess the prediction of incident vertebral fractures. RESULTS The texture feature Correlation (AUC = 0.754, p = 0.014; OR = 2.76, CI = 1.16-6.58) and vBMD (AUC = 0.750, p = 0.016; OR = 2.67, CI = 1.12-6.37) classified incident vertebral fractures best, while the best FEA-based parameter FL showed an AUC = 0.719 (p = 0.035). Correlation was the only significant predictor of incident fractures in the logistic regression analysis of all parameters (p = 0.022). CONCLUSION MDCT-derived FEA parameters and texture features, averaged from L1 - L3, showed only a moderate, but no statistically significant improvement of incident vertebral fracture prediction beyond BMD, supporting the hypothesis that vertebral-specific parameters may be superior for fracture risk assessment.
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Affiliation(s)
- Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Nithin Manohar Rayudu
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.
| | - Long Yu Yeung
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.
| | - Maximilian Löffler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; Department of Radiology, University Medical Center, Albert-Ludwigs-University Freiburg, Hugstetter Str. 55, 79106 Freiburg, Germany.
| | - Anjany Sekuboyina
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Egon Burian
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany.
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Karupppasamy Subburaj
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; Changi General Hospital, 2 Simei Street 3, Singapore 529889, Singapore.
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Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies. Sci Rep 2021; 11:9821. [PMID: 33972636 PMCID: PMC8110584 DOI: 10.1038/s41598-021-89311-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 04/15/2021] [Indexed: 12/15/2022] Open
Abstract
To develop a machine learning (ML) model that predicts disease groups or autoantibodies in patients with idiopathic inflammatory myopathies (IIMs) using muscle MRI radiomics features. Twenty-two patients with dermatomyositis (DM), 14 with amyopathic dermatomyositis (ADM), 19 with polymyositis (PM) and 19 with non-IIM were enrolled. Using 2D manual segmentation, 93 original features as well as 93 local binary pattern (LBP) features were extracted from MRI (short-tau inversion recovery [STIR] imaging) of proximal limb muscles. To construct and compare ML models that predict disease groups using each set of features, dimensional reductions were performed using a reproducibility analysis by inter-reader and intra-reader correlation coefficients, collinearity analysis, and the sequential feature selection (SFS) algorithm. Models were created using the linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM), k-nearest neighbors (k-NN), random forest (RF) and multi-layer perceptron (MLP) classifiers, and validated using tenfold cross-validation repeated 100 times. We also investigated whether it was possible to construct models predicting autoantibody status. Our ML-based MRI radiomics models showed the potential to distinguish between PM, DM, and ADM. Models using LBP features provided better results, with macro-average AUC values of 0.767 and 0.714, accuracy of 61.2 and 61.4%, and macro-average recall of 61.9 and 59.8%, in the LDA and k-NN classifiers, respectively. In contrast, the accuracies of radiomics models distinguishing between non-IIM and IIM disease groups were low. A subgroup analysis showed that classification models for anti-Jo-1 and anti-ARS antibodies provided AUC values of 0.646–0.853 and 0.692–0.792, with accuracy of 71.5–81.0 and 65.8–78.3%, respectively. ML-based TA of muscle MRI may be used to predict disease groups or the autoantibody status in patients with IIM and is useful in non-invasive assessments of disease mechanisms.
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Smets J, Shevroja E, Hügle T, Leslie WD, Hans D. Machine Learning Solutions for Osteoporosis-A Review. J Bone Miner Res 2021; 36:833-851. [PMID: 33751686 DOI: 10.1002/jbmr.4292] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 02/04/2021] [Accepted: 03/16/2021] [Indexed: 12/11/2022]
Abstract
Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high-dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis. A systemic search in PubMed and Web of Science resulted in 89 studies for inclusion in the review. These covered one or more of four main areas in osteoporosis management: bone properties assessment (n = 13), osteoporosis classification (n = 34), fracture detection (n = 32), and risk prediction (n = 14). Reporting and methodological quality was determined by means of a 12-point checklist. In general, the studies were of moderate quality with a wide range (mode score 6, range 2 to 11). Major limitations were identified in a significant number of studies. Incomplete reporting, especially over model selection, inadequate splitting of data, and the low proportion of studies with external validation were among the most frequent problems. However, the use of images for opportunistic osteoporosis diagnosis or fracture detection emerged as a promising approach and one of the main contributions that ML could bring to the osteoporosis field. Efforts to develop ML-based models for identifying novel fracture risk factors and improving fracture prediction are additional promising lines of research. Some studies also offered insights into the potential for model-based decision-making. Finally, to avoid some of the common pitfalls, the use of standardized checklists in developing and sharing the results of ML models should be encouraged. © 2021 American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Julien Smets
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Enisa Shevroja
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Thomas Hügle
- Department of Rheumatology, Lausanne University Hospital, Lausanne, Switzerland
| | | | - Didier Hans
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
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Ong T, Copeland R, Thiam CN, Cerda Mas G, Marshall L, Sahota O. Integration of a vertebral fracture identification service into a fracture liaison service: a quality improvement project. Osteoporos Int 2021; 32:921-926. [PMID: 33170309 DOI: 10.1007/s00198-020-05710-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 10/20/2020] [Indexed: 10/23/2022]
Abstract
Integration of a vertebral fracture identification service into a Fracture Liaison Service is possible. Almost one-fifth of computerised tomography scans performed identified an individual with a fracture. This increase in workload needs to be considered by any FLS that wants to utilise such a service. INTRODUCTION This service improvement project aimed to improve detection of incidental vertebral fractures on routine imaging. It embedded a vertebral fracture identification service (Optasia Medical, OM) on routine computerised tomography (CT) scans performed in this hospital as part of its Fracture Liaison Service (FLS). METHODS The service was integrated into the hospital's CT workstream. Scans of patients aged ≥ 50 years for 3 months were prospectively retrieved, alongside their clinical history and the CT report. Fractures were identified via OM's machine learning algorithm and cross-checked by the OM radiologist. Fractures identified were then added as an addendum to the original CT report and the hospital FLS informed. The FLS made recommendations based on an agreed algorithm. RESULTS In total, 4461 patients with CT scans were retrieved over the 3-month period of which 850 patients had vertebra fractures identified (19.1%). Only 49% had the fractures described on hospital radiology report. On average, 61 patients were identified each week with a median of two fractures. Thirty-six percent were identified by the FLS for further action and recommendations were made to either primary care or the community osteoporosis team within 3 months of fracture detection. Of the 64% not identified for further action, almost half was because the CT was part of cancer assessment or treatment. The remaining were due to a combination of only ≤ 2 mild fractures; already known to a bone health specialist; in the terminal stages of any chronic illness; significant dependency for activities of daily living; or a life expectancy of less than 12 months CONCLUSION: It was feasible to integrate a commercial vertebral fracture identification service into the daily working of a FLS. There was a significant increase in workload which needs to be considered by any future FLS planning to incorporate such a service into their clinical practice.
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Affiliation(s)
- T Ong
- Department for Healthcare of Older People, Queens Medical Centre, Nottingham University Hospital NHS Trust, Nottingham, UK.
- Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia.
| | - R Copeland
- Department for Healthcare of Older People, Queens Medical Centre, Nottingham University Hospital NHS Trust, Nottingham, UK
| | - C N Thiam
- Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - G Cerda Mas
- Consorci Sanitari de Terrassa, Terrassa, Spain
| | - L Marshall
- Department for Trauma and Orthopaedics, Queens Medical Centre, Nottingham University Hospital NHS Trust, Nottingham, UK
| | - O Sahota
- Department for Healthcare of Older People, Queens Medical Centre, Nottingham University Hospital NHS Trust, Nottingham, UK
- Division of Rehabilitation, Ageing and Wellbeing, University of Nottingham, Nottingham, UK
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre (BRC), Nottingham, UK
- National Institute for Health Research (NIHR) Collaboration for Applied Health Research and Care (CLAHRC) East Midlands, Nottingham, UK
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Small JE, Osler P, Paul AB, Kunst M. CT Cervical Spine Fracture Detection Using a Convolutional Neural Network. AJNR Am J Neuroradiol 2021; 42:1341-1347. [PMID: 34255730 DOI: 10.3174/ajnr.a7094] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 01/25/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Multidetector CT has emerged as the standard of care imaging technique to evaluate cervical spine trauma. Our aim was to evaluate the performance of a convolutional neural network in the detection of cervical spine fractures on CT. MATERIALS AND METHODS We evaluated C-spine, an FDA-approved convolutional neural network developed by Aidoc to detect cervical spine fractures on CT. A total of 665 examinations were included in our analysis. Ground truth was established by retrospective visualization of a fracture on CT by using all available CT, MR imaging, and convolutional neural network output information. The ĸ coefficients, sensitivity, specificity, and positive and negative predictive values were calculated with 95% CIs comparing diagnostic accuracy and agreement of the convolutional neural network and radiologist ratings, respectively, compared with ground truth. RESULTS Convolutional neural network accuracy in cervical spine fracture detection was 92% (95% CI, 90%-94%), with 76% (95% CI, 68%-83%) sensitivity and 97% (95% CI, 95%-98%) specificity. The radiologist accuracy was 95% (95% CI, 94%-97%), with 93% (95% CI, 88%-97%) sensitivity and 96% (95% CI, 94%-98%) specificity. Fractures missed by the convolutional neural network and by radiologists were similar by level and location and included fractured anterior osteophytes, transverse processes, and spinous processes, as well as lower cervical spine fractures that are often obscured by CT beam attenuation. CONCLUSIONS The convolutional neural network holds promise at both worklist prioritization and assisting radiologists in cervical spine fracture detection on CT. Understanding the strengths and weaknesses of the convolutional neural network is essential before its successful incorporation into clinical practice. Further refinements in sensitivity will improve convolutional neural network diagnostic utility.
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Affiliation(s)
- J E Small
- From the Departments of Neuroradiology (J.E.S., A.B.P., M.K.)
| | - P Osler
- Radiology (P.O), Lahey Hospital and Medical Center, Burlington, Massachusetts
| | - A B Paul
- From the Departments of Neuroradiology (J.E.S., A.B.P., M.K.)
| | - M Kunst
- From the Departments of Neuroradiology (J.E.S., A.B.P., M.K.)
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Liu X, Zheng M, Sun J, Cui X. A diagnostic model for differentiating tuberculous spondylitis from pyogenic spondylitis on computed tomography images. Eur Radiol 2021; 31:7626-7636. [PMID: 33768287 DOI: 10.1007/s00330-021-07812-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 01/01/2021] [Accepted: 02/18/2021] [Indexed: 12/19/2022]
Abstract
OBJECTIVES To develop and evaluate a logistics regression diagnostic model based on computer tomography (CT) features to differentiate tuberculous spondylitis (TS) from pyogenic spondylitis (PS). METHODS Demographic and clinical features were collected from the Electronic Medical Record System. Data of bony changes seen on CT images were compared between the PS (n = 61) and TS (n = 51) groups using the chi-squared test or t test. Based on features that were identified to be significant, a diagnostic model was developed from a derivation set (two thirds) and evaluated in a validation set (one third). The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. RESULTS The width of bone formation around the vertebra and sequestrum was greater in the TS group. There were significant differences between the two groups in the horizontal and longitudinal location of erosion and the morphology of axial bone destruction and sagittal residual vertebra. Kyphotic deformity and overlapping vertebrae were more common in the TS group. A diagnostic model that included eight predictors was developed and simplified to include the following six predictors: width of the bone formation surrounding the vertebra, longitudinal location, axial-specific erosive morphology, specific morphology of the residual vertebra, kyphotic deformity, and overlapping vertebrae. The simplified model showed good sensitivity, specificity, and total accuracy (85.59%, 87.80%, and 86.50%, respectively); the AUC was 0.95, indicating good clinical predictive ability. CONCLUSIONS A diagnostic model based on bone destruction and formation seen on CT images can facilitate clinical differentiation of TS from PS. KEY POINTS • We have developed and validated a simple diagnostic model based on bone destruction and formation observed on CT images that can differentiate tuberculous spondylitis from pyogenic spondylitis. • The model includes six predictors: width of the bone formation surrounding the vertebra, longitudinal location, axial-specific erosive morphology, specific morphology of the residual vertebra, kyphotic deformity, and overlapping vertebrae. • The simplified model has good sensitivity, specificity, and total accuracy with a high AUC, indicating excellent predictive ability.
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Affiliation(s)
- Xiaoyang Liu
- Department of Spine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 9677 in Jingshi Road, Jinan City, China
| | - Meimei Zheng
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Jianmin Sun
- Department of Spine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 9677 in Jingshi Road, Jinan City, China
| | - Xingang Cui
- Department of Spine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 9677 in Jingshi Road, Jinan City, China.
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