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Erjiang E, Carey JJ, Wang T, Ebrahimiarjestan M, Yang L, Dempsey M, Yu M, Chan WP, Whelan B, Silke C, O'Sullivan M, Rooney B, McPartland A, O'Malley G, Brennan A. Modelling future bone mineral density: Simplicity or complexity? Bone 2024:117178. [PMID: 38972532 DOI: 10.1016/j.bone.2024.117178] [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: 12/15/2023] [Revised: 03/14/2024] [Accepted: 06/24/2024] [Indexed: 07/09/2024]
Abstract
BACKGROUND Osteoporotic fractures are a major global public health issue, leading to patient suffering and death, and considerable healthcare costs. Bone mineral density (BMD) measurement is important to identify those with osteoporosis and assess their risk of fracture. Both the absolute BMD and the change in BMD over time contribute to fracture risk. Predicting future fracture in individual patients is challenging and impacts clinical decisions such as when to intervene or repeat BMD measurement. Although the importance of BMD change is recognised, an effective way to incorporate this marginal effect into clinical algorithms is lacking. METHODS We compared two methods using longitudinal DXA data generated from subjects with two or more hip DXA scans on the same machine between 2000 and 2018. A simpler statistical method (ZBM) was used to predict an individual's future BMD based on the mean BMD and the standard deviation of the reference group and their BMD measured in the latest scan. A more complex deep learning (DL)-based method was developed to cope with multidimensional longitudinal data, variables extracted from patients' historical DXA scan(s), as well as features drawn from the ZBM method. Sensitivity analyses of several subgroups was conducted to evaluate the performance of the derived models. RESULTS 2948 white adults aged 40-90 years met our study inclusion: 2652 (90 %) females and 296 (10 %) males. Our DL-based models performed significantly better than the ZBM models in women, particularly our Hybrid-DL model. In contrast, the ZBM-based models performed as well or better than DL-based models in men. CONCLUSIONS Deep learning-based and statistical models have potential to forecast future BMD using longitudinal clinical data. These methods have the potential to augment clinical decisions regarding when to repeat BMD testing in the assessment of osteoporosis.
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Affiliation(s)
- E Erjiang
- School of Management, Guangxi Minzu Univeristy, Nanning, China
| | - John J Carey
- School of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Ireland; Department of Rheumatology, Galway University Hospitals, Galway, Ireland
| | - Tingyan Wang
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Lan Yang
- Insight SFI Research Centre for Data Analytics, Data Science Institute, University of Galway, Ireland
| | - Mary Dempsey
- School of Engineering, College of Science and Engineering, University of Galway, Ireland
| | - Ming Yu
- Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Wing P Chan
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taiwan
| | - Bryan Whelan
- School of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Ireland; Department of Rheumatology, Our Lady's Hospital, Manorhamilton, Co. Leitrim, Ireland
| | - Carmel Silke
- School of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Ireland; Department of Rheumatology, Our Lady's Hospital, Manorhamilton, Co. Leitrim, Ireland
| | - Miriam O'Sullivan
- School of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Ireland; Department of Rheumatology, Our Lady's Hospital, Manorhamilton, Co. Leitrim, Ireland
| | - Bridie Rooney
- Department of Geriatric Medicine, Sligo University Hospital, Sligo, Ireland
| | - Aoife McPartland
- Department of Rheumatology, Our Lady's Hospital, Manorhamilton, Co. Leitrim, Ireland
| | - Gráinne O'Malley
- School of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Ireland; Department of Geriatric Medicine, Sligo University Hospital, Sligo, Ireland
| | - Attracta Brennan
- School of Computer Science, College of Science and Engineering, University of Galway, Ireland.
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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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3
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Wu Y, Chao J, Bao M, Zhang N. Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis. BMJ Open 2023; 13:e071430. [PMID: 38070927 PMCID: PMC10728980 DOI: 10.1136/bmjopen-2022-071430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 11/06/2023] [Indexed: 12/18/2023] Open
Abstract
OBJECTIVES Early identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis. METHODS Relevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models and fracture sites. RESULTS Fifty-three studies were included in our meta-analysis, involving 15 209 268 patients, 86 prediction models specifically developed for the osteoporosis population and 41 validation sets. The most commonly used predictors in these models encompassed age, BMI, past fracture history, bone mineral density T-score, history of falls, BMD, radiomics data, weight, height, gender and other chronic diseases. Overall, the pooled C-index of ML was 0.75 (95% CI: 0.72, 0.78) and 0.75 (95% CI: 0.71, 0.78) in the training set and validation set, respectively; the pooled sensitivity was 0.79 (95% CI: 0.72, 0.84) and 0.76 (95% CI: 0.80, 0.81) in the training set and validation set, respectively; and the pooled specificity was 0.81 (95% CI: 0.75, 0.86) and 0.83 (95% CI: 0.72, 0.90) in the training set and validation set, respectively. CONCLUSIONS ML has a favourable predictive performance for fracture risk in patients with osteoporosis. However, most current studies lack external validation. Thus, external validation is required to verify the reliability of ML models. PROSPERO REGISTRATION NUMBER CRD42022346896.
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Affiliation(s)
- Yanqian Wu
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Jianqian Chao
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Min Bao
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Na Zhang
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
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4
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Han D, Fan Z, Chen YS, Xue Z, Yang Z, Liu D, Zhou R, Yuan H. Retrospective study: risk assessment model for osteoporosis-a detailed exploration involving 4,552 Shanghai dwellers. PeerJ 2023; 11:e16017. [PMID: 37701834 PMCID: PMC10494836 DOI: 10.7717/peerj.16017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 08/10/2023] [Indexed: 09/14/2023] Open
Abstract
Background Osteoporosis, a prevalent orthopedic issue, significantly influences patients' quality of life and results in considerable financial burden. The objective of this study was to develop and validate a clinical prediction model for osteoporosis risk, utilizing computer algorithms and demographic data. Method In this research, a total of 4,552 residents from Shanghai were retrospectively included. LASSO regression analysis was executed on the sample's basic characteristics, and logistic regression was employed for analyzing clinical characteristics and building a predictive model. The model's diagnostic capacity for predicting osteoporosis risk was assessed using R software and computer algorithms. Results The predictive nomogram model for bone loss risk, derived from the LASSO analysis, comprised factors including BMI, TC, TG, HDL, Gender, Age, Education, Income, Sleep, Alcohol Consumption, and Diabetes. The nomogram prediction model demonstrated impressive discriminative capability, with a C-index of 0.908 (training set), 0.908 (validation set), and 0.910 (entire cohort). The area under the ROC curve (AUC) of the model was 0.909 (training set), 0.903 (validation set), and applicable to the entire cohort. The decision curve analysis further corroborated that the model could efficiently predict the risk of bone loss in patients. Conclusion The nomogram, based on essential demographic and health factors (Body Mass Index, Total Cholesterol, Triglycerides, High-Density Lipoprotein, Gender, Age, Education, Income, Sleep, Alcohol Consumption, and Diabetes), offered accurate predictions for the risk of bone loss within the studied population.
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Affiliation(s)
- Dan Han
- Department of Emergency Medicine and Intensive Care, Songjiang Hospital Affiliated to Shanghai Jiaotong University School of Medicine (Preparatory Stage), Shanghai, Shanghai, China
| | - Zhongcheng Fan
- Department of Orthopaedics, Hainan Province Clinical Medical Center, Haikou Orthopedic and Diabetes Hospital of Shanghai Sixth People’s Hospital, Haikou, China
| | - Yi-sheng Chen
- Department of Sports medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Zichao Xue
- Department of Orthopaedics, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Zhenwei Yang
- Department of Orthopaedics, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Danping Liu
- Department of Orthopaedics, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Rong Zhou
- Department Two of Medical Administration, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Hong Yuan
- Department Two of Medical Administration, Zhongshan Hospital, Fudan University, Shanghai, China
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Cha Y, Kim JT, Kim JW, Seo SH, Lee SY, Yoo JI. Effect of Artificial Intelligence or Machine Learning on Prediction of Hip Fracture Risk: Systematic Review. J Bone Metab 2023; 30:245-252. [PMID: 37718902 PMCID: PMC10509025 DOI: 10.11005/jbm.2023.30.3.245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/12/2023] [Accepted: 05/29/2023] [Indexed: 09/19/2023] Open
Abstract
BACKGROUND Dual energy X-ray absorptiometry (DXA) is a preferred modality for screening or diagnosis of osteoporosis and can predict the risk of hip fracture. However, the DXA test is difficult to implement easily in some developing countries, and fractures have been observed before patients underwent DXA. The purpose of this systematic review is to search for studies that predict the risk of hip fracture using artificial intelligence (AI) or machine learning, organize the results of each study, and analyze the usefulness of this technology. METHODS The PubMed, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched including "hip fractures" AND "artificial intelligence". RESULTS A total of 7 studies are included in this study. The total number of subjects included in the 7 studies was 330,099. There were 3 studies that included only women, and 4 studies included both men and women. One study conducted AI training after 1:1 matching between fractured and non-fractured patients. The area under the curve of AI prediction model for hip fracture risk was 0.39 to 0.96. The accuracy of AI prediction model for hip fracture risk was 70.26% to 90%. CONCLUSIONS We believe that predicting the risk of hip fracture by the AI model will help select patients with high fracture risk among osteoporosis patients. However, to apply the AI model to the prediction of hip fracture risk in clinical situations, it is necessary to identify the characteristics of the dataset and AI model and use it after performing appropriate validation.
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Affiliation(s)
- Yonghan Cha
- Department of Orthopedic Surgery, Daejeon Eulji Medical Center, Eulji University School of Medicine, Daejeon,
Korea
| | - Jung-Taek Kim
- Department of Orthopedic Surgery, Ajou Medical Center, Ajou University School of Medicine, Suwon,
Korea
| | - Jin-Woo Kim
- Department of Orthopedic Surgery, Nowon Eulji Medical Center, Eulji University, Seoul,
Korea
| | - Sung Hyo Seo
- Department of Biomedical Research Institute, Gyeongsang National University Hospital, Jinju,
Korea
| | - Sang-Yeob Lee
- Department of Biomedical Research Institute, Gyeongsang National University Hospital, Jinju,
Korea
| | - Jun-Il Yoo
- Department of Orthopaedic Surgery, Inha University Hospital, Inha University School of Medicine, Incheon,
Korea
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Rahim F, Zaki Zadeh A, Javanmardi P, Emmanuel Komolafe T, Khalafi M, Arjomandi A, Ghofrani HA, Shirbandi K. Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study. Biomed Eng Online 2023; 22:68. [PMID: 37430259 DOI: 10.1186/s12938-023-01132-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 06/26/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUND Osteoporosis is a significant health problem in the skeletal system, associated with bone tissue changes and its strength. Machine Learning (ML), on the other hand, has been accompanied by improvements in recent years and has been in the spotlight. This study is designed to investigate the Diagnostic Test Accuracy (DTA) of ML to detect osteoporosis through the hip dual-energy X-ray absorptiometry (DXA) images. METHODS The ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched until June 2023 for studies that tested the diagnostic precision of ML model-assisted for predicting an osteoporosis diagnosis. RESULTS The pooled sensitivity of univariate analysis of seven studies was 0.844 (95% CI 0.791 to 0.885, I2 = 94% for 7 studies). The pooled specificity of univariate analysis was 0.781 (95% CI 0.732 to 0.824, I2 = 98% for 7 studies). The pooled diagnostic odds ratio (DOR) was 18.91 (95% CI 14.22 to 25.14, I2 = 93% for 7 studies). The pooled mean positive likelihood ratio (LR+) and the negative likelihood ratio (LR-) were 3.7 and 0.22, respectively. Also, the summary receiver operating characteristics (sROC) of the bivariate model has an AUC of 0.878. CONCLUSION Osteoporosis can be diagnosed by ML with acceptable accuracy, and hip fracture prediction was improved via training in an Architecture Learning Network (ALN).
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Affiliation(s)
- Fakher Rahim
- Department of Anesthesia, Cihan University - Sulaimaniya, Sulaymaniyah, Kurdistan Region, Iraq
| | - Amin Zaki Zadeh
- Medical Doctor (MD), School of Medicine, Ahvaz Jondishapour University of Medical Sciences, Ahvaz, Iran
| | - Pouya Javanmardi
- Department of Radiologic Technology, Faculty of Paramedicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | | | - Mohammad Khalafi
- School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
| | - Ali Arjomandi
- Department of Radiologic Technology, Faculty of Paramedicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Haniye Alsadat Ghofrani
- Department of Radiologic Technology, Faculty of Paramedicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Kiarash Shirbandi
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran.
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A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems. J Funct Biomater 2023; 14:jfb14030156. [PMID: 36976080 PMCID: PMC10054603 DOI: 10.3390/jfb14030156] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/07/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023] Open
Abstract
Novel designs of porous and semi-porous hip stems attempt to alleviate complications such as aseptic loosening, stress shielding, and eventual implant failure. Various designs of hip stems are modeled to simulate biomechanical performance using finite element analysis; however, these models are computationally expensive. Therefore, the machine learning approach is incorporated with simulated data to predict the new biomechanical performance of new designs of hip stems. Six types of algorithms based on machine learning were employed to validate the simulated results of finite element analysis. Afterwards, new designs of semi-porous stems with outer dense layers of 2.5 and 3 mm and porosities of 10–80% were used to predict the stiffness of the stems, stresses in outer dense layers, stresses in porous sections, and factor of safety under physiological loads using machine learning algorithms. It was determined that decision tree regression is the top-performing machine learning algorithm as per the used simulation data in terms of the validation mean absolute percentage error which equals 19.62%. It was also found that ridge regression produces the most consistent test set trend as compared with the original simulated finite element analysis results despite relying on a relatively small data set. These predicted results employing trained algorithms provided the understanding that changing the design parameters of semi-porous stems affects the biomechanical performance without carrying out finite element analysis.
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Jepsen KJ, Bigelow EMR, Casden MA, Goulet RW, Kennedy K, Hertz S, Kadur C, Nolan BT, Richards‐McCullough K, Merillat S, Karvonen‐Gutierrez CA, Clines G, Bredbenner TL. Associations Among Hip Structure, Bone Mineral Density, and Strength Vary With External Bone Size in White Women. JBMR Plus 2023; 7:e10715. [PMID: 36936363 PMCID: PMC10020918 DOI: 10.1002/jbm4.10715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/01/2022] [Accepted: 12/12/2022] [Indexed: 12/29/2022] Open
Abstract
Bone mineral density (BMD) is heavily relied upon to reflect structural changes affecting hip strength and fracture risk. Strong correlations between BMD and strength are needed to provide confidence that structural changes are reflected in BMD and, in turn, strength. This study investigated how variation in bone structure gives rise to variation in BMD and strength and tested whether these associations differ with external bone size. Cadaveric proximal femurs (n = 30, White women, 36-89+ years) were imaged using nanocomputed tomography (nano-CT) and loaded in a sideways fall configuration to assess bone strength and brittleness. Bone voxels within the nano-CT images were projected onto a plane to create pseudo dual-energy X-ray absorptiometry (pseudo-DXA) images consistent with a clinical DXA scan. A validation study using 19 samples confirmed pseudo-DXA measures correlated significantly with those measured from a commercially available DXA system, including bone mineral content (BMC) (R 2 = 0.95), area (R 2 = 0.58), and BMD (R 2 = 0.92). BMD-strength associations were conducted using multivariate linear regression analyses with the samples divided into narrow and wide groups by pseudo-DXA area. Nearly 80% of the variation in strength was explained by age, body weight, and pseudo-DXA BMD for the narrow subgroup. Including additional structural or density distribution information in regression models only modestly improved the correlations. In contrast, age, body weight, and pseudo-DXA BMD explained only half of the variation in strength for the wide subgroup. Including bone density distribution or structural details did not improve the correlations, but including post-yield deflection (PYD), a measure of bone material brittleness, did increase the coefficient of determination to more than 70% for the wide subgroup. This outcome suggested material level effects play an important role in the strength of wide femoral necks. Thus, the associations among structure, BMD, and strength differed with external bone size, providing evidence that structure-function relationships may be improved by judiciously sorting study cohorts into subgroups. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
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Affiliation(s)
- Karl J Jepsen
- Department of Orthopaedic Surgery (Medical School) and Department of Epidemiology (Public Health)University of MichiganAnn ArborMIUSA
| | - Erin MR Bigelow
- Department of Orthopaedic Surgery (Medical School) and Department of Epidemiology (Public Health)University of MichiganAnn ArborMIUSA
| | - Michael A Casden
- Department of Orthopaedic Surgery (Medical School) and Department of Epidemiology (Public Health)University of MichiganAnn ArborMIUSA
| | - Robert W Goulet
- Department of Orthopaedic Surgery (Medical School) and Department of Epidemiology (Public Health)University of MichiganAnn ArborMIUSA
| | - Kathryn Kennedy
- Department of Biomedical EngineeringMarquette UniversityMilwaukeeWIUSA
| | - Samantha Hertz
- Department of Orthopaedic Surgery (Medical School) and Department of Epidemiology (Public Health)University of MichiganAnn ArborMIUSA
| | - Chandan Kadur
- Department of Orthopaedic Surgery (Medical School) and Department of Epidemiology (Public Health)University of MichiganAnn ArborMIUSA
| | - Bonnie T Nolan
- Department of Orthopaedic Surgery (Medical School) and Department of Epidemiology (Public Health)University of MichiganAnn ArborMIUSA
| | - Kerry Richards‐McCullough
- Department of Orthopaedic Surgery (Medical School) and Department of Epidemiology (Public Health)University of MichiganAnn ArborMIUSA
| | - Steffenie Merillat
- Department of Orthopaedic Surgery (Medical School) and Department of Epidemiology (Public Health)University of MichiganAnn ArborMIUSA
| | - Carrie A Karvonen‐Gutierrez
- Department of Orthopaedic Surgery (Medical School) and Department of Epidemiology (Public Health)University of MichiganAnn ArborMIUSA
| | - Gregory Clines
- Department of Orthopaedic Surgery (Medical School) and Department of Epidemiology (Public Health)University of MichiganAnn ArborMIUSA
- EndocrinologyVA Medical CenterAnn ArborMIUSA
| | - Todd L Bredbenner
- Department of Mechanical and Aerospace EngineeringUniversity of Colorado Colorado SpringsColorado SpringsCOUSA
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Inui A, Nishimoto H, Mifune Y, Yoshikawa T, Shinohara I, Furukawa T, Kato T, Tanaka S, Kusunose M, Kuroda R. Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach. Bioengineering (Basel) 2023; 10:bioengineering10030277. [PMID: 36978668 PMCID: PMC10045086 DOI: 10.3390/bioengineering10030277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/12/2023] [Accepted: 02/16/2023] [Indexed: 02/24/2023] Open
Abstract
The diagnosis of osteoporosis is made by measuring bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA). Machine learning, one of the artificial intelligence methods, was used to predict low BMD without using DXA in elderly women. Medical records from 2541 females who visited the osteoporosis clinic were used in this study. As hyperparameters for machine learning, patient age, body mass index (BMI), and blood test data were used. As machine learning models, logistic regression, decision tree, random forest, gradient boosting trees, and lightGBM were used. Each model was trained to classify and predict low-BMD patients. The model performance was compared using a confusion matrix. The accuracy of each trained model was 0.772 in logistic regression, 0.739 in the decision tree, 0.775 in the random forest, 0.800 in gradient boosting, and 0.834 in lightGBM. The area under the curve (AUC) was 0.595 in the decision tree, 0.673 in logistic regression, 0.699 in the random forest, 0.840 in gradient boosting, and 0.961, which was the highest, in the lightGBM model. Important features were BMI, age, and the number of platelets. Shapley additive explanation scores in the lightGBM model showed that BMI, age, and ALT were ranked as important features. Among several machine learning models, the lightGBM model showed the best performance in the present research.
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Affiliation(s)
- Atsuyuki Inui
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kusunoki-cho, 7-5-1, Chuou-ku, Kobe City 650-0017, Japan
- Correspondence: ; Tel.: +81-78-382-5985
| | - Hanako Nishimoto
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kusunoki-cho, 7-5-1, Chuou-ku, Kobe City 650-0017, Japan
- Orthopaedic Surgery Kobe Rosai Hospital, Kagoike-dori 4-1-23, Chuou-ku, Kobe City 651-0053, Japan
| | - Yutaka Mifune
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kusunoki-cho, 7-5-1, Chuou-ku, Kobe City 650-0017, Japan
| | - Tomoya Yoshikawa
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kusunoki-cho, 7-5-1, Chuou-ku, Kobe City 650-0017, Japan
| | - Issei Shinohara
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kusunoki-cho, 7-5-1, Chuou-ku, Kobe City 650-0017, Japan
| | - Takahiro Furukawa
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kusunoki-cho, 7-5-1, Chuou-ku, Kobe City 650-0017, Japan
| | - Tatsuo Kato
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kusunoki-cho, 7-5-1, Chuou-ku, Kobe City 650-0017, Japan
| | - Shuya Tanaka
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kusunoki-cho, 7-5-1, Chuou-ku, Kobe City 650-0017, Japan
| | - Masaya Kusunose
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kusunoki-cho, 7-5-1, Chuou-ku, Kobe City 650-0017, Japan
| | - Ryosuke Kuroda
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kusunoki-cho, 7-5-1, Chuou-ku, Kobe City 650-0017, Japan
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C1QC, VSIG4, and CFD as Potential Peripheral Blood Biomarkers in Atrial Fibrillation-Related Cardioembolic Stroke. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2023; 2023:5199810. [PMID: 36644582 PMCID: PMC9837713 DOI: 10.1155/2023/5199810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 11/28/2022] [Accepted: 12/09/2022] [Indexed: 01/07/2023]
Abstract
Atrial fibrillation (AF) is a major risk factor for ischemic stroke. We aimed to identify novel potential biomarkers with diagnostic value in patients with atrial fibrillation-related cardioembolic stroke (AF-CE).Publicly available gene expression profiles related to AF, cardioembolic stroke (CE), and large artery atherosclerosis (LAA) were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified and then functionally annotated. The support vector machine recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to identify potential diagnostic AF-CE biomarkers. Furthermore, the results were validated by using external data sets, and discriminability was measured by the area under the ROC curve (AUC). In order to verify the predictive results, the blood samples of 13 healthy controls, 20 patients with CE, and 20 patients with LAA stroke were acquired for RT-qPCR, and the correlation between biomarkers and clinical features was further explored. Lastly, a nomogram and the companion website were developed to predict the CE-risk rate. Three feature genes (C1QC, VSIG4, and CFD) were selected and validated in the training and the external datasets. The qRT-PCR evaluation showed that the levels of blood biomarkers (C1QC, VSIG4, and CFD) in patients with AF-CE can be used to differentiate patients with AF-CE from normal controls (P < 0.05) and can effectively discriminate AF-CE from LAA stroke (P < 0.05). Immune cell infiltration analysis revealed that three feature genes were correlated with immune system such as neutrophils. Clinical impact curve, calibration curves, ROC, and DCAs of the nomogram indicate that the nomogram had good performance. Our findings showed that C1QC, VSIG4, and CFD can potentially serve as diagnostic blood biomarkers of AF-CE; novel nomogram and the companion website can help clinicians to identify high-risk individuals, thus helping to guide treatment decisions for stroke patients.
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Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection. Int J Dent 2023; 2023:6662911. [PMID: 36896411 PMCID: PMC9991474 DOI: 10.1155/2023/6662911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 02/12/2023] [Accepted: 02/18/2023] [Indexed: 03/03/2023] Open
Abstract
Osteoporosis leads to the loss of cortical thickness, a decrease in bone mineral density (BMD), deterioration in the size of trabeculae, and an increased risk of fractures. Changes in trabecular bone due to osteoporosis can be observed on periapical radiographs, which are widely used in dental practice. This study proposes an automatic trabecular bone segmentation method for detecting osteoporosis using a color histogram and machine learning (ML), based on 120 regions of interest (ROI) on periapical radiographs, and divided into 60 training and 42 testing datasets. The diagnosis of osteoporosis is based on BMD as evaluated by dual X-ray absorptiometry. The proposed method comprises five stages: the obtaining of ROI images, conversion to grayscale, color histogram segmentation, extraction of pixel distribution, and performance evaluation of the ML classifier. For trabecular bone segmentation, we compare K-means and Fuzzy C-means. The distribution of pixels obtained from the K-means and Fuzzy C-means segmentation was used to detect osteoporosis using three ML methods: decision tree, naive Bayes, and multilayer perceptron. The testing dataset was used to obtain the results in this study. Based on the performance evaluation of the K-means and Fuzzy C-means segmentation methods combined with 3 ML, the osteoporosis detection method with the best diagnostic performance was K-means segmentation combined with a multilayer perceptron classifier, with accuracy, specificity, and sensitivity of 90.48%, 90.90%, and 90.00%, respectively. The high accuracy of this study indicates that the proposed method provides a significant contribution to the detection of osteoporosis in the field of medical and dental image analysis.
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Dong ST, Zhu J, Yang H, Huang G, Zhao C, Yuan B. Development and Internal Validation of Supervised Machine Learning Algorithm for Predicting the Risk of Recollapse Following Minimally Invasive Kyphoplasty in Osteoporotic Vertebral Compression Fractures. Front Public Health 2022; 10:874672. [PMID: 35586015 PMCID: PMC9108356 DOI: 10.3389/fpubh.2022.874672] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 03/23/2022] [Indexed: 11/16/2022] Open
Abstract
Background The published literatures indicate that patients with osteoporotic vertebral compression fractures (OVCFs) benefit significantly from percutaneous kyphoplasty (PKP), but this surgical technique is associated with frequent postoperative recollapse, a complication that severely limits long-term postoperative functional recovery. Methods This study retrospectively analyzed single-segment OVCF patients who underwent bilateral PKP at our academic center from January 1, 2017 to September 30, 2019. Comparing the plain films of patients within 3 days after surgery and at the final follow-up, we classified patients with more than 10% loss of sagittal anterior height as the recollapse group. Univariate and multivariate logistic regression analyses were performed to determine the risk factors affecting recollapse after PKP. Based on the logistic regression results, we constructed one support vector machine (SVM) classifier to predict recollapse using machine learning (ML) algorithm. The predictive performance of this prediction model was validated by the receiver operating characteristic (ROC) curve, 10-fold cross validation, and confusion matrix. Results Among the 346 consecutive patients (346 vertebral bodies in total), postoperative recollapse was observed in 40 patients (11.56%). The results of the multivariate logistical regression analysis showed that high body mass index (BMI) (Odds ratio [OR]: 2.08, 95% confidence interval [CI]: 1.58–2.72, p < 0.001), low bone mineral density (BMD) T-scores (OR: 4.27, 95% CI: 1.55–11.75, p = 0.005), presence of intravertebral vacuum cleft (IVC) (OR: 3.10, 95% CI: 1.21–7.99, p = 0.019), separated cement masses (OR: 3.10, 95% CI: 1.21–7.99, p = 0.019), cranial endplate or anterior cortical wall violation (OR: 0.17, 95% CI: 0.04–0.79, p = 0.024), cement-contacted upper endplate alone (OR: 4.39, 95% CI: 1.20–16.08, p = 0.025), and thoracolumbar fracture (OR: 6.17, 95% CI: 1.04–36.71, p = 0.045) were identified as independent risk factors for recollapse after a kyphoplasty surgery. Furthermore, the evaluation indices demonstrated a superior predictive performance of the constructed SVM model, including mean area under receiver operating characteristic curve (AUC) of 0.81, maximum AUC of 0.85, accuracy of 0.81, precision of 0.89, and sensitivity of 0.98. Conclusions For patients with OVCFs, the risk factors leading to postoperative recollapse were multidimensional. The predictive model we constructed provided insights into treatment strategies targeting secondary recollapse prevention.
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Affiliation(s)
- Sheng-tao Dong
- Department of Spine Surgery, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jieyang Zhu
- Department of Spine Surgery, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Hua Yang
- Department of Otolaryngology, Head and Neck Surgery, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Guangyi Huang
- Department of Spine Surgery, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chenning Zhao
- Department of Orthopedics, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Bo Yuan
- Department of Reparative and Reconstructive Surgery, Second Affiliated Hospital of Dalian Medical University, Dalian, China
- *Correspondence: Bo Yuan
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Dong S, Li Z, Tang ZR, Zheng Y, Yang H, Zeng Q. Predictors of adverse events after percutaneous pedicle screws fixation in patients with single-segment thoracolumbar burst fractures. BMC Musculoskelet Disord 2022; 23:168. [PMID: 35193550 PMCID: PMC8864915 DOI: 10.1186/s12891-022-05122-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 02/16/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Percutaneous pedicle screw fixation (PPSF) is the primary approach for single-segment thoracolumbar burst fractures (TLBF). The healing angle at the thoracolumbar junction is one of the most significant criteria for evaluating the efficacy of PPSF. Therefore, the purpose of this study was to analyze the predictors associated with the poor postoperative alignment of the thoracolumbar region from routine variables using a support vector machine (SVM) model. METHODS We retrospectively analyzed patients with TLBF operated at our academic institute between March 1, 2014 and December 31, 2019. Stepwise logistic regression analysis was performed to assess potential statistical differences between all clinical and radiological variables and the adverse events. Based on multivariate logistic results, a series of independent risk factors were fed into the SVM model. Meanwhile, the feature importance of radiologic outcome for each parameter was explored. The predictive performance of the SVM classifier was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy (ACC) and confusion matrices with 10-fold cross-validation, respectively. RESULTS In the recruited 150 TLBFs, unfavorable radiological outcomes were observed in 53 patients (35.33%). The relationship between osteoporosis (p = 0.036), preoperative Cobb angle (p = 0.001), immediate postoperative Cobb angle (p = 0.029), surgically corrected Cobb angle (p = 0.001), intervertebral disc injury (Score 2 p = 0.001, Score 3 p = 0.001), interpedicular distance (IPD) (p = 0.001), vertebral body compression rate (VBCR) (p = 0.010) and adverse events was confirmed by univariate regression. Thereafter, independent risk factors including preoperative Cobb angle, the disc status and IPD and independent protective factors surgical correction angle were identified by multivariable logistic regression. The established SVM classifier demonstrated favorable predictive performance with the best AUC = 0.93, average AUC = 0.88, and average ACC = 0.87. The variables associated with radiological outcomes, in order of correlation strength, were intervertebral disc injury (42%), surgically corrected Cobb angle (25%), preoperative Cobb angle (18%), and IPD (15%). The confusion matrix reveals the classification results of the discriminant analysis. CONCLUSIONS Critical radiographic indicators and surgical purposes were confirmed to be associated with an unfavorable radiographic outcome of TLBF. This SVM model demonstrated good predictive ability for endpoints in terms of adverse events in patients after PPSF surgery.
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Affiliation(s)
- Shengtao Dong
- Department of Spine Surgery, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China
| | - Zongyuan Li
- Department of Orthopedics, Mianyang Central Hospital, Mianyang, 621000, China
| | - Zhi-Ri Tang
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Yuanyuan Zheng
- Department of Oncology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China
| | - Hua Yang
- Department of Otolaryngology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China
| | - Qiuming Zeng
- Department of Orthopedics, University-Town Hospital of Chongqing Medical University, Chongqing, 401331, China.
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AIM in Endocrinology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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15
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Abstract
PURPOSE OF REVIEW We re-evaluated clinical applications of image-to-FE models to understand if clinical advantages are already evident, which proposals are promising, and which questions are still open. RECENT FINDINGS CT-to-FE is useful in longitudinal treatment evaluation and groups discrimination. In metastatic lesions, CT-to-FE strength alone accurately predicts impending femoral fractures. In osteoporosis, strength from CT-to-FE or DXA-to-FE predicts incident fractures similarly to DXA-aBMD. Coupling loads and strength (possibly in dynamic models) may improve prediction. One promising MRI-to-FE workflow may now be tested on clinical data. Evidence of artificial intelligence usefulness is appearing. CT-to-FE is already clinical in opportunistic CT screening for osteoporosis, and risk of metastasis-related impending fractures. Short-term keys to improve image-to-FE in osteoporosis may be coupling FE with fall risk estimates, pool FE results with other parameters through robust artificial intelligence approaches, and increase reproducibility and cross-validation of models. Modeling bone modifications over time and bone fracture mechanics are still open issues.
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Affiliation(s)
- Enrico Schileo
- Bioengineering and Computing Laboratory, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
| | - Fulvia Taddei
- Bioengineering and Computing Laboratory, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
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Park HW, Jung H, Back KY, Choi HJ, Ryu KS, Cha HS, Lee EK, Hong AR, Hwangbo Y. Application of Machine Learning to Identify Clinically Meaningful Risk Group for Osteoporosis in Individuals Under the Recommended Age for Dual-Energy X-Ray Absorptiometry. Calcif Tissue Int 2021; 109:645-655. [PMID: 34195852 DOI: 10.1007/s00223-021-00880-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 06/16/2021] [Indexed: 11/29/2022]
Abstract
Dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis; it is generally recommended in men ≥ 70 and women ≥ 65 years old. Therefore, assessment of clinical risk factors for osteoporosis is very important in individuals under the recommended age for DXA. Here, we examine the diagnostic performance of machine learning-based prediction models for osteoporosis in individuals under the recommended age for DXA examination. Data of 2210 men aged 50-69 and 1099 women aged 50-64 obtained from the Korea National Health and Nutrition Examination Survey IV-V were analyzed. Extreme gradient boosting (XGBoost) was used to find relevant clinical features and applied to three machine learning models: XGBoost, logistic regression, and a multilayer perceptron. For the prediction of osteoporosis, the XGBoost model using the top 20 features extracted from XGBoost showed the most reliable performance with area under the receiver operating characteristic curve (AUROC) of 0.73 and 0.79 in men and women, respectively. We compared the diagnostic accuracy of the Shapley additive explanation values based on a risk-score model obtained from XGBoost and conventional osteoporosis risk assessment tools for prediction of osteoporosis using optimal cut-off values for each model. We observed that a cut-off risk score of ≥ 28 in men and ≥ 47 in women was optimal to classify a positive screening for osteoporosis (an AUROC of 0.86 in men and 0.91 in women). The XGBoost-based osteoporosis-prediction model outperformed conventional risk assessment tools. Therefore, machine learning-based prediction models are a more suitable option than conventional risk assessment methods for screening osteoporosis in individuals under the recommended age for DXA examination.
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Affiliation(s)
- Hyun Woo Park
- Healthcare AI Team, National Cancer Center, 323, Ilsan-ro, Ilsandong-gu, Goyang, Gyeonggi, 10408, South Korea
| | - Hyojung Jung
- Healthcare AI Team, National Cancer Center, 323, Ilsan-ro, Ilsandong-gu, Goyang, Gyeonggi, 10408, South Korea
| | - Kyoung Yeon Back
- Healthcare AI Team, National Cancer Center, 323, Ilsan-ro, Ilsandong-gu, Goyang, Gyeonggi, 10408, South Korea
| | - Hyeon Ju Choi
- Healthcare AI Team, National Cancer Center, 323, Ilsan-ro, Ilsandong-gu, Goyang, Gyeonggi, 10408, South Korea
| | - Kwang Sun Ryu
- Cancer Big Data Center, National Cancer Center, National Cancer Control Institute, Goyang, South Korea
| | - Hyo Soung Cha
- Cancer Big Data Center, National Cancer Center, National Cancer Control Institute, Goyang, South Korea
| | - Eun Kyung Lee
- Center for Thyroid Cancer, National Cancer Center, Goyang, South Korea
| | - A Ram Hong
- Department of Internal Medicine, Chonnam National University Medical School, 160, Baekseo-ro, Dong-gu, Gwangju, 61469, South Korea.
| | - Yul Hwangbo
- Healthcare AI Team, National Cancer Center, 323, Ilsan-ro, Ilsandong-gu, Goyang, Gyeonggi, 10408, South Korea.
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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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Eremina GM, Smolin AY. Risk assessment of resurfacing implant loosening and femur fracture under low-energy impacts taking into account degenerative changes in bone tissues. Computer simulation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105929. [PMID: 33450504 DOI: 10.1016/j.cmpb.2021.105929] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 12/31/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Degenerative diseases of the musculoskeletal system significantly reduce the quality of human life. Hip resurfacing is used to treat degenerative diseases in the later stages. After surgery, there is a risk of endoprosthesis loosening and low-energy fracture during daily physical activity. Computer modeling is a promising way to predict the optimal low-energy loads that do not lead to bone destruction. This paper aims to study numerically the mechanical behavior of the proximal femur, amenable to degenerative changes and subjected to hip resurfacing under low-energy impact equivalent to physiological loads. METHODS A numerical model of the mechanical behavior of the femur after hip resurfacing arthroplasty under low-energy impacts equivalent to physiological loads is presented. The model is based on the movable cellular automaton method (discrete elements), where the mechanical behavior of bone tissue is described using the Biot poroelasticity accounting for the presence and transfer of interstitial biological fluid. RESULTS For the first time, it is shown that a poroelastic model allows predicting the service life of endoprostheses, taking into account the individual characteristics of the bone tissues amenable to various degenerative diseases. The obtained results indicate that the changes in the bone properties have a significant influence on the critical forces corresponding to the first appearance of microcracks and the fracture formation. At the same time, their effect on the type of fracture is negligible. A much more impact on the type of fracture has the kinematic and dynamic conditions of the exposure. CONCLUSIONS The obtained results show the promise of using the proposed model for predicting the operational resource of resurfacing endoprostheses, taking into account the physiological features of the structure of the patient's bone tissues.
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Affiliation(s)
- Galina M Eremina
- Institute of Strength Physics and Materials Science of SB RAS, 2/4, pr. Akademicheskii, Tomsk, 634055, Russia.
| | - Alexey Yu Smolin
- Institute of Strength Physics and Materials Science of SB RAS, 2/4, pr. Akademicheskii, Tomsk, 634055, Russia.
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19
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Hong N, Park Y, You SC, Rhee Y. AIM in Endocrinology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_328-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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20
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Risk Assessment of Hip Fracture Based on Machine Learning. Appl Bionics Biomech 2020; 2020:8880786. [PMID: 33425008 PMCID: PMC7772022 DOI: 10.1155/2020/8880786] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 11/17/2020] [Accepted: 12/08/2020] [Indexed: 01/23/2023] Open
Abstract
Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%. In order to improve this accuracy, this paper proposes the use of Machine Learning (ML) models trained with data from a biomechanical model that simulates a sideways-fall. Machine Learning (ML) models are models able to learn and to make predictions from data. During a training process, ML models learn a function that maps inputs and outputs without previous knowledge of the problem. The main advantage of ML models is that once the mapping function is constructed, they can make predictions for complex biomechanical behaviours in real time. However, despite the increasing popularity of Machine Learning (ML) models and their wide application to many fields of medicine, their use as hip fracture predictors is still limited. This paper proposes the use of ML models to assess and predict hip fracture risk. Clinical, geometric, and biomechanical variables from the finite element simulation of a side fall are used as independent variables to train the models. Among the different tested models, Random Forest stands out, showing its capability to outperform BMD-DXA, achieving an accuracy over 87%, with specificity over 92% and sensitivity over 83%.
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Iwamoto Y, Imura T, Tanaka R, Imada N, Inagawa T, Araki H, Araki O. Development and Validation of Machine Learning-Based Prediction for Dependence in the Activities of Daily Living after Stroke Inpatient Rehabilitation: A Decision-Tree Analysis. J Stroke Cerebrovasc Dis 2020; 29:105332. [DOI: 10.1016/j.jstrokecerebrovasdis.2020.105332] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/03/2020] [Accepted: 09/12/2020] [Indexed: 01/19/2023] Open
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Klontzas ME, Papadakis GZ, Marias K, Karantanas AH. Musculoskeletal trauma imaging in the era of novel molecular methods and artificial intelligence. Injury 2020; 51:2748-2756. [PMID: 32972725 DOI: 10.1016/j.injury.2020.09.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 08/14/2020] [Accepted: 09/15/2020] [Indexed: 02/08/2023]
Abstract
Over the past decade rapid advancements in molecular imaging (MI) and artificial intelligence (AI) have revolutionized traditional musculoskeletal radiology. Molecular imaging refers to the ability of various methods to in vivo characterize and quantify biological processes, at a molecular level. The extracted information provides the tools to understand the pathophysiology of diseases and thus to early detect, to accurately evaluate the extend and to apply and evaluate targeted treatments. At present, molecular imaging mainly involves CT, MRI, radionuclide, US, and optical imaging and has been reported in many clinical and preclinical studies. Although originally MI techniques targeted at central nervous system disorders, later on their value on musculoskeletal disorders was also studied in depth. Meaningful exploitation of the large volume of imaging data generated by molecular and conventional imaging techniques, requires state-of-the-art computational methods that enable rapid handling of large volumes of information. AI allows end-to-end training of computer algorithms to perform tasks encountered in everyday clinical practice including diagnosis, disease severity classification and image optimization. Notably, the development of deep learning algorithms has offered novel methods that enable intelligent processing of large imaging datasets in an attempt to automate decision-making in a wide variety of settings related to musculoskeletal trauma. Current applications of AI include the diagnosis of bone and soft tissue injuries, monitoring of the healing process and prediction of injuries in the professional sports setting. This review presents the current applications of novel MI techniques and methods and the emerging role of AI regarding the diagnosis and evaluation of musculoskeletal trauma.
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Affiliation(s)
- Michail E Klontzas
- Department of Medical Imaging, Heraklion University Hospital, Crete, 70110, Greece; Advanced Hybrid Imaging Systems, Institute of Computer Science, Foundation for Research and Technology (FORTH), N. Plastira 100, Vassilika Vouton 70013, Heraklion, Crete, Greece.
| | - Georgios Z Papadakis
- Advanced Hybrid Imaging Systems, Institute of Computer Science, Foundation for Research and Technology (FORTH), N. Plastira 100, Vassilika Vouton 70013, Heraklion, Crete, Greece; Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013, Heraklion, Crete, Greece; Department of Radiology, School of Medicine, University of Crete, 70110 Greece.
| | - Kostas Marias
- Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013, Heraklion, Crete, Greece; Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410, Heraklion, Crete, Greece.
| | - Apostolos H Karantanas
- Department of Medical Imaging, Heraklion University Hospital, Crete, 70110, Greece; Advanced Hybrid Imaging Systems, Institute of Computer Science, Foundation for Research and Technology (FORTH), N. Plastira 100, Vassilika Vouton 70013, Heraklion, Crete, Greece; Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013, Heraklion, Crete, Greece; Department of Radiology, School of Medicine, University of Crete, 70110 Greece.
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