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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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End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:3018320. [PMID: 36970245 PMCID: PMC10036193 DOI: 10.1155/2023/3018320] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 02/23/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023]
Abstract
Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energy X-ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time-consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self-built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present.
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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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Xie Q, Chen Y, Hu Y, Zeng F, Wang P, Xu L, Wu J, Li J, Zhu J, Xiang M, Zeng F. Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography. BMC Med Imaging 2022; 22:140. [PMID: 35941568 PMCID: PMC9358842 DOI: 10.1186/s12880-022-00868-5] [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: 03/31/2022] [Accepted: 07/26/2022] [Indexed: 12/01/2022] Open
Abstract
Background To develop and validate a quantitative computed tomography (QCT) based radiomics model for discriminating osteoporosis and osteopenia.
Methods A total of 635 patients underwent QCT were retrospectively included from November 2016 to November 2019. The patients with osteopenia or osteoporosis (N = 590) were divided into a training cohort (N = 414) and a test cohort (N = 176). Radiomics features were extracted from the QCT images of the third lumbar vertebra. Minimum redundancy and maximum relevance and least absolute shrinkage and selection operator were used for data dimensional reduction, features selection and radiomics model building. Multivariable logistic regression was applied to construct the combined clinical-radiomic model that incorporated radiomics signatures and clinical characteristics. The performance of the combined clinical-radiomic model was evaluated by the area under the curve of receiver operator characteristic curve (ROC–AUC), accuracy, specificity, sensitivity, positive predictive value, and negative predictive value. Results The patients with osteopenia or osteoporosis were randomly divided into training and test cohort with a ratio of 7:3. Six more predictive radiomics signatures, age, alkaline phosphatase and homocysteine were selected to construct the combined clinical-radiomic model for diagnosis of osteoporosis and osteopenia. The AUC of the combined clinical-radiomic model was 0.96 (95% confidence interval (CI), 0.95 to 0.98) in the training cohort and 0.96 (95% CI 0.92 to 1.00) in the test cohort, which were superior to the clinical model alone (training-AUC = 0.81, test-AUC = 0.79). The calibration curve demonstrated that the radiomics nomogram had good agreement between prediction and observation and decision curve analysis confirmed clinically useful. Conclusions The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors, can serve as a reliable and powerful tool for discriminating osteoporosis and osteopenia. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00868-5.
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Affiliation(s)
- Qianrong Xie
- Department of Clinical Research Center, Dazhou Central Hospital, No.56 Nanyuemiao Street, Tongchuan District, Dazhou, 635000, Sichuan, China.,Department of Laboratory Medicine, The Third People's Hospital of Chengdu, Chengdu, 610000, China
| | - Yue Chen
- Department of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, No. 37 Shi-er-qiao Road, Jinniu District, Chengdu, 610000, Sichuan, China
| | - Yimei Hu
- Department of Orthopedics, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610000, China
| | - Fanwei Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, No.56 Nanyuemiao Street, Tongchuan District, Dazhou, 635000, Sichuan, China
| | - Pingxi Wang
- Department of Bone Disease, Dazhou Central Hospital, Dazhou, 635000, China
| | - Lin Xu
- Department of Medical Imaging, Dazhou Central Hospital, Dazhou, 635000, China
| | - Jianhong Wu
- Department of Bone Disease, Dazhou Central Hospital, Dazhou, 635000, China
| | - Jie Li
- Department of Clinical Research Center, Dazhou Central Hospital, No.56 Nanyuemiao Street, Tongchuan District, Dazhou, 635000, Sichuan, China
| | - Jing Zhu
- Department of Rheumatology and Immunology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, No.32 First Ring Road West, Jinniu District, Chengdu, 610000, Sichuan, China.
| | - Ming Xiang
- Department of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, No. 37 Shi-er-qiao Road, Jinniu District, Chengdu, 610000, Sichuan, China. .,Department of Orthopedics, Sichuan Provincial Orthopedic Hospital, Chengdu, 610000, China.
| | - Fanxin Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, No.56 Nanyuemiao Street, Tongchuan District, Dazhou, 635000, Sichuan, China. .,Department of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, No. 37 Shi-er-qiao Road, Jinniu District, Chengdu, 610000, Sichuan, China.
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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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Ensemble Classifiers for Predicting HIV-1 Resistance from Three Rule-Based Genotypic Resistance Interpretation Systems. J Med Syst 2017; 41:155. [PMID: 28856560 DOI: 10.1007/s10916-017-0802-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 08/22/2017] [Indexed: 10/19/2022]
Abstract
Resistance to antiretrovirals (ARVs) is a major problem faced by HIV-infected individuals. Different rule-based algorithms were developed to infer HIV-1 susceptibility to antiretrovirals from genotypic data. However, there is discordance between them, resulting in difficulties for clinical decisions about which treatment to use. Here, we developed ensemble classifiers integrating three interpretation algorithms: Agence Nationale de Recherche sur le SIDA (ANRS), Rega, and the genotypic resistance interpretation system from Stanford HIV Drug Resistance Database (HIVdb). Three approaches were applied to develop a classifier with a single resistance profile: stacked generalization, a simple plurality vote scheme and the selection of the interpretation system with the best performance. The strategies were compared with the Friedman's test and the performance of the classifiers was evaluated using the F-measure, sensitivity and specificity values. We found that the three strategies had similar performances for the selected antiretrovirals. For some cases, the stacking technique with naïve Bayes as the learning algorithm showed a statistically superior F-measure. This study demonstrates that ensemble classifiers can be an alternative tool for clinical decision-making since they provide a single resistance profile from the most commonly used resistance interpretation systems.
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Classification of nucleotide sequences for quality assessment using logistic regression and decision tree approaches. Neural Comput Appl 2017. [DOI: 10.1007/s00521-017-2960-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Premaladha J, Ravichandran KS. Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms. J Med Syst 2016; 40:96. [PMID: 26872778 DOI: 10.1007/s10916-016-0460-2] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 02/01/2016] [Indexed: 11/29/2022]
Abstract
Dermoscopy is a technique used to capture the images of skin, and these images are useful to analyze the different types of skin diseases. Malignant melanoma is a kind of skin cancer whose severity even leads to death. Earlier detection of melanoma prevents death and the clinicians can treat the patients to increase the chances of survival. Only few machine learning algorithms are developed to detect the melanoma using its features. This paper proposes a Computer Aided Diagnosis (CAD) system which equips efficient algorithms to classify and predict the melanoma. Enhancement of the images are done using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) and median filter. A new segmentation algorithm called Normalized Otsu's Segmentation (NOS) is implemented to segment the affected skin lesion from the normal skin, which overcomes the problem of variable illumination. Fifteen features are derived and extracted from the segmented images are fed into the proposed classification techniques like Deep Learning based Neural Networks and Hybrid Adaboost-Support Vector Machine (SVM) algorithms. The proposed system is tested and validated with nearly 992 images (malignant & benign lesions) and it provides a high classification accuracy of 93 %. The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies.
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Affiliation(s)
- J Premaladha
- School of Computing, SASTRA University, Tirumalaisamudram, Thanjavur, 613401, Tamilnadu, India.
| | - K S Ravichandran
- School of Computing, SASTRA University, Tirumalaisamudram, Thanjavur, 613401, Tamilnadu, India.
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