A Prediction Model for the Risk of Osteoporosis Fracture in the Elderly Based on a Neural Network

Wang, Yong; Zhang, Zhiqiang; Cai, Nanchuan; Zhou, Yousheng; Xiao, Di

doi:10.1007/978-3-319-92537-0_92

Cited by 4 publications

(7 citation statements)

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“…Clinical data have been used previously for the identification of individuals with osteoporosis or osteoporotic fracture [ 22 , 23 ] and demographic characteristics, in particular, have been widely used in multiple osteoporosis assessment tools [ 18 ]. In the first and second layers of the proposed model, the performance of demographic characteristics and clinical data were tested, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, all these classifiers provided similar performances, which demonstrated the effectiveness of clinical data in osteoporosis discrimination. Among clinical data, menopause status, age, and BMI were the most important indicators that were consistent with known risk factors [ 18 , 22 , 23 ]. Additionally, Clinical 6 (red blood cell count), Clinical 9 (alkaline phosphatase), and Clinical 11 (albumin) were also helpful in identifying individuals with osteoporosis, since they were considered as top 10 important features by one or two classifiers.…”

Section: Discussionmentioning

confidence: 99%

“…According to Occam's Razor that "entities should not be multiplied beyond necessity" [38], a simpler model based on LR would be preferred in our work, which achieved an AUC of 0.818, 0.838 and 0.962, respectively. S6 Clinical data have been used previously for the identification of individuals with osteoporosis or osteoporotic fracture [22,23] and demographic characteristics, in particular, have been widely used in multiple osteoporosis assessment tools [18]. In the first and second layers of the proposed model, the performance of demographic characteristics and clinical data were tested, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…de Lira, et al [ 21 ] used J48 decision tree algorithm to discriminate between osteoporosis and osteopenia in women via BMI, age, menopause status, and other risk factors and obtained an AUC of 0.65. Besides these demographic characteristics, other studies [ 22 , 23 ] also considered several routine laboratory tests data, including alkaline phosphatase, calcium, phosphorus, the numbers of hemoglobin and lymphocyte and albumin to predict osteoporosis risk. Such approaches underline the benefits of routine laboratory tests data in identifying osteoporosis risk.…”

Section: Introductionmentioning

confidence: 99%

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A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images

Liu

et al. 2022

BMC Bioinformatics

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images

Liu

et al. 2022

BMC Bioinformatics

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“…Already published studies investigating machine learning and CNN approaches to predict future osteoporotic fractures are mainly risk assessment tools requiring the input of existing clinical examination data, such as aBMD derived from DXA, but not generating any new BMD data for fracture prediction [ 24 , 25 , 26 ]. Other approaches focused on machine learning combined with texture analysis of vertebrae, however, not did not take BMD into consideration [ 27 ], or performed feature extraction by a deep-learning algorithm from lateral spine radiographs by using non-CNN-generated aBMD values from DXA-scans [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Opportunistic Trabecular Volumetric Bone Mineral Density Extraction Outperforms Manual Measurements for the Prediction of Vertebral Fractures in Routine CT

et al. 2023

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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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A Systematic Review on Osteoporosis Prediction in Postmenopausal Women

Mohankumar

2024

2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST)

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A Prediction Model for the Risk of Osteoporosis Fracture in the Elderly Based on a Neural Network

Cited by 4 publications

References 5 publications

A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images

A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images

Automated Opportunistic Trabecular Volumetric Bone Mineral Density Extraction Outperforms Manual Measurements for the Prediction of Vertebral Fractures in Routine CT

A Systematic Review on Osteoporosis Prediction in Postmenopausal Women

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