Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men

Wu, Qing; Nasoz, Fatma; Jung, Jong-Yun; Bhattarai, Bibek; Han, Mira V.

doi:10.1007/s00223-020-00734-y

Cited by 24 publications

(32 citation statements)

References 37 publications

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“…Three studies clearly documented methods for correcting class imbalance caused by the very low incidence of positive cases. ( 105,106,109 ) Ye and colleagues and Kruse and colleagues calibrated their prediction models by aligning the predicted with the observed probabilities, to aid its understandability and applicability in clinical practice. ( 101,108 ) Good visualization of predictors was obtained using SHAP values.…”

Section: Resultsmentioning

confidence: 99%

“…Predicting the risk of bone loss, osteoporotic fractures, falls, or comorbidities in osteoporotic patients over time was investigated in 14 studies (Table 4). ( 98–111 ) Two of them used unsupervised learning to identify fracture and comorbidity risk groups, respectively. ( 98,99 ) Kruse and colleagues developed a fracture risk clustering model to categorize subgroups of patients at risk.…”

Section: Resultsmentioning

confidence: 99%

“…( 98 ) Wang and colleagues investigated osteoporotic patients' subgroups and their related comorbidity risk. ( 99 ) The 12 remaining studies used supervised learning for the prediction of risk of osteoporosis by bone density loss at 10 years, ( 100 ) incident falls at 6 months ( 102 ) and 1 year, ( 101 ) incident vertebral fracture at ≈ 8 months, ( 103 ) hip fracture prediction at 4, 5, or 10 years, ( 107–111 ) vertebral or hip fractures at ≈ 7.5 years, ( 104 ) major osteoporotic fractures (hip, spine, wrist, or humerus) at ≈ 4.5 years, ( 105 ) and all sort of fracture sites at 1 and 2 years. ( 106 ) Because unsupervised learning is not intended to predict a predetermined outcome, no performance metrics were reported.…”

Section: Resultsmentioning

confidence: 99%

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Machine Learning Solutions for Osteoporosis—A Review

Smets

Shevroja

Hügle

et al. 2020

Journal of Bone and Mineral Research

114

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Solutions for Osteoporosis—A Review

Smets

Shevroja

Hügle

et al. 2020

Journal of Bone and Mineral Research

114

View full text Add to dashboard Cite

show abstract

“…Some models only included two or three predictors, such as Sambrook 2011 (age, prior fractures) [ 32 ], Su 2017 (TBS, femoral neck BMD) [ 46 ], Tamaki 2011 (age, weight, femoral neck BMD) [ 35 ], with AUC/C indexes being 0.78, 0.67, and 0.90, respectively. Wu 2020 [ 57 ], gSOS (for MOF) [ 58 ], and gSOS (for hip fracture) [ 58 ] included SNPs as predictors, all contained more than 1000 predictors, with AUC/C indexes being 0.71, 0.73 and 0.80, respectively.…”

Section: Resultsmentioning

confidence: 99%

Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal

Sun¹,

Chen²,

Gao³

et al. 2022

Aging and disease

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Osteoporotic fractures (OF) are a global public health problem currently. Many risk prediction models for OF have been developed, but their performance and methodological quality are unclear. We conducted this systematic review to summarize and critically appraise the OF risk prediction models. Three databases were searched until April 2021. Studies developing or validating multivariable models for OF risk prediction were considered eligible. Used the prediction model risk of bias assessment tool to appraise the risk of bias and applicability of included models. All results were narratively summarized and described. A total of 68 studies describing 70 newly developed prediction models and 138 external validations were included. Most models were explicitly developed (n=31, 44%) and validated (n=76, 55%) only for female. Only 22 developed models (31%) were externally validated. The most validated tool was Fracture Risk Assessment Tool. Overall, only a few models showed outstanding (n=3, 1%) or excellent (n=32, 15%) prediction discrimination. Calibration of developed models (n=25, 36%) or external validation models (n=33, 24%) were rarely assessed. No model was rated as low risk of bias, mostly because of an insufficient number of cases and inappropriate assessment of calibration. There are a certain number of OF risk prediction models. However, few models have been thoroughly internally validated or externally validated (with calibration being unassessed for most of the models), and all models showed methodological shortcomings. Instead of developing completely new models, future research is suggested to validate, improve, and analyze the impact of existing models.

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“…First, we included a larger sample size of BI-RADS 4 lesions (223 vs. 86), which may be the main reason. Since larger datasets could provide larger training samples, thus better accuracies for radiomic models [ 26 ]. Second, Hu et al only extracted radiomic features from the largest long-axis cross-section image, while we extracted multiplanar (axial, coronal, and sagittal) radiomic features of the lesion, thus better reflecting the biological behavior and tumoral heterogeneity, providing higher performance in predicting malignant BI-RADS 4 lesions.…”

Section: Discussionmentioning

confidence: 99%

Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions

et al. 2022

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Improving the assessment of breast imaging reporting and data system (BI-RADS) 4 lesions and reducing unnecessary biopsies are urgent clinical issues. In this prospective study, a radiomic nomogram based on the automated breast volume scanner (ABVS) was constructed to identify benign and malignant BI-RADS 4 lesions and evaluate its value in reducing unnecessary biopsies. A total of 223 histologically confirmed BI-RADS 4 lesions were enrolled and assigned to the training and validation cohorts. A radiomic score was generated from the axial, sagittal, and coronal ABVS images. Combining the radiomic score and clinical-ultrasound factors, a radiomic nomogram was developed by multivariate logistic regression analysis. The nomogram integrating the radiomic score, lesion size, and BI-RADS 4 subcategories showed good discrimination between malignant and benign BI-RADS 4 lesions in the training (AUC, 0.959) and validation (AUC, 0.925) cohorts. Moreover, 42.5% of unnecessary biopsies would be reduced by using the nomogram, but nine (4%) malignant BI-RADS 4 lesions were unfortunately missed, of which 4A (77.8%) and small-sized (<10 mm) lesions (66.7%) accounted for the majority. The ABVS radiomics nomogram may be a potential tool to reduce unnecessary biopsies of BI-RADS 4 lesions, but its ability to detect small BI-RADS 4A lesions needs to be improved.

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Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men

Cited by 24 publications

References 37 publications

Machine Learning Solutions for Osteoporosis—A Review

Machine Learning Solutions for Osteoporosis—A Review

Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal

Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions

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