ArticleAnnals of biomedical engineering2019
Hip Fracture Discrimination Based on Statistical Multi-parametric Modeling (SMPM).
Article in Annals of biomedical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Augmenting Osteoporosis Imaging with Machine Learning.Current osteoporosis reports · 2021Pooled it
- Simulated low-dose multi-detector computed tomography: spatial effects on surrogate parameters of bone strength at the proximal femur.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Article
- Male-female spatio-temporal differences of age-related bone changes show faster bone deterioration in older women at femoral regions associated with incident hip fracture.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Article
- Performance of iCare quantitative computed tomography in bone mineral density assessment of the hip and vertebral bodies in European spine phantom.Journal of orthopaedic surgery and research · 2023Article
- Differences in Hip Geometry Between Female Subjects With and Without Acute Hip Fracture: A Cross-Sectional Case-Control Study.Frontiers in endocrinology · 2022Article
- Applications of Machine Learning in Bone and Mineral Research.Endocrinology and metabolism (Seoul, Korea) · 2021Review
- Artificial intelligence in orthopaedics: A scoping review.PloS one · 2021Article
- Economic evaluations of big data analytics for clinical decision-making: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Machine Learning Applications in Orthopaedic Imaging.The Journal of the American Academy of Orthopaedic Surgeons · 2020Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Studies using quantitative computed tomography (QCT) and data-driven image analysis techniques have shown that trabecular and cortical volumetric bone mineral density (vBMD) can improve the hip fracture prediction of dual-energy X-ray absorptiometry areal BMD (aBMD). Here, we hypothesize that (1) QCT imaging features of shape, density and structure derived from data-driven image analysis techniques can improve the hip fracture discrimination of classification models based on mean femoral neck aBMD (Neck.aBMD), and (2) that data-driven cortical bone thickness (Ct.Th) features can improve the hip fracture discrimination of vBMD models. We tested our hypotheses using statistical multi-parametric modeling (SMPM) in a QCT study of acute hip fracture of 50 controls and 93 fragility fracture cases. SMPM was used to extract features of shape, vBMD, Ct.Th, cortical vBMD, and vBMD in a layer adjacent to the endosteal surface to develop hip fracture classification models with machine learning logistic LASSO. The performance of these classification models was evaluated in two aspects: (1) their hip fracture classification capability without Neck.aBMD, and (2) their capability to improve the hip fracture classification of the Neck.aBMD model. Assessments were done with 10-fold cross-validation, areas under the receiver operating characteristic curve (AUCs), differences of AUCs, and the integrated discrimination improvement (IDI) index. All LASSO models including SMPM-vBMD features, and the majority of models including SMPM-Ct.Th features performed significantly better than the Neck.aBMD model; and all SMPM features significantly improved the hip fracture discrimination of the Neck.aBMD model (Hypothesis 1). An interesting finding was that SMPM-features of vBMD also captured Ct.Th patterns, potentially explaining the superior classification performance of models based on SMPM-vBMD features (Hypothesis 2). Age, height and weight had a small impact on model performances, and the model of shape, vBMD and Ct.Th consistently yielded better performances than the Neck.aBMD models. Results of this study clearly support the relevance of bone density and quality on the assessment of hip fracture, and demonstrate their potential on patient and healthcare cost benefits.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.