ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2016
Bone texture characterization for osteoporosis diagnosis using digital radiography.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Is jaw fractal dimension a reliable biomarker for osteoporosis screening? A systematic review and meta-analysis of diagnostic test accuracy studies.Dento maxillo facial radiology · 2022Pooled it
- Osteoporosis prediction from hand X-ray images using segmentation-for-classification and self-supervised learning.Scientific reports · 2025Article
- Bayesian Graphical Models for Multiscale Inference in Medical Image-Based Joint Degeneration Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence in pediatric osteopenia diagnosis: evaluating deep network classification and model interpretability using wrist X-rays.Bone reports · 2025Article
- A highly generalized classifier for osteoporosis radiography based on multiscale fractal, lacunarity, and entropy distributions.Frontiers in bioengineering and biotechnology · 2023Article
- Comparison of the Classification Results Accuracy for CT Soft Tissue and Bone Reconstructions in Detecting the Porosity of a Spongy Tissue.Journal of clinical medicine · 2022Article
- Deep Radiomics-based Approach to the Diagnosis of Osteoporosis Using Hip Radiographs.Radiology. Artificial intelligence · 2022Article
- Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey.Medical & biological engineering & computing · 2020Review
- Spatially localized sparse representations for breast lesion characterization.Computers in biology and medicine · 2020Article
- Integrative blockwise sparse analysis for tissue characterization and classification.Artificial intelligence in medicine · 2020Article
- Automated skeletal tissue quantification in the lower leg using peripheral quantitative computed tomography.Physiological measurement · 2018Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
We introduce texture classification techniques to effectively diagnose osteoporosis in bone radiography data. Osteoporosis is an age-related systemic bone skeletal disorder characterized by low bone mass and bone structure deterioriation that results in increased bone fragility and higher fracture risk. Therefore, early diagnosis can effectively predict fracture risk and prevent the disease. Automated diagnosis from digital radiographs is very challenging since the scans of healthy and osteoporotic subjects show little or no visual differences, and their density histograms mostly overlap. We designed a system to separate healthy from osteoporotic subjects using high-dimensional textural feature representations computed from radiographs. These features were then reduced using feature selection to obtain the more discriminant subset that was finally classified by our methods. The top performing approach yields 79.3% accuracy and 81% area under the ROC over 116 bone radiographs.
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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.