ReviewMedical & biological engineering & computing2020
Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey.
Review in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- In-depth meta-analysis: unilateral PKP demonstrates significant advantages in treating osteoporotic vertebral compression fractures-an expanded RCT study with GRADE scoring.Frontiers in surgery · 2025Pooled it
- Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Deep Learning for Osteoporosis Diagnosis Using Magnetic Resonance Images of Lumbar Vertebrae.Journal of imaging informatics in medicine · 2026Article
- Deep‑learning based osteoporosis classification in knee X‑rays using transfer‑learning approach.Scientific reports · 2025Article
- Breast cancer detection and classification with digital breast tomosynthesis: a two-stage deep learning approach.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- SSDL-an automated semi-supervised deep learning approach for patient-specific 3D reconstruction of proximal femur from QCT images.Medical & biological engineering & computing · 2024Article
- Osteoporosis Screening Using Dental Panoramic Radiographs and Age at Menarche.Diagnostics (Basel, Switzerland) · 2023Article
- Osteoporosis diagnosis in knee X-rays by transfer learning based on convolution neural network.Multimedia tools and applications · 2023Article
- AI-Assisted Ultrasound for the Early Diagnosis of Antibody-Negative Autoimmune Thyroiditis.Journal of multidisciplinary healthcare · 2023Review
- Application of a Modified Combinational Approach to Brain Tumor Detection in MR Images.Journal of digital imaging · 2022Article
- Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis.Medicina (Kaunas, Lithuania) · 2021Article
- A Review on Multiscale Bone Damage: From the Clinical to the Research Perspective.Materials (Basel, Switzerland) · 2021Review
- Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates.Biomolecules · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided diagnosis (CAD) has revolutionized the field of medical diagnosis. They assist in improving the treatment potentials and intensify the survival frequency by early diagnosing the diseases in an efficient, timely, and cost-effective way. The automatic segmentation has led the radiologist to successfully segment the region of interest to improve the diagnosis of diseases from medical images which is not so efficiently possible by manual segmentation. The aim of this paper is to survey the vision-based CAD systems especially focusing on the segmentation techniques for the pathological bone disease known as osteoporosis. Osteoporosis is the state of the bones where the mineral density of bones decreases and they become porous, making the bones easily susceptible to fractures by small injury or a fall. The article covers the image acquisition techniques for acquiring the medical images for osteoporosis diagnosis. The article also discusses the advanced machine learning paradigms employed in segmentation for osteoporosis disease. Other image processing steps in osteoporosis like feature extraction and classification are also briefly described. Finally, the paper gives the future directions to improve the osteoporosis diagnosis and presents the proposed architecture. Graphical abstract.
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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.