ReviewEndocrinology and metabolism (Seoul, Korea)2021
Applications of Machine Learning in Bone and Mineral Research.
Review in Endocrinology and metabolism (Seoul, Korea), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 32 citations in OpenAlex.
- Attention-Guided EfficientNet-B3 with Grad-CAM Visualization for 22-Class Bone Fracture and Anatomical-Region Classification on the MultiBoneX Dataset.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- A Cross-Tissue Multiomics Analysis Reveals the Protective Role of TGFBR3 in Postmenopausal Osteoporosis.International journal of genomics · 2026Article
- Artificial Intelligence and Machine Learning in the Diagnosis and Management of Osteoporosis: A Comprehensive Review.Medicina (Kaunas, Lithuania) · 2025Review
- Prophylactic fixation in elderly fractures: Preventive breakthrough or unnecessary intervention?World journal of orthopedics · 2025Review
- Prediction of biomechanical properties of ex vivo human femoral cortical bone using Raman spectroscopy and machine learning algorithms.Bone reports · 2025Article
- Incorporating Artificial Intelligence into Fracture Risk Assessment: Using Clinical Imaging to Predict the Unpredictable.Endocrinology and metabolism (Seoul, Korea) · 2025Review
- Artificial intelligence for predicting the risk of bone fragility fractures in osteoporosis.European radiology experimental · 2025Review
- Associations between longitudinal fluoride intakes from birth to age 23 and multi-row detector computed tomography bone densitometry outcomes at age 23.Journal of public health dentistry · 2024Article
- From Genomics to Metabolomics: Molecular Insights into Osteoporosis for Enhanced Diagnostic and Therapeutic Approaches.Biomedicines · 2024Review
- A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study.Journal of medical Internet research · 2024Article
- Review
- Development and validation of common data model-based fracture prediction model using machine learning algorithm.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2023Article
- Machine learning algorithms for predicting the risk of fracture in patients with diabetes in China.Heliyon · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this unprecedented era of the overwhelming volume of medical data, machine learning can be a promising tool that may shed light on an individualized approach and a better understanding of the disease in the field of osteoporosis research, similar to that in other research fields. This review aimed to provide an overview of the latest studies using machine learning to address issues, mainly focusing on osteoporosis and fractures. Machine learning models for diagnosing and classifying osteoporosis and detecting fractures from images have shown promising performance. Fracture risk prediction is another promising field of research, and studies are being conducted using various data sources. However, these approaches may be biased due to the nature of the techniques or the quality of the data. Therefore, more studies based on the proposed guidelines are needed to improve the technical feasibility and generalizability of artificial intelligence algorithms.
Indexed as
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What OpenQuestion holds
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.