ReviewJournal of orthopaedic surgery and research2023
AI algorithms for accurate prediction of osteoporotic fractures in patients with diabetes: an up-to-date review.
Review in Journal of orthopaedic surgery and research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
15 citing papers in PubMed, 17 citations in OpenAlex.
- Artificial Intelligence-Driven Precision Bisphosphonate Therapy in Osteoporosis: Mechanisms, Clinical Applications, Risks, and Future Directions.Orthopaedic surgery · 2026Review
- In silico augmentation strategies for enhanced machine learning performance in fracture recognition.Scientific reports · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Association between the nutritional risk index and postmenopausal osteoporosis in patients with type 2 diabetes mellitus.Frontiers in nutrition · 2026Article
- Magnetic resonance imaging appearance of targeted muscle reinnervation and regenerative peripheral nerve interfaces in oncologic amputees.Skeletal radiology · 2026Article
- Fueling bone loss: the immunometabolic reprogramming of the bone microenvironment in diabetic osteoporosis.Frontiers in immunology · 2026Review
- Artificial Intelligence in Postmenopausal Health: From Risk Prediction to Holistic Care.Journal of clinical medicine · 2025Review
- Artificial intelligence for predicting the risk of bone fragility fractures in osteoporosis.European radiology experimental · 2025Review
- Bridging the gap: enhancing osteoporosis management through AI-driven predictive models.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Article
- Implant Design and Its Applications in the Fixation of Osteoporotic Bones: Newer Technologies in Nails, Plates and External Fixators.Indian journal of orthopaedics · 2025Review
- Development and evaluation of a multidisciplinary intervention program for osteoporotic hip fractures in the elderly.Frontiers in medicine · 2025Article
- Machine learning approaches to predict hip fracture incidence: insights from the CHARLS dataset.Frontiers in public health · 2025Article
- The use of the FRAX® tool and its adjustments in women living with diabetes: a cohort study in primary care in Brazil.Archives of osteoporosis · 2024Article
- Intraoperative Periprosthetic Fractures in Total Hip Arthroplasty: A 1.6-Million-Patient Analysis of Complications, Costs, and the Challenges in AI-Based Prediction.Journal of clinical medicine · 2024Article
- From Genomics to Metabolomics: Molecular Insights into Osteoporosis for Enhanced Diagnostic and Therapeutic Approaches.Biomedicines · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Osteoporotic fractures impose a substantial burden on patients with diabetes due to their unique characteristics in bone metabolism, limiting the efficacy of conventional fracture prediction tools. Artificial intelligence (AI) algorithms have shown great promise in predicting osteoporotic fractures. This review aims to evaluate the application of traditional fracture prediction tools (FRAX, QFracture, and Garvan FRC) in patients with diabetes and osteoporosis, review AI-based fracture prediction achievements, and assess the potential efficiency of AI algorithms in this population. This comprehensive literature search was conducted in Pubmed and Web of Science. We found that conventional prediction tools exhibit limited accuracy in predicting fractures in patients with diabetes and osteoporosis due to their distinct bone metabolism characteristics. Conversely, AI algorithms show remarkable potential in enhancing predictive precision and improving patient outcomes. However, the utilization of AI algorithms for predicting osteoporotic fractures in diabetic patients is still in its nascent phase, further research is required to validate their efficacy and assess the potential advantages of their application in clinical practice.
Indexed as
Identifiers
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.