ArticleArchives of osteoporosis2025
Total radius BMD correlates with the hip and lumbar spine BMD among post-menopausal patients with fragility wrist fracture in a machine learning model.
Article in Archives of osteoporosis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Low forearm bone density as a clue to suspect primary hyperparathyroidism in postmenopausal women with osteoporosis: a retrospective study.Archives of osteoporosis · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Osteoporosis screening should be systematic in the group of over 50-year-old females with a radius fracture. We tested a phantom combined with machine learning model and studied osteoporosis-related variables. This machine learning model for screening osteoporosis using plain radiographs requires further investigation in larger cohorts to assess its potential as a replacement for DXA measurements in settings where DXA is not available. PURPOSE: The main purpose of this study was to improve osteoporosis screening, especially in post-menopausal patients with fragility wrist fractures. The secondary objective was to increase understanding of the connection between osteoporosis and aging, as well as other risk factors.
methodsWe collected data on 83 females > 50 years old with a distal radius fracture treated at Oulu University Hospital in 2019-2020. The data included basic patient information, WHO FRAX tool, blood tests, X-ray imaging of the fractured wrist, and DXA scanning of the non-fractured forearm, both hips, and the lumbar spine. Machine learning was used in combination with a custom phantom.
resultsEighty-five percent of the study population had osteopenia or osteoporosis. Only 28.4% of patients had increased bone resorption activity measured by ICTP values. Total radius BMD correlated with other osteoporosis-related variables (age r = - 0.494, BMI r = 0.273, FRAX osteoporotic fracture risk r = - 0.419, FRAX hip fracture risk r = - 0.433, hip BMD r = 0.435, and lumbar spine BMD r = 0.645), but the ultra distal (UD) radius BMD did not. Our custom phantom combined with a machine learning model showed potential for screening osteoporosis, with the class-wise accuracies for "Osteoporotic vs. osteopenic & normal bone" of 76% and 75%, respectively.
conclusionWe suggest osteoporosis screening for all females over 50 years old with wrist fractures. We found that the total radius BMD correlates with the central BMD. Due to the limited sample size in the phantom and machine learning parts of the study, further research is needed to make a clinically useful tool for screening osteoporosis.
Indexed as
Identifiers
40366492What 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.