ReviewCurrent osteoporosis reports2026
Current Status of AI-Assisted Screening for Opportunistic Osteoporosis.
Review in Current osteoporosis reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
purpose of reviewThis review aims to summarise the current application and development trends of artificial intelligence in opportunistic screening for osteoporosis, with a focus on its potential to improve early detection and management of the disease. RECENT
findingsRecent advancements in AI, including radiomics, deep learning, and transfer learning, have significantly enhanced the efficacy of opportunistic screening for osteoporosis. Imaging modalities such as chest X-rays, lumbar X-rays, chest CT, hip joint CT, PET-CT, and lumbar magnetic resonance imaging have been successfully integrated into AI-driven screening protocols. These technologies have demonstrated acceptable efficacy in detecting osteoporosis, with the potential to increase the probability of identifying at-risk individuals. The evolution of image processing and segmentation techniques further supports the prospect of achieving fully automated AI-based opportunistic screening in the near future. The integration of AI into opportunistic screening for osteoporosis shows promise in improving early detection rates, particularly in aging populations. Achieving automatic segmentation of Region of Interest areas based on common medical imaging is a critical step in enabling opportunistic osteoporosis screening using artificial intelligence.
Indexed as
Identifiers
41563609What 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.