ArticleLa Radiologia medica2026
IgeaNet: a deep learning model for opportunistic osteoporosis screening from chest X-ray images.
Article in La Radiologia medica, 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and validate a multi-modal deep learning model for opportunistic screening of osteoporosis by classifying bone mineral density (BMD) from chest radiographs integrated with clinical metadata. MATERIALS AND
methodsThis single-centre retrospective study included 833 adult patients who underwent chest radiography and dual-energy X-ray absorptiometry (DEXA) within 12 months (January 2018-December 2024). A convolutional neural network (CNN) model was developed using a convolutional branch to process chest radiographs, while a dense branch processed patient demographics (age, sex, height, and weight). Clinical data available in the DEXA report were also included. The two representations were merged into a fully connected classifier optimized end-to-end.
resultsThe dataset comprised training (n = 589), validation (n = 124), and testing (n = 120) sets. On the internal test set, the model achieved 85.8% accuracy (95% CI: 78.5-90.9%), F1-macro 0.86, and AUC-macro 0.95. Class-specific performance showed sensitivity/specificity of 84.2%/91.4% for normal, 90.2%/77.1% for osteopenia, and 82.3%/91.9% for osteoporosis, with 14.2% misclassification rate. Analysis of DEXA anatomical site distribution revealed wrist as the determining site in 302 patients (36.3%), hip in 384 patients (46.1%), and lumbar spine in 141 patients (16.9%). Inclusion of wrist measurements changed diagnostic classification in 115 patients (13.8%), with 34 patients reclassified from normal to osteopenia, 14 from normal to osteoporosis, and 67 from osteopenia to osteoporosis. This differential diagnostic yield reflects distinct bone composition differences, as wrist cortical bone may reveal mineral density reductions undetected at predominantly trabecular sites.
conclusionsIntegration of chest radiographs and clinical data by residual network with attention represents a feasible approach for the preliminary opportunistic classification of BMD without additional radiation exposure. However, these findings are hypothesis-generating and derive from internal validation; external prospective studies are required before clinical implementation.
Indexed as
Identifiers
42753041What 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.