ArticleArchives of osteoporosis2024
External validation of a deep learning model for predicting bone mineral density on chest radiographs.
Article in Archives of osteoporosis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 17 citations in OpenAlex.
- IgeaNet: a deep learning model for opportunistic osteoporosis screening from chest X-ray images.La Radiologia medica · 2026Article
- Diagnostic accuracy of an artificial intelligence-based osteoporosis screening system on portable chest radiographs.Journal of thoracic disease · 2026Article
- Clinical impact of adopting an AI-driven opportunistic bone health screening software using routine X-ray.Skeletal radiology · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Explainable opportunistic osteoporosis screening from chest X-rays: a retrospective comparison of foundation models.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- Cost-effectiveness of opportunistic osteoporosis screening using artificial-intelligence assisted chest radiographs in Japan.Osteoporosis and sarcopenia · 2025Article
- Article
- Incorporating Artificial Intelligence into Fracture Risk Assessment: Using Clinical Imaging to Predict the Unpredictable.Endocrinology and metabolism (Seoul, Korea) · 2025Review
- Article
- Cost-effectiveness of opportunistic osteoporosis screening using chest radiographs with deep learning in Germany.Aging clinical and experimental research · 2025Article
- Utility of osteoporosis screening based on estimation of bone mineral density using bidirectional chest radiographs with deep learning models.Frontiers in medicine · 2025Article
- Transferable CNN-based data mining approaches for medical imaging: application to spine DXA scans for osteoporosis detection.Frontiers in computational neuroscience · 2025Article
- Failure of AI-driven diagnostic tool: lessons and strategies to prevent patient harm.Archives of osteoporosis · 2024Article
- Rebuttal submission to "Failure of AI-driven diagnostic tool: lessons and strategies to prevent patient harm".Archives of osteoporosis · 2024Article
- Clinical Validation of a Deep Learning-Based Software for Lumbar Bone Mineral Density and T-Score Prediction from Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed a new model for predicting bone mineral density on chest radiographs and externally validated it using images captured at facilities other than the development environment. The model performed well and showed potential for clinical use. PURPOSE: In this study, we performed external validation (EV) of a developed deep learning model for predicting bone mineral density (BMD) of femoral neck on chest radiographs to verify the usefulness of this model in clinical practice.
methodsThis study included patients who visited any of the collaborating facilities from 2010 to 2020 and underwent chest radiography and dual-energy X-ray absorptiometry (DXA) at the femoral neck in the year before and after their visit. A total of 50,114 chest radiographs were obtained, and BMD was measured using DXA. We developed the model with 47,150 images from 17 facilities and performed EV with 2914 images from three other facilities (EV dataset). We trained the deep learning model via ensemble learning based on chest radiographs, age, and sex to predict BMD using regression. The outcomes were the correlation of the predicted BMD and measured BMD with diagnoses of osteoporosis and osteopenia using the T-score estimated from the predicted BMD.
resultsThe mean BMD was 0.64±0.14 g/cm
conclusionOur model, which was externally validated using data obtained at facilities other than the development environment, predicted BMD of femoral neck on chest radiographs. The model performed well and showed potential for clinical use.
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.