ArticleMedicine2026
Artificial intelligence models for osteoporosis risk prediction in postmenopausal women with CKD: A cross-sectional analysis of NHANES 2005 to 2018.
Article in Medicine, 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
7 authors.
Funding
Abstract
Artificial intelligence (AI) can transform osteoporosis (OP) screening, but its application in high-risk, complex populations like postmenopausal women with chronic kidney disease remains limited. We sought to develop and interpret an explainable AI model specifically for identifying low bone mass (LBM) or OP in this vulnerable group. Using data from the National Health and Nutrition Examination Survey (2005-2018), a nationally representative cross-sectional study, we developed and compared 8 AI models. For the purpose of algorithm development and comparison, the complex survey weights of National Health and Nutrition Examination Survey were not applied. Model performance was assessed by AUC-ROC, recall, precision, F1 score, and Brier score. Interpretability was achieved using SHapley Additive exPlanations, local interpretable model-agnostic explanations (LIME) and generalized additive models to identify key features and their nonlinear interactions. The multilayer perceptron model emerged as the most effective (AUC: 0.72; Precision: 0.84). Interpretation revealed that weight, age, estimated glomerular filtration rate (eGFR), age when heaviest weight, total cholesterol (TC), and age at last menstrual period were top predictors. Critically, we discovered that the relationship between weight and LBM/OP is nonlinear, and that complex interactions exist between weight, age, and age when heaviest weight. This study presents a well-validated and interpretable AI model for LBM/OP screening in postmenopausal women with chronic kidney disease. By moving beyond a "black-box" approach, we provide a tool with potential clinical utility and novel pathophysiological insights, though its implementation requires further external validation in prospective, independent cohorts. This work underscores the potential of AI to enhance understanding of OP risk and highlights the necessity of personalized risk evaluation in this population.
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.