ArticleNature communications2023
Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 52 citations in OpenAlex.
- Development and external evaluation of an interpretable machine-learning model for early prediction of organ failure in higher-risk acute pancreatitis patients: A multicentre cohort study.PLOS digital health · 2026Article
- A deep learning model for opportunistic screening of chronic kidney disease using chest radiographs: a multicentre validation study in the USA.EClinicalMedicine · 2026Article
- Article
- Clinically interpretable nomogram incorporating radiomics and deep learning feature fusion from abdominal CT for preclinical type 2 diabetes.Abdominal radiology (New York) · 2026Article
- Hepatic and abdominal adiposity in type 2 diabetes as assessed with machine learning on computed tomography scans.Diabetes, obesity & metabolism · 2026Article
- Prediction of Future Risk of Moderate to Severe Kidney Function Loss Using a Deep Learning Model-Enabled Chest Radiography.Journal of imaging informatics in medicine · 2026Article
- Article
- Will AI Replace Physicians in the Near Future? AI Adoption Barriers in Medicine.Diagnostics (Basel, Switzerland) · 2026Review
- Chest X-Ray Foundation Model With Global and Local Representations Integration.IEEE transactions on medical imaging · 2025Article
- Mapping the plasma metabolome to human health and disease in 274,241 adults.Nature metabolism · 2025Article
- A Pretraining Approach for Small-sample Training Employing Radiographs (PASTER): a Multimodal Transformer Trained by Chest Radiography and Free-text Reports.Journal of medical systems · 2025Article
- DRPM: An advanced predictive model for early diabetes detection and risk stratification.Molecular therapy. Nucleic acids · 2025Article
- Opportunistic use of artificial intelligence with X-ray imaging for diagnosis of HIV status in tuberculosis patients in Uganda and Tanzania.PLOS digital health · 2025Article
- XComposition: multimodal deep learning model to measure body composition using chest radiographs and clinical data.Radiology advances · 2025Article
- Assessing the Reliability of Pancreatic CT Imaging Biomarkers for Diabetes Prediction: A Dual Center Retrospective Study.Academic radiology · 2025Article
- Federated multimodal AI for precision-equitable diabetes care.Frontiers in digital health · 2025Review
- Annotation-free multi-organ anomaly detection in abdominal CT using free-text radiology reports: a multi-centre retrospective study.EBioMedicine · 2024Article
- Denoising diffusion model for increased performance of detecting structural heart disease.medRxiv : the preprint server for health sciences · 2024Article
- Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan.BMJ health & care informatics · 2024Article
- Correlation of HbA1c levels with CT-based body composition biomarkers in diabetes mellitus and metabolic syndrome.Scientific reports · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
25 authors at 13 institutions in 1 country.
Funding
Abstract
Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs. Here we show the model effectively detected T2D with a ROC AUC of 0.84 and a 16% prevalence. The algorithm flagged 1,381 cases (14%) as suspicious for T2D. External validation at a distinct institution yielded a ROC AUC of 0.77, with 5% of patients subsequently diagnosed with T2D. Explainable AI techniques revealed correlations between specific adiposity measures and high predictivity, suggesting CXRs' potential for enhanced T2D screening.
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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.