ArticleMayo Clinic proceedings. Digital health2025
Artificial Intelligence Chest X-Ray Opportunistic Screening Model for Coronary Artery Calcium Deposition: A Multi-Objective Model With Multimodal Data Fusion.
Article in Mayo Clinic proceedings. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- A deep learning model for opportunistic screening of chronic kidney disease using chest radiographs: a multicentre validation study in the USA.EClinicalMedicine · 2026Article
- Artificial intelligence vs human opportunistic detection of vertebral fractures in routine CT scans: results of a pilot study.Hormones (Athens, Greece) · 2026Article
- The application of artificial intelligence in the intersection of metabolic dysfunction-associated steatotic liver disease and cardiovascular diseases.Frontiers in immunology · 2026Review
- Article
- Review
Corrections and comments
- Update of
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To create an opportunistic screening model to predict coronary calcium burden and associated cardiovascular risk using only commonly available frontal chest x-rays (CXR) and patient demographics. Patients and Methods: We proposed a novel multitask learning framework and trained a model using 2121 patients with paired gated computed tomography scans and CXR images internally (Mayo Clinic) from January 1, 2012, to December 31, 2022, with coronary artery calcification (CAC) scores (0, 1-99, and 100+) as ground truths. Results from the internal training were validated on multiple external datasets (Emory University Healthcare and Taipei Veterans General Hospital-from January 1, 2012, to December 31, 2022) with significant racial and ethnic differences. Results: Classification performance between 0, 1-99, and 100+ CAC scores performed moderately on both the internal test and external datasets, reaching average f1-scores of 0.71±0.04 for Mayo, 0.65±0.02 for Emory University Healthcare, and 0.70±0.06 for Taipei Veterans General Hospital. For the clinically relevant risk identification, the performance of our model on the internal and 2 external datasets reached area under the receiver operating curves of 0.86±0.02, 0.77±0.03, and 0.82±0.03 for 0 versus 400+, respectively. For 0 versus 100+, we achieved area under the receiver operating curve of 0.83±0.03, 0.71±0.02, and 0.78±0.01, respectively. Prospective evaluation across 3 Mayo Clinic sites is on par with the external validations and reports only minimal temporal drift. Conclusion: Open-source fusion artificial intelligence-CXR model performed better than existing state-of-the-art models for predicting CAC scores only on internal cohort, with robust performance on external datasets. This proposed model may be useful as a robust, first-pass opportunistic screening method for cardiovascular risk from regular CXR.
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.