Evidence map›Paper›PMID 41340775›Full record

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.

Jiwoong Jeong, Chieh-Ju Chao, Reza Arsanjani, Chadi Ayoub, Steven J Lester, Milagros Pereyra, Ebram F Said, Michael Roarke, Cecilia Tagle-Cornell, Laura M Koepke and 7 more

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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.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Jiwoong JeongSchool of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ.
Chieh-Ju ChaoDepartment of Cardiovascular Diseases, Mayo Clinic, Rochester, MN.
Reza ArsanjaniDepartment of Cardiovascular Diseases, Mayo Clinic, Scottsdale, AZ.
Chadi AyoubDepartment of Cardiovascular Diseases, Mayo Clinic, Scottsdale, AZ.
Steven J LesterDepartment of Cardiovascular Diseases, Mayo Clinic, Scottsdale, AZ.
Milagros PereyraDepartment of Cardiovascular Diseases, Mayo Clinic, Scottsdale, AZ.
Ebram F SaidDepartment of Cardiovascular Diseases, Mayo Clinic, Scottsdale, AZ.
Michael RoarkeDepartment of Cardiovascular Diseases, Mayo Clinic, Scottsdale, AZ.
Cecilia Tagle-CornellDepartment of Cardiovascular Diseases, Mayo Clinic, Scottsdale, AZ.
Laura M KoepkeDepartment of Cardiovascular Diseases, Mayo Clinic, Scottsdale, AZ.
Yi-Lin TsaiDepartment of Internal Medicine, Taipei Veterans General Hospital, Taiwan.
Chen Jung-HsuanDepartment of Radiology, Taipei Veterans General Hospital, Taiwan.
Chun-Chin ChangDepartment of Internal Medicine, Taipei Veterans General Hospital, Taiwan.
Juan M FarinaDepartment of Cardiovascular Diseases, Mayo Clinic, Scottsdale, AZ.
Hari TrivediDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Bhavik N PatelDepartment of Radiology, Mayo Clinic, Scottsdale, AZ.
Imon BanerjeeSchool of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

PMID41340775
PMCPMC12671366

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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.