Evidence map›Paper›PMID 41535345›Full record

ArticleNPJ digital medicine2026

Multicenter evaluation of interpretable AI for coronary artery disease diagnosis from PET biomarkers.

Wenhao Zhang, Jacek Kwiecinski, Aakash Shanbhag, Robert J H Miller, Shiva Mostafavi, Giselle Ramirez, Jirong Yi, Donghee Han, Damini Dey, Dominika Grodecka and 19 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

29 authors.

Wenhao ZhangArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Jacek KwiecinskiDepartment of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland.
Aakash ShanbhagArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Robert J H MillerArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Shiva MostafaviArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Giselle RamirezArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Jirong YiArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Donghee HanArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Damini DeyArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Dominika GrodeckaArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Kajetan GrodeckiArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Mark LemleyArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Paul KavanaghArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Joanna X LiangArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Jianhang ZhouArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Valerie BuiloffArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Jon HainerDepartment of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, MA, USA.
Sylvain CarreDepartment of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, MA, USA.
Leanne BarrettDepartment of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, MA, USA.
Andrew J EinsteinDivision of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center/New York-Presbyterian Hospital, New York, NY, USA.
Stacey KnightIntermountain Medical Center Heart Institute, Intermountain Healthcare, Murray, UT, USA.
Steve MasonIntermountain Medical Center Heart Institute, Intermountain Healthcare, Murray, UT, USA.
Viet T LeIntermountain Medical Center Heart Institute, Intermountain Healthcare, Murray, UT, USA.
Wanda AcampaDepartment of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
Samuel WoppererDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Panithaya ChareonthaitaweeDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Daniel S BermanArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Marcelo F Di Carli *Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, MA, USA.
Piotr J Slomka *Artificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Piotr.Slomka@cshs.org.

Funding

Patient-specific Outcome Prediction from Cardiovascular Multimodality Imaging by Artificial IntelligenceR35HL161195 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI Piotr J Slomka · 2022 to 2026
$5.0M
Integrating Artificial Intelligence for Optimal Analysis of CardiacPET/CTR01EB034586 · NIBIB · CEDARS-SINAI MEDICAL CENTER · PI DI CARLI, MARCELO F, SLOMKA, PIOTR J · 2022 to 2025
$2.9M
NHLBI NIH HHS R35 HL161195NHLBI NIH HHS R35HL161195NIBIB NIH HHS R01 EB034586NIBIB NIH HHS R01EB034586
6 · The paper itself

Abstract

Positron emission tomography (PET)/computed tomography (CT) for myocardial perfusion imaging (MPI) provides multiple imaging biomarkers, often evaluated separately. We developed an artificial intelligence (AI) model integrating key clinical PET MPI parameters to improve the diagnosis of obstructive coronary artery disease (CAD). From 17,348 patients undergoing cardiac PET/CT across four sites, 1664 with invasive coronary angiography and no prior CAD were retrospectively analyzed. Coronary artery calcium (CAC) scores were derived from CT attenuation correction maps, and XGBoost model was trained on one site using 10 image-derived parameters: CAC, stress/rest left ventricular ejection fraction, stress myocardial blood flow (MBF), myocardial flow reserve (MFR), ischemic and stress total perfusion deficit (TPD), transient ischemic dilation ratio, rate pressure product, and sex. External validation was performed across three independent sites. In the testing cohort (n = 1278; CAD prevalence 53%), the AI model achieved an area under the receiver operating characteristic curve (AUC) of 0.83 (95% CI: 0.81-0.85), outperforming experienced physicians (0.80, p = 0.02) and individual biomarkers such as ischemic TPD (0.79, p < 0.001) and MFR (0.75, p < 0.001). Performance was consistent across sex, body mass index, and age. AI integrating perfusion, flow, and CAC scoring improves PET MPI diagnostic accuracy, offering automated and interpretable predictions for CAD diagnosis.

Identifiers

PMID41535345
PMCPMC12901043

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.