Evidence map›Paper›PMID 39978815›Full record

ArticleJournal of nuclear medicine : official publication, Society of Nuclear Medicine2025

Artificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging.

Robert J H Miller, Paul Kavanagh, Mark Lemley, Joanna X Liang, Tali Sharir, Andrew J Einstein, Mathews B Fish, Terrence D Ruddy, Philipp A Kaufmann, Albert J Sinusas and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Deep learning-based obstructive coronary artery disease prediction from myocardial perfusion SPECT.European journal of nuclear medicine and molecular imaging · 2026
    Article
  3. Article
  4. Artificial Intelligence in Nuclear Cardiology.Journal of clinical medicine · 2025
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Robert J H MillerDivision of Artificial Intelligence in Medicine, Departments of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Paul KavanaghDivision of Artificial Intelligence in Medicine, Departments of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Mark LemleyDivision of Artificial Intelligence in Medicine, Departments of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Joanna X LiangDivision of Artificial Intelligence in Medicine, Departments of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Tali SharirDepartment of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, Israel, and Ben Gurion University of the Negev, Beer Sheba, Israel.
Andrew J EinsteinDivision of Cardiology, Departments of Medicine, and Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York.
Mathews B FishOregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, Oregon.
Terrence D RuddyDivision of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada.
Philipp A KaufmannCardiac Imaging, Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.
Albert J SinusasSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut.
Edward J MillerSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut.
Timothy M BatemanCardiovascular Imaging Technologies LLC, Kansas City, Missouri; and.
Sharmila DorbalaDivision of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts.
Marcelo Di CarliDivision of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts.
Sean HayesDivision of Artificial Intelligence in Medicine, Departments of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
John FriedmanDivision of Artificial Intelligence in Medicine, Departments of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Daniel S BermanDivision of Artificial Intelligence in Medicine, Departments of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Damini DeyDivision of Artificial Intelligence in Medicine, Departments of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Piotr J SlomkaDivision of Artificial Intelligence in Medicine, Departments of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; piotr.slomka@cshs.org.

Funding

Quantitative Prediction of Disease and Outcomes from Next Generation SPECT and CTR01HL089765 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI SLOMKA, PIOTR J · 2007 to 2021
$6.1M
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
NHLBI NIH HHS R01 HL089765NHLBI NIH HHS R35 HL161195
6 · The paper itself

Abstract

We previously demonstrated that a deep learning (DL) model of myocardial perfusion SPECT imaging improved accuracy for detection of obstructive coronary artery disease (CAD). We aimed to improve the clinical translatability of this artificial intelligence (AI) approach using the results to derive enhanced total perfusion deficit (TPD) and 17-segment summed scores.

Indexed as

Artificial IntelligenceCoronary Artery DiseaseMyocardial Perfusion ImagingAgedDeep LearningFemaleHumansMaleMiddle AgedTomography, Emission-Computed, Single-Photonartificial intelligencedeep learningdiagnostic accuracymyocardial perfusion imagingquantification

Identifiers

PMID39978815
PMCPMC11960614

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.