Evidence map›Paper›PMID 42315311›Full record

ArticleJournal of nuclear medicine : official publication, Society of Nuclear Medicine2026

Revisiting Perfusion in the Forgotten Right Ventricle: Artificial Intelligence-Enhanced Quantification on PET/CT.

Aakash Shanbhag, Robert J H Miller, Paul Kavanagh, Giselle Ramirez, Mark Lemley, Jacek Kwiecinski, Assiata Kamagate, Leandro Slipczuk, Mark I Travin, Erick Alexanderson and 15 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

25 authors.

Aakash ShanbhagArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, California.
Robert J H MillerArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, California.
Paul KavanaghArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, California.
Giselle RamirezArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, California.
Mark LemleyArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, California.
Jacek KwiecinskiDepartment of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland.
Assiata KamagateArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, California.
Leandro SlipczukCardiology Division, Montefiore Health System and Albert Einstein College of Medicine, Bronx, New York.
Mark I TravinDepartment of Radiology (Nuclear Medicine), Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, New York.
Erick AlexandersonFaculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico.
Isabel Carvajal-JuarezFaculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico.
René R S PackardDivision of Cardiology, Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, California.
Mouaz Al-MallahHouston Methodist DeBakey Heart and Vascular Center, Houston Methodist Academic Institute, Houston, Texas.
Andrew J EinsteinDivision of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York.
Wanda AcampaDepartment of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
Stacey KnightIntermountain Medical Center Heart Institute, Intermountain Healthcare, Murray, Utah.
Viet T LeIntermountain Medical Center Heart Institute, Intermountain Healthcare, Murray, Utah.
Steve MasonIntermountain Medical Center Heart Institute, Intermountain Healthcare, Murray, Utah.
Samuel WoppererDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Panithaya ChareonthaitaweeDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Thomas L RosamondDepartment of Cardiovascular Medicine, University of Kansas Medical Center, Kansas City, Kansas; and.
Daniel S BermanArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, California.
Damini DeyArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, California.
Marcelo F Di CarliCardiovascular Imaging Program, Departments of Radiology and Medicine; Division of Nuclear Medicine and Molecular Imaging, Department of Radiology; and Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
Piotr J SlomkaArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, California; 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 HL161195NIBIB NIH HHS R01 EB034586
6 · The paper itself

Abstract

Increased right ventricular (RV) radiotracer uptake on perfusion imaging has been recognized as a marker of increased cardiovascular risk. However, this uptake is challenging to quantify because of the variable intensity of uptake in a thin structure. We used a validated artificial intelligence-enhanced method for segmenting the right ventricle from CT attenuation correction (CTAC) imaging to automatically quantify RV activity and then evaluated its prognostic significance.

Indexed as

Artificial IntelligenceHeart VentriclesImage Processing, Computer-AssistedMyocardial Perfusion ImagingPositron Emission Tomography Computed TomographyAgedFemaleHumansMaleMiddle Agedartificial intelligencedeep learningnuclear cardiologyright ventricle

Identifiers

PMID42315311
PMCPMC13435549

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.