ArticleJournal of nuclear medicine : official publication, Society of Nuclear Medicine2019
Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study.
Registry-linked trialOpen access · bronzeAbstract readMulticenter Study
In one paragraph
Article in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05316025 (Grenoble Cardiovascular Digital Health Data Observatory), which is not on this map. Cited by 63 papers.
0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed
11.5field-weighted citation impact, top 1% of its field
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.
Grenoble Cardiovascular Digital Health Data Observatory
Who cites it
63 citing papers in PubMed, 151 citations in OpenAlex.
- Multi-modality deep learning-based [European journal of nuclear medicine and molecular imaging · 2024Trial
- A Prototype-Guided 3D Deep Learning Framework for Myocardial Perfusion Scintigraphy Segmentation.Journal of clinical medicine · 2026Review
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- Deep learning-based obstructive coronary artery disease prediction from myocardial perfusion SPECT.European journal of nuclear medicine and molecular imaging · 2026Article
- The importance of developing multiparametric prognostic scores to stratify coronary risk by means of artificial intelligence.European journal of nuclear medicine and molecular imaging · 2026Article
- Artificial Intelligence in Nuclear Cardiology.Journal of clinical medicine · 2025Review
- Cross-institutional validation of a polar map-free 3D deep learning model for obstructive coronary artery disease prediction using myocardial perfusion imaging: insights into generalizability and bias.European journal of nuclear medicine and molecular imaging · 2025Article
- Artificial intelligence-powered coronary artery disease diagnosis from SPECT myocardial perfusion imaging: a comprehensive deep learning study.European journal of nuclear medicine and molecular imaging · 2025Article
- Artificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Article
- Artificial Intelligence in Cardiovascular Imaging and Interventional Cardiology: Emerging Trends and Clinical Implications.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- The role of deep learning in myocardial perfusion imaging for diagnosis and prognosis: A systematic review.iScience · 2024Review
- Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging.Scientific reports · 2024Article
- Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases.Anatolian journal of cardiology · 2024Article
- Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review.Applied bionics and biomechanics · 2024Article
- Artificial Intelligence Empowered Nuclear Medicine and Molecular Imaging in Cardiology: A State-of-the-Art Review.Phenomics (Cham, Switzerland) · 2023Review
- Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study.Scientific reports · 2023Article
- Article
- Preparing for the Artificial Intelligence Revolution in Nuclear Cardiology.Nuclear medicine and molecular imaging · 2023Review
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors at 9 institutions in 4 countries.
Julian BetancurDivision of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Lien-Hsin HuDivision of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Frederic CommandeurDivision of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Tali SharirDepartment of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, Israel.
Andrew J EinsteinDivision of Cardiology, Department of Medicine, 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 KaufmannDepartment of Nuclear Medicine, Cardiac Imaging, 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.
Guido GermanoDivision of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Yuka OtakiDivision of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Joanna X LiangDivision of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Balaji K TamarappooDivision of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Damini DeyDivision of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Daniel S BermanDivision of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Piotr J SlomkaDivision of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California piotr.slomka@cshs.org.
Cedars-Sinai Medical Center · USBrigham and Women's Hospital · USYale University · USBen-Gurion University of the Negev · ILColumbia University · USHeart Imaging Technologies (United States) · USSacred Heart Medical Center · USUniversity Hospital of Zurich · CHUniversity of Ottawa · CA
Funding
Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9MQuantitative Prediction of Disease and Outcomes from Next Generation SPECT and CTR01HL089765 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI SLOMKA, PIOTR J · 2007 to 2021
$6.1MNCATS NIH HHS UL1 TR001863NHLBI NIH HHS R01 HL089765
Abstract
Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely used to mitigate attenuation artifacts. We evaluated the prediction of obstructive disease from combined analysis of semiupright and supine stress MPI by deep learning (DL) as compared with standard combined total perfusion deficit (TPD).
Indexed as
Deep LearningMyocardial Perfusion ImagingTomography, Emission-Computed, Single-PhotonAgedCoronary Artery DiseaseFemaleHeart VentriclesHumansImage Processing, Computer-AssistedMaleMiddle AgedStress, Physiologicalconvolutional neural networkdeep learningobstructive coronary artery diseaseSPECT myocardial perfusion imagingtotal perfusion deficit
Identifiers
What OpenQuestion holds
Textmetadata
Read underepoch 390
Registered trials
NCT05316025ClinicalTrials.gov
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.