ArticleEuropean heart journal. Cardiovascular Imaging2021
Prognostically safe stress-only single-photon emission computed tomography myocardial perfusion imaging guided by machine learning: report from REFINE SPECT.
Article in European heart journal. Cardiovascular Imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 64 citations in OpenAlex.
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Nuclear Cardiac Imaging: Novel Advances, Emerging Techniques, and Recent Clinical Trials.Journal of clinical medicine · 2025Review
- Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.NPJ digital medicine · 2025Article
- The Updated Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT 2.0).Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Review
- Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [Scientific reports · 2024Article
- Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.medRxiv : the preprint server for health sciences · 2024Article
- Artificial intelligence in detecting left atrial appendage thrombus by transthoracic echocardiography and clinical features: the Left Atrial Thrombus on Transoesophageal Echocardiography (LATTEE) registry.European heart journal · 2024Article
- Artificial Intelligence Empowered Nuclear Medicine and Molecular Imaging in Cardiology: A State-of-the-Art Review.Phenomics (Cham, Switzerland) · 2023Review
- Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging.European journal of nuclear medicine and molecular imaging · 2023Article
- Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging.NPJ digital medicine · 2023Article
- Integration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Preparing for the Artificial Intelligence Revolution in Nuclear Cardiology.Nuclear medicine and molecular imaging · 2023Review
- Deep Learning-Based Attenuation Correction Improves Diagnostic Accuracy of Cardiac SPECT.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning.JACC. Cardiovascular imaging · 2023Article
- Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- Machine learning to predict abnormal myocardial perfusion from pre-test features.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Article
- Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry.Cardiovascular research · 2022Article
- Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease.JACC. Cardiovascular imaging · 2022Article
- Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry.Computers in biology and medicine · 2022Article
Corrections and comments
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Authors and funding
19 authors at 9 institutions in 5 countries.
Funding
Abstract
aimsSingle-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) stress-only protocols reduce radiation exposure and cost but require clinicians to make immediate decisions regarding rest scan cancellation. We developed a machine learning (ML) approach for automatic rest scan cancellation and evaluated its prognostic safety. METHODS AND
resultsIn total, 20 414 patients from a solid-state SPECT MPI international multicentre registry with clinical data and follow-up for major adverse cardiac events (MACE) were used to train ML for MACE prediction as a continuous probability (ML score), using 10-fold repeated hold-out testing to separate test from training data. Three ML score thresholds (ML1, ML2, and ML3) were derived by matching the cancellation rates achieved by physician interpretation and two clinical selection rules. Annual MACE rates were compared in patients selected for rest scan cancellation between approaches. Patients selected for rest scan cancellation with ML had lower annualized MACE rates than those selected by physician interpretation or clinical selection rules (ML1 vs. physician interpretation: 1.4 ± 0.1% vs. 2.1 ± 0.1%; ML2 vs. clinical selection: 1.5 ± 0.1% vs. 2.0 ± 0.1%; ML3 vs. stringent clinical selection: 0.6 ± 0.1% vs. 1.7 ± 0.1%, all P < 0.0001) at matched cancellation rates (60 ± 0.7, 64 ± 0.7, and 30 ± 0.6%). Annualized all-cause mortality rates in populations recommended for rest cancellation by physician interpretation, clinical selection approaches were higher (1.3%, 1.2%, and 1.0%, respectively) compared with corresponding ML thresholds (0.6%, 0.6%, and 0.2%).
conclusionML, using clinical and stress imaging data, can be used to automatically recommend cancellation of rest SPECT MPI scans, while ensuring higher prognostic safety than current clinical approaches.
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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.