ArticleEuropean journal of nuclear medicine and molecular imaging2021
Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning.
Article in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 43 citations in OpenAlex.
- A Prototype-Guided 3D Deep Learning Framework for Myocardial Perfusion Scintigraphy Segmentation.Journal of clinical medicine · 2026Review
- Decoding the Myocardium: Tracer-Aware Deep Learning for Patient-Level Classification in Stress-Rest SPECT Myocardial Perfusion Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- Scatter correction for self-collimating SPECT using a 3D U-Net framework.American journal of nuclear medicine and molecular imaging · 2026Article
- Artificial Intelligence in Nuclear Cardiology.Journal of clinical medicine · 2025Review
- 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 in Nuclear Cardiac Imaging: Novel Advances, Emerging Techniques, and Recent Clinical Trials.Journal of clinical medicine · 2025Review
- Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study.PloS one · 2025Article
- The role of deep learning in myocardial perfusion imaging for diagnosis and prognosis: A systematic review.iScience · 2024Review
- A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging.Scientific reports · 2024Article
- Predicting long-term time to cardiovascular incidents using myocardial perfusion imaging and deep convolutional neural networks.Scientific reports · 2024Article
- SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach.Acta medica Philippina · 2024Article
- Deep learning prediction of quantitative coronary angiography values using myocardial perfusion images with a CZT camera.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies.EJNMMI physics · 2023Review
- Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images.Journal of clinical medicine · 2022Article
- Prediction of multivessel coronary artery disease and candidates for stress-only imaging using multivariable models with myocardial perfusion imaging.Annals of nuclear medicine · 2022Article
- A Narrative Review of the Classical and Modern Diagnostic Methods of the No-Reflow Phenomenon.Diagnostics (Basel, Switzerland) · 2022Review
- Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review.Frontiers in cardiovascular medicine · 2022Article
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeDeep convolutional neural networks (CNN) for single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been used to improve the diagnostic accuracy of coronary artery disease (CAD). This study was to design and evaluate a deep learning (DL) approach to automatic diagnosis of myocardial perfusion abnormalities from stress-only MPI.
methodsThe new DL approach developed for this study was compared to a conventional quantitative perfusion defect size (DS) method. A total of 37,243 patients (51.5% males) undergone stress
resultsThe AUC value resulted from the DL method was significantly higher than that from the DS method (0.872 ± 0.002 vs. 0.838 ± 0.003, p < 0.01). Across the eight sub-groups, the DL method provided more consistent AUC values in terms of smaller standard deviation and higher diagnostic accuracy and specificity, but slightly lower sensitivity than the DS method (AUC: 0.865 ± 0.010 vs. 0.838 ± 0.019, Accuracy: 82.7% ± 2.5% vs. 78.5% ± 3.6%, Specificity: 84.9% ± 3.7% vs. 77.5% ± 6.5%, Sensitivity: 74.4% ± 4.2% vs. 79.8% ± 5.8%).
conclusionsThe incorporation of deep learning for stress-only MPI has a considerable potential to improve the diagnostic accuracy and consistency in the detection of myocardial perfusion abnormalities.
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