ArticleFrontiers in cardiovascular medicine2021
Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps.
Article in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- A Novel Quantitative Analysis in Myocardial Perfusion Imaging: How does it compare to the "Golden Eye"?Zeitschrift fur medizinische Physik · 2026Article
- A Prototype-Guided 3D Deep Learning Framework for Myocardial Perfusion Scintigraphy Segmentation.Journal of clinical medicine · 2026Review
- Integrative Bioinformatics and Experimental Validation Identify LOX as a Glycolysis-Related Biomarker in Myocardial Ischemia-Reperfusion Injury.Cardiovascular toxicology · 2026Article
- Artificial Intelligence in Nuclear Cardiology.Journal of clinical medicine · 2025Review
- A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging.Scientific reports · 2024Article
- Detecting obstructive coronary artery disease with machine learning: rest-only gated single photon emission computed tomography myocardial perfusion imaging combined with coronary artery calcium score and cardiovascular risk factors.Quantitative imaging in medicine and surgery · 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
- Machine learning-based predictive modeling of depression in hypertensive populations.PloS one · 2022Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Myocardial perfusion imaging (MPI) plays an important role in patients with suspected and documented coronary artery disease (CAD). Machine Learning (ML) algorithms have been developed for many medical applications with excellent performance. This study used ML algorithms to discern normal and abnormal gated Single Photon Emission Computed Tomography (SPECT) images. We analyzed one thousand and seven polar maps from a database of patients referred to a university hospital for clinically indicated MPI between January 2016 and December 2018. These studies were reported and evaluated by two different expert readers. The image features were extracted from a specific type of polar map segmentation based on horizontal and vertical slices. A senior expert reading was the comparator (gold standard). We used cross-validation to divide the dataset into training and testing subsets, using data augmentation in the training set, and evaluated 04 ML models. All models had accuracy >90% and area under the receiver operating characteristics curve (AUC) >0.80 except for Adaptive Boosting (AUC = 0.77), while all precision and sensitivity obtained were >96 and 92%, respectively. Random Forest had the best performance (AUC: 0.853; accuracy: 0,938; precision: 0.968; sensitivity: 0.963). ML algorithms performed very well in image classification. These models were capable of distinguishing polar maps remarkably into normal and abnormal.
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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.