ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2019
Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning.
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed.
- 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
- T1 Mapping–derived Predictors of Cardiac Remodeling and Fibrosis in Athletes using Advanced Machine Learning TechniquesCurrent medical imaging · 2026Article
- Predicting cardiac resynchronization therapy response: development and validation of a single photon emission computed tomography-based nomogram.Quantitative imaging in medicine and surgery · 2025Article
- Expanding interpretability through complexity reduction in machine learning-based modelling of cardiovascular disease: A myocardial perfusion imaging PET/CT prognostic study.European journal of clinical investigation · 2025Article
- Artificial Intelligence in Cardiovascular Imaging and Interventional Cardiology: Emerging Trends and Clinical Implications.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Artificial Intelligence in Imaging for Personalized Management of Coronary Artery Disease.Journal of clinical medicine · 2025Review
- Precision cardiovascular medicine: shifting the innovation paradigm.Frontiers in science · 2025Article
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- Heart disease prediction with a feature-sensitized interpretable framework for the Internet of Medical Things sensors.Frontiers in digital health · 2025Article
- Application of the U-Net Deep Learning Model for Segmenting Single-Photon Emission Computed Tomography Myocardial Perfusion Images.Diagnostics (Basel, Switzerland) · 2024Article
- A novel algorithm developed using machine learning and a J-ACCESS database can estimate defect scores from myocardial perfusion single-photon emission tomography images.Annals of nuclear medicine · 2024Article
- Machine learning for prognostic prediction in coronary artery disease with SPECT data: a systematic review and meta-analysis.EJNMMI research · 2024Review
- Incremental prognostic value of downstream positron emission tomography perfusion imaging after coronary computed tomography angiography: a study using machine learning.European heart journal. Cardiovascular Imaging · 2024Article
- A machine learning method integrating ECG and gated SPECT for cardiac resynchronization therapy decision support.European journal of nuclear medicine and molecular imaging · 2023Article
- Preparing for the Artificial Intelligence Revolution in Nuclear Cardiology.Nuclear medicine and molecular imaging · 2023Review
- Artificial Intelligence in Resuscitation: A Scoping Review.Journal of clinical medicine · 2023Article
- "Global" cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation inJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Article
- Non-Fatal Drowning Risk Prediction Based on Stacking Ensemble Algorithm.Children (Basel, Switzerland) · 2022Article
- Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry.Cardiovascular research · 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
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6 authors.
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Abstract
backgroundWe developed machine-learning (ML) models to estimate a patient's risk of cardiac death based on adenosine myocardial perfusion SPECT (MPS) and associated clinical data, and compared their performance to baseline logistic regression (LR). We demonstrated an approach to visually convey the reasoning behind a patient's risk to provide insight to clinicians beyond that of a "black box."
methodsWe trained multiple models using 122 potential clinical predictors (features) for 8321 patients, including 551 cases of subsequent cardiac death. Accuracy was measured by area under the ROC curve (AUC), computed within a cross-validation framework. We developed a method to display the model's rationale to facilitate clinical interpretation.
resultsThe baseline LR (AUC = 0.76; 14 features) was outperformed by all other methods. A least absolute shrinkage and selection operator (LASSO) model (AUC = 0.77; p = .045; 6 features) required the fewest features. A support vector machine (SVM) model (AUC = 0.83; p < .0001; 49 features) provided the highest accuracy.
conclusionsLASSO outperformed LR in both accuracy and simplicity (number of features), with SVM yielding best AUC for prediction of cardiac death in patients undergoing MPS. Combined with presenting the reasoning behind the risk scores, our results suggest that ML can be more effective than LR for this application.
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