ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2022
A machine learning-based approach to directly compare the diagnostic accuracy of myocardial perfusion imaging by conventional and cadmium-zinc telluride SPECT.
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Radiopharmaceuticals for PET and SPECT Imaging: A Literature Review over the Last Decade.International journal of molecular sciences · 2022Pooled it
- Elimination of artifacts caused by residual radiopharmaceutical activity in injection site in myocardial perfusion imaging on Discovery NM 530c semiconductor gamma camera.EJNMMI research · 2025Article
- Immediate recruitment of dormant coronary collaterals can provide more than half of normal resting perfusion during coronary occlusion in patients with coronary artery disease.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Review of cardiovascular imaging in the Journal of Nuclear Cardiology 2022: single photon emission computed tomography.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Review
- Regional myocardial perfusion imaging in predicting vessel-related outcome: interplay between the perfusion results and angiographic findings.European journal of nuclear medicine and molecular imaging · 2022Article
- Prone-only SPECT myocardial perfusion imaging: An alternative standard in clinical practice?Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Article
- Assessment of the routine reporting of very low-dose exercise-first myocardial perfusion SPECT from a large-scale real-world cohort and correlation with the subsequent reporting of coronary stenosis at angiography.European journal of nuclear medicine and molecular imaging · 2022Article
- Exploration of the efficacy of radiomics applied to left ventricular tomograms obtained from D-SPECT MPI for the auxiliary diagnosis of myocardial ischemia in CAD.The international journal of cardiovascular imaging · 2022Article
- Deep learning applications in myocardial perfusion imaging, a systematic review and meta-analysis.Informatics in medicine unlocked · 2022Article
- Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure.Bioengineering (Basel, Switzerland) · 2021Article
- Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps.Frontiers in cardiovascular medicine · 2021Article
- Comparing the Prognostic Value of Stress Myocardial Perfusion Imaging by Conventional and Cadmium-Zinc Telluride Single-Photon Emission Computed Tomography through a Machine Learning Approach.Computational and mathematical methods in medicine · 2021Article
- Machine learning to predict mortality after rehabilitation among patients with severe stroke.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
15 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe evaluated the performance of conventional (C) single-photon emission computed tomography (SPECT) and cadmium-zinc-telluride (CZT)-SPECT in a large cohort of patients with suspected or known coronary artery disease (CAD) and compared the diagnostic accuracy of the two systems using machine learning (ML) algorithms. METHODS AND
resultsA total of 517 consecutive patients underwent stress myocardial perfusion imaging (MPI) by both C-SPECT and CZT-SPECT. In the overall population, an excellent correlation between stress MPI data and left ventricular (LV) functional parameters measured by C-SPECT and by CZT-SPECT was observed (all P < .001). ML analysis performed through the implementation of random forest (RF) and k-nearest neighbors (NN) algorithms proved that CZT-SPECT has greater accuracy than C-SPECT in detecting CAD. For both algorithms, the sensitivity of CZT-SPECT (96% for RF and 60% for k-NN) was greater than that of C-SPECT (88% for RF and 53% for k-NN).
conclusionsMPI data and LV functional parameters obtained by CZT-SPECT are highly reproducible and provide good correlation with those obtained by C-SPECT. ML approach showed that the accuracy and sensitivity of CZT-SPECT is greater than C-SPECT in detecting CAD.
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