ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2010
An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT.
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Applications and implementation of generative artificial intelligence in cardiovascular imaging with a focus on ethical and legal considerations: what cardiovascular imagers need to know!BJR artificial intelligence · 2024Review
- Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study.Scientific reports · 2023Article
- Content-based image retrieval for the diagnosis of myocardial perfusion imaging using a deep convolutional autoencoder.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Machine Learning for Multi-Vessel Coronary Artery Disease Prediction on Electrocardiogram Gated Single-Photon Emission Computed Tomography.Annals of nuclear cardiology · 2023Article
- Modeling the diagnosis of coronary artery disease by discriminant analysis and logistic regression: a cross-sectional study.BMC medical informatics and decision making · 2022Article
- Current and Future Applications of Artificial Intelligence in Coronary Artery Disease.Healthcare (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
- Deep learning applications in myocardial perfusion imaging, a systematic review and meta-analysis.Informatics in medicine unlocked · 2022Article
- Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble.Frontiers in cardiovascular medicine · 2021Review
- Image-Based Cardiac Diagnosis With Machine Learning: A Review.Frontiers in cardiovascular medicine · 2020Review
- Machine learning for nuclear cardiology: The way forward.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2019Article
- Predicting coronary artery disease: a comparison between two data mining algorithms.BMC public health · 2019Article
- Ability of artificial intelligence to diagnose coronary artery stenosis using hybrid images of coronary computed tomography angiography and myocardial perfusion SPECT.European journal of hybrid imaging · 2019Article
- Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study.European journal of nuclear medicine and molecular imaging · 2017Article
- Computerized decision making in myocardial perfusion SPECT: The new era in nuclear cardiology?Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2015Article
- Computer-aided diagnosis system outperforms scoring analysis in myocardial perfusion imaging.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2014Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe purpose of this study is to develop and analyze an open-source artificial intelligence program built on artificial neural networks that can participate in and support the decision making of nuclear medicine physicians in detecting coronary artery disease from myocardial perfusion SPECT (MPS). METHODS AND
resultsTwo hundred and forty-three patients, who had MPS and coronary angiography within three months, were selected to train neural networks. Six nuclear medicine residents, one experienced nuclear medicine physician, and neural networks evaluated images of 65 patients for presence of coronary artery stenosis. Area under the curve (AUC) of receiver operating characteristics analysis for networks and expert was .74 and .84, respectively. The AUC of the other physicians ranged from .67 to .80. There were no significant differences between expert, neural networks, and standard quantitative values, summed stress score and total stress defect extent.
conclusionsThe open-source neural networks developed in this study may provide a framework for further testing, development, and integration of artificial intelligence into nuclear cardiology environment.
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Registered trials
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.