ArticleAnnals of nuclear medicine2018
Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database.
Article in Annals of nuclear medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- 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
- Qualitative stress perfusion American Heart Association plot and outcome prediction using artificial intelligence.Informatics in medicine unlocked · 2024Article
- Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review.Applied bionics and biomechanics · 2024Article
- J-ACCESS investigation and nuclear cardiology in Japan: implications for heart failure.Annals of nuclear medicine · 2023Review
- Diagnosis of Parkinson syndrome and Lewy-body disease usingAnnals of nuclear 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
- Deep learning applications in myocardial perfusion imaging, a systematic review and meta-analysis.Informatics in medicine unlocked · 2022Article
- Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review.Frontiers in cardiovascular medicine · 2022Article
- Usefulness of an artificial neural network for a beginner to achieve similar interpretations to an expert when examining myocardial perfusion images.The international journal of cardiovascular imaging · 2021Article
- A review of the application of machine learning in molecular imaging.Annals of translational medicine · 2021Review
- Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications.Diagnostics (Basel, Switzerland) · 2021Review
- Image-Based Cardiac Diagnosis With Machine Learning: A Review.Frontiers in cardiovascular medicine · 2020Review
- Quantitative imaging biomarkers in nuclear medicine: from SUV to image mining studies. Highlights from annals of nuclear medicine 2018.European journal of nuclear medicine and molecular imaging · 2019Review
- Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist.Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2019Review
- 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
- Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine.European radiology experimental · 2018Review
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7 authors.
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Abstract
purposeAn artificial neural network (ANN) has been applied to detect myocardial perfusion defects and ischemia. The present study compares the diagnostic accuracy of a more recent ANN version (1.1) with the initial version 1.0.
methodsWe examined 106 patients (age, 77 ± 10 years) with coronary angiographic findings, comprising multi-vessel disease (≥ 50% stenosis) (52%) or old myocardial infarction (27%), or who had undergone coronary revascularization (30%). The ANN versions 1.0 and 1.1 were trained in Sweden (n = 1051) and Japan (n = 1001), respectively, using
resultsAlthough the AUC for stress defects was 0.95 and 0.93 (p = 0.27) for versions 1.1 and 1.0, respectively, that for detecting ischemia was significantly improved in version 1.1 (p = 0.0055): AUC 0.96 for version 1.1 (sensitivity 87%, specificity 96%) vs. 0.89 for version 1.0 (sensitivity 78%, specificity 97%). The improvement in the AUC shown by version 1.1 was also significant for patients with neither coronary revascularization nor old myocardial infarction (p = 0.0093): AUC = 0.98 for version 1.1 (sensitivity 88%, specificity 100%) and 0.88 for version 1.0 (sensitivity 76%, specificity 100%). Intermediate ANN probability between 0.1 and 0.7 was more often calculated by version 1.1 compared with version 1.0, which contributed to the improved diagnostic accuracy. The diagnostic accuracy of the new version was also improved in patients with either single-vessel disease or no stenosis (n = 47; AUC, 0.81 vs. 0.66 vs. p = 0.0060) when coronary stenosis was used as a gold standard.
conclusionThe diagnostic ability of the ANN version 1.1 was improved by retraining using the Japanese database, particularly for identifying ischemia.
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