Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database
Autor: | Satoru Watanabe, Lars Edenbrandt, Seigo Kinuya, Karin Toth, Kenichi Nakajima, Shinro Matsuo, Koichi Okuda |
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Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
Male
Artificial intelligence Myocardial ischemia Ischemia Myocardial Ischemia 030204 cardiovascular system & hematology computer.software_genre Coronary Angiography Coronary artery disease 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Japan Image Interpretation Computer-Assisted medicine Humans Radiology Nuclear Medicine and imaging In patient Myocardial infarction Cardiac Surgical Procedures Aged Tomography Emission-Computed Single-Photon Database business.industry Myocardial perfusion imaging Endovascular Procedures Nuclear cardiology Heart General Medicine Gold standard (test) Organotechnetium Compounds medicine.disease Coronary revascularization Quality Improvement Stenosis Databases as Topic ROC Curve Area Under Curve Original Article Female Neural Networks Computer Radiopharmaceuticals business computer Area under the roc curve |
Zdroj: | Annals of Nuclear Medicine |
ISSN: | 1864-6433 0914-7187 |
Popis: | Purpose An 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. Methods We 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 99mTc-methoxyisobutylisonitrile myocardial perfusion images. The ANN probabilities (from 0.0 to 1.0) of stress defects and ischemia were calculated in candidate regions of abnormalities. The diagnostic accuracy was compared using receiver-operating characteristics (ROC) analysis and the calculated area under the ROC curve (AUC) using expert interpretation as the gold standard. Results Although 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. Conclusion The diagnostic ability of the ANN version 1.1 was improved by retraining using the Japanese database, particularly for identifying ischemia. |
Databáze: | OpenAIRE |
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