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
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
Externí odkaz: