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Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database

Nakajima, K. (författare)
Okuda, K. (författare)
Watanabe, S. (författare)
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Matsuo, S. (författare)
Kinuya, S. (författare)
Toth, K. (författare)
Edenbrandt, Lars, 1957 (författare)
Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
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 (creator_code:org_t)
2018-03-07
2018
Engelska.
Ingår i: Annals of Nuclear Medicine. - : Springer Science and Business Media LLC. - 0914-7187 .- 1864-6433. ; 32:5, s. 303-310
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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. 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 Tc-99m-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. 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. The diagnostic ability of the ANN version 1.1 was improved by retraining using the Japanese database, particularly for identifying ischemia.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Kardiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)

Nyckelord

Nuclear cardiology
Artificial intelligence
Myocardial perfusion imaging
Coronary artery disease
perfusion spect
diagnostic performance
cardiology
quantification
quantitation
system
Radiology
Nuclear Medicine & Medical Imaging

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