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Annals of Nuclear Medicine
Published in: Annals of Nuclear Medicine 5/2018

Open Access 01-06-2018 | Original Article

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

Authors: Kenichi Nakajima, Koichi Okuda, Satoru Watanabe, Shinro Matsuo, Seigo Kinuya, Karin Toth, Lars Edenbrandt

Published in: Annals of Nuclear Medicine | Issue 5/2018

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Abstract

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.
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Metadata
Title
Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database
Authors
Kenichi Nakajima
Koichi Okuda
Satoru Watanabe
Shinro Matsuo
Seigo Kinuya
Karin Toth
Lars Edenbrandt
Publication date
01-06-2018
Publisher
Springer Japan
Published in
Annals of Nuclear Medicine / Issue 5/2018
Print ISSN: 0914-7187
Electronic ISSN: 1864-6433
DOI
https://doi.org/10.1007/s12149-018-1247-y

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