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 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.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.
Highlights
The diagnostic ability of artificial neural network (ANN), which is a type of artificial intelligence, has been examined from the viewpoint of nuclear cardiology applications [1, 2]
That validation study indicated that the ANN had good diagnostic ability comparable to nuclear cardiology expert interpretation, as the area under the receiver-operating characteristics (ROC) curve (AUC) was 0.92
A larger area with a probability of 0.88 and an extent of 9% was identified in the anterior wall, but a small basal region that was selected as candidate was determined as insignificant
Summary
The diagnostic ability of artificial neural network (ANN), which is a type of artificial intelligence, has been examined from the viewpoint of nuclear cardiology applications [1, 2]. That ANN was trained to detect myocardial stress perfusion defects and induced ischemia on a Swedish database, but its diagnostic ability was comparable to that of expert interpretation for Japanese patients. Thereafter, the diagnostic ability was further improved by training the ANN on a Japanese multicenter database (n = 1,001) using 99mTcmethoxyisobutylisonitrile (MIBI) MPI [4]. That validation study indicated that the ANN had good diagnostic ability comparable to nuclear cardiology expert interpretation, as the area under the receiver-operating characteristics (ROC) curve (AUC) was 0.92. The present study aimed to determine whether the diagnostic ability of version 1.1 trained on a Japanese database was improved over the original version by comparison with the same population that was used before [3]
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