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Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging
Kenichi Nakajima1, Shinro Matsuo, Hiroshi Wakabayashi
1Department of Nuclear Medicine, Kanazawa University Hospital.
Insights
An artificial neural network (ANN) shows comparable diagnostic accuracy to conventional methods for evaluating coronary artery disease (CAD) using myocardial perfusion imaging (MPI). This promising ANN approach offers a new perspective for abnormality assessment in MPI.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Coronary artery disease (CAD) diagnosis relies on myocardial perfusion imaging (MPI).
- Conventional visual and quantitative methods are used for MPI analysis.
- Novel computational approaches are being explored to enhance diagnostic capabilities.
Purpose of the Study:
- To apply an artificial neural network (ANN) in patients with CAD.
- To assess the diagnostic ability of ANN in MPI.
- To compare ANN performance against conventional MPI analysis methods.
Main Methods:
- 106 patients with CAD underwent MPI.
- An artificial neural network (ANN) was developed to detect stress defects and ischemia.
- Consensus diagnosis based on expert interpretation and coronary stenosis served as the gold standard.
- Receiver-operating characteristic (ROC) curve analysis was performed.
Main Results:
- The ANN demonstrated higher values in stress-defect and ischemia groups compared to no-defect/no-ischemia groups (P<0.0001).
- ANN diagnostic accuracy was comparable to conventional scoring methods for both stress defect (0.971 vs. 0.980) and ischemia (0.882 vs. 0.937).
- A non-linear relationship was observed between ANN values and defect scores.
Conclusions:
- The ANN exhibits similar diagnostic ability to conventional scoring methods in MPI for CAD.
- ANN provides a novel perspective for interpreting MPI abnormalities.
- ANN is a promising tool for evaluating abnormalities in myocardial perfusion imaging.
Background:
The purpose of this study was to apply an artificial neural network (ANN) in patients with coronary artery disease (CAD) and to characterize its diagnostic ability compared with conventional visual and quantitative methods in myocardial perfusion imaging (MPI).
Methods And Results:
A total of 106 patients with CAD were studied with MPI, including multiple vessel disease (49%), history of myocardial infarction (27%) and coronary intervention (30%). The ANN detected abnormal areas with a probability of stress defect and ischemia. The consensus diagnosis based on expert interpretation and coronary stenosis was used as the gold standard. The left ventricular ANN value was higher in the stress-defect group than in the no-defect group (0.92±0.11 vs. 0.25±0.32, P<0.0001) and higher in the ischemia group than in the no-ischemia group (0.70±0.40 vs. 0.004±0.032, P<0.0001). Receiver-operating characteristics curve analysis showed comparable diagnostic accuracy between ANN and the scoring methods (0.971 vs. 0.980 for stress defect, and 0.882 vs. 0.937 for ischemia, both P=NS). The relationship between the ANN and defect scores was non-linear, with the ANN rapidly increased in ranges of summed stress score of 2-7 and summed defect score of 2-4.
Conclusions:
Although the diagnostic ability of ANN was similar to that of conventional scoring methods, the ANN could provide a different viewpoint for judging abnormality, and thus is a promising method for evaluating abnormality in MPI.
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