Value of exercise data for the interpretation of myocardial perfusion SPECT

An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT

BACKGROUND

The purpose of this study is to develop and analyze an open-source artificial intelligence program built on artificial neural networks that can participate in and support the decision making of nuclear medicine physicians in detecting coronary artery disease from myocardial perfusion SPECT (MPS).

METHODS AND RESULTS

Two hundred and forty-three patients, who had MPS and coronary angiography within three months, were selected to train neural networks. Six nuclear medicine residents, one experienced nuclear medicine physician, and neural networks evaluated images of 65 patients for presence of coronary artery stenosis. Area under the curve (AUC) of receiver operating characteristics analysis for networks and expert was .74 and .84, respectively. The AUC of the other physicians ranged from .67 to .80. There were no significant differences between expert, neural networks, and standard quantitative values, summed stress score and total stress defect extent.

CONCLUSIONS

The open-source neural networks developed in this study may provide a framework for further testing, development, and integration of artificial intelligence into nuclear cardiology environment.

Integration of clinical and imaging data to predict the presence of coronary artery disease with the use of neural networks

AIM

To establish proof of the principle that a computer-based neural network method can be employed that will enhance diagnostic accuracy vis-a-vis image analysis alone in the interpretation of treadmill exercise tests performed in conjunction with myocardial perfusion imaging.

MATERIALS AND METHODS

One-hundred-and-two patients underwent myocardial perfusion imaging in association with the standard Bruce protocol. Twenty objective parameters describing each patient's exercise physiology, general clinical status and image appearance were used to train an artificial neural network. Classification accuracy of the neural network and clinical interpretation was determined by coronary angiography. We evaluated the ability of the neural network to integrate clinical, exercise and imaging data to determine the likelihood of coronary artery disease and compared these results with an optimized method of clinical image interpretation, which made use of all available clinical, angiographic and stress test data.

RESULTS

The artificial neural network had a sensitivity of 88% and a specificity of 65% for detection of ischemic heart disease and was comparable to that of the optimized clinical method (sensitivity 80%, specificity 69%). Incorporation of clinical and exercise data significantly improved the predictive accuracy of the network compared to a network based on image data alone (P<0.05).

CONCLUSION

The results show a computer-based neural network can perform as well as expert readers working under optimal conditions including full knowledge of the patient's clinical, prior angiographic and stress test data. Thus, the method is promising as a diagnostic aid to the recognition of ischemic heart disease in the clinical setting of treadmill exercise testing in conjunction with myocardial perfusion imaging.