Background: The World Health Organization reported that cardiovascular disease is the most common cause of death worldwide. On average, one person dies of heart disease every 26 min worldwide. Deep learning approaches are characterized by the appropriate combination of abnormal features based on numerous annotated images. The constructed convolutional neural network (CNN) model can identify normal states of reversible and irreversible myocardial defects and alert physicians for further diagnosis.
Methods: Cadmium zinc telluride single-photon emission computed tomography myocardial perfusion resting-state images were collected at Chang Gung Memorial Hospital, Kaohsiung Medical Center, Kaohsiung, Taiwan, and were analyzed with a deep learning convolutional neural network to classify myocardial perfusion images for coronary heart diseases.
Results: In these grey-scale images, the heart blood flow distribution was the most crucial feature. The deep learning technique of You Only Look Once was used to determine the myocardial defect area and crop the images. After surrounding noise had been eliminated, a three-dimensional CNN model was used to identify patients with coronary heart diseases. The prediction area under the curve, accuracy, sensitivity, and specificity was 90.97, 87.08, 86.49, and 87.41%, respectively.
Conclusion: Our prototype system can considerably reduce the time required for image interpretation and improve the quality of medical care. It can assist clinical experts by offering accurate coronary heart disease diagnosis in practice.
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