## 4 Discussion and Conclusions  
According to the guideline of American Heart Association (AHA), myocardial perfusion imaging (MPI) is an important reference for determining the necessity of performing invasive cardiac catheterization. With the breakthrough of technology and hardware in recent years, artificial intelligence technology has been gradually applied to assist in the interpretation of various medical images. However, there still lacks enough attention on the application of machine learning in the field of nuclear medicine imaging. This research project aims to apply machine learning to construct a nuclear medicine cardiac perfusion imaging AI diagnosis aid system that helps to enhance the efficiency of clinical diagnosis of physicians [18]. The model of automatic MPI diagnosis aids system in this project plan to use the huge amount of MPI data gathered from 2007 to 2016 as an input for the training model. Both supervised learning and non-supervised learning techniques in machine learning would be applied in model building. We use MPI data from 2017 as criteria for evaluating the efficacy of the model. When a new MPI image being input, the model will classify the image into one of three categories--normal, abnormal, or unpredictable. If the MPI data is being classified as unpredictable, the system would war physicians and  
Figure 8: Grad cam map for normal images.  
Figure 9: Grad cam map for abnormal images.  
physicians would do the diagnosis. Otherwise, the physicians only need to do a double check for those MPI data that have been classified into normal and abnormal categories.  
In this study, we collected the dataset accumulated from Kaohsiung Chang Gung Memorial Hospital. All images are original images without artificial modification so that there is no distortion due to human factors. The performance of the proposed classification method has achieved prediction accuracy, sensitivity, and specificity at the level of 87.64%, 81.58%, and 92.16%. These results can be implemented in computer-aided systems for heart diseases in future studies. The system will provide useful reference information for physicians in clinical practice, and physicians will continue to feedback the interpretation information to modify the prediction model to improve the accuracy of the examination report and expand to other image prediction applications in the future. It is expected to be used in other hospitals of the Chang Gung Medical Foundation in the future, and there are plans to cooperate with other hospitals to expand the model sample and strengthen the predictive ability. It is expected to become a leader in nuclear medicine research.  
All authors contribute to this study. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the grants CMRPG8K0121 from Chang Gung Memorial Hospital and the grant MOST 107-2118-M-009-006-MY3 (H.H.-S.L.) from Ministry of Science and Technology, Taiwan, ROC. This research was also partly supported in part by the Higher Education Sprout Project (H.H.-S.L.) from the National Chiao Tung University and Ministry of Education (MOE), Taiwan, ROC.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Chang Gung Medical Foundation (protocol code 201801122B0C501 and 22 August 2019 of approval).  
The images we use are taken from the human body. We have research consent. Informed consent was obtained from all subjects involved in the study.  
The data that support the findings of this study are available on request from the authors in Kaohsiung Chang Gung Memorial Hospital, [[PERSON], [PERSON]]. The data are not publicly available due to restrictions because their containing information that could compromise the privacy of research participants.  
The funders had no role in the design of the study.