9:00 AM - 9:20 AM
[2E1-GS-13-01] Classification of Functionally Significant Coronary Artery Stenosis using LSTM
Keywords:LSTM, CNN, FFR, Automated diagnosis support system
Coronary artery stenosis is one of the main causes of heart disease, which is the second leading cause of death in Japan. CT is used for detection of coronary artery stenosis, and coronary angiography (CAG) is necessary when coronary artery stenosis is suspected. However, CAG is invasive and it is convenient to detect functionally significant coronary artery stenosis on CT, because unnecessary CAG can be avoided.Therefore, we propose a method to identify functionally significant coronary artery stenosis, using a deep learning model of CT images. Our model is based on convolutional neural networks and LSTM. Consecutive 150 short axial images of each coronary artery are used. We regard them as time series data and fractional flow reserve (FFR), which is used as an index of functionally significant stenosis, was evaluated.The results show that FFR can be estimated only from CT images although the recognition accuracy is not sufficient.
Authentication for paper PDF access
A password is required to view paper PDFs. If you are a registered participant, please log on the site from Participant Log In.
You could view the PDF with entering the PDF viewing password bellow.