FFR-Estimation

Non-Invasive Fractional Flow Reserve Estimation using Deep Learning on Intermediate Left Anterior Descending Coronary Artery Lesion Angiography Images

This repository contains an end-to-end deep learning model for estimating the value of fractional flow reserve (FFR) using angiography images to classify left anterior descending (LAD) branch angiography images with average stenosis between 50% and 70% into two categories: FFR>80 and FFR≤80. In this study, 3,625 images were extracted from 41 patients’ angiography films. Ten pre-trained convolutional neural networks (CNN), including DenseNet-121, InceptionResNetV2, VGG-16, VGG-19, ResNet50V2, Xception, MobileNetV3Large, InceptionV3, DenseNet-201, and DenseNet-169, were used to extract the features of images. DenseNet169 indicated higher performance compared to other networks. Accuracy, Sensitivity, Specificity, Precision, and F1-score of the proposed DenseNet-169 network were 0.81, 0.86, 0.75, 0.82, and 0.84, respectively. The deep learning-based method proposed in this study can non-invasively and consistently estimate FFR from angiographic images, offering significant clinical potential for diagnosing and treating coronary artery disease by combining anatomical and physiological parameters.

Model architecture

Inference

You may use FFR-Estimation.ipynb to train and test the model, inference is as simple as:

# Example
classes = Classifier.predict([Test_img])

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Paper / Data / Pre-trained model availability:

• Due to the policies and guidelines of Shahid Beheshti University of Medical Science, data is not allowed for publication.

• The model is not publicly available at this moment due to Git LFS limitations.

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Condition and terms to use any sources of this project (Codes, Datasets, etc.):

1. Please cite the following paper:

Arefinia, F, Aria, M, Rabiei, R, Hosseini, A, Ghaemian, A, Roshanpoor, A.
Non-Invasive Fractional Flow Reserve Estimation using Deep Learning on Intermediate Left Anterior Descending Coronary Artery Lesion Angiography Images.
J. 2023; 37: 5113- 5133. doi:X

2. Please do not distribute the database or source codes to others without author authorization. Authors’ Email: mehrad.aria[at]shirazu.ac.ir (M. Aria).