Deep learning analysis in coronary computed tomographic angiography imaging for the assessment of patients with coronary artery stenosis.
Convolutional neural network
Coronary atherosclerotic stenosis
Coronary computed tomographic angiography
Deep learning
Journal
Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513
Informations de publication
Date de publication:
Nov 2020
Nov 2020
Historique:
received:
18
09
2019
accepted:
04
07
2020
pubmed:
28
7
2020
medline:
15
5
2021
entrez:
27
7
2020
Statut:
ppublish
Résumé
Recently, deep convolutional neural network has significantly improved image classification and image segmentation. If coronary artery disease (CAD) can be diagnosed through machine learning and deep learning, it will significantly reduce the burdens of the doctors and accelerate the critical patient diagnoses. The purpose of the study is to assess the practicability of utilizing deep learning approaches to process coronary computed tomographic angiography (CCTA) imaging (termed CCTA-artificial intelligence, CCTA-AI) in coronary artery stenosis. A CCTA reconstruction pipeline was built by utilizing deep learning and transfer learning approaches to generate auto-reconstructed CCTA images based on a series of two-dimensional (2D) CT images. 150 patients who underwent successively CCTA and digital subtraction angiography (DSA) from June 2017 to December 2017 were retrospectively analyzed. The dataset was divided into two parts comprising training dataset and testing dataset. The training dataset included the CCTA images of 100 patients which are trained using convolutional neural networks (CNN) in order to further identify various plaque classifications and coronary stenosis. The other 50 CAD patients acted as testing dataset that is evaluated by comparing the auto-reconstructed CCTA images with traditional CCTA images on the condition that DSA images are regarded as the reference method. Receiver operating characteristic (ROC) analysis was used for statistical analysis to compare CCTA-AI with DSA and traditional CCTA in the aspect of detecting coronary stenosis and plaque features. AI significantly reduces time for post-processing and diagnosis comparing to the traditional methods. In identifying various degrees of coronary stenosis, the diagnostic accuracy of CCTA-AI is better than traditional CCTA (AUC The proposed CCTA-AI allows the generation of auto-reconstructed CCTA images from a series of 2D CT images. This approach is relatively accurate for detecting ≥50% stenosis and analyzing plaque features compared to traditional CCTA.
Sections du résumé
BACKGROUND AND OBJECTIVE
OBJECTIVE
Recently, deep convolutional neural network has significantly improved image classification and image segmentation. If coronary artery disease (CAD) can be diagnosed through machine learning and deep learning, it will significantly reduce the burdens of the doctors and accelerate the critical patient diagnoses. The purpose of the study is to assess the practicability of utilizing deep learning approaches to process coronary computed tomographic angiography (CCTA) imaging (termed CCTA-artificial intelligence, CCTA-AI) in coronary artery stenosis.
MATERIALS AND METHODS
METHODS
A CCTA reconstruction pipeline was built by utilizing deep learning and transfer learning approaches to generate auto-reconstructed CCTA images based on a series of two-dimensional (2D) CT images. 150 patients who underwent successively CCTA and digital subtraction angiography (DSA) from June 2017 to December 2017 were retrospectively analyzed. The dataset was divided into two parts comprising training dataset and testing dataset. The training dataset included the CCTA images of 100 patients which are trained using convolutional neural networks (CNN) in order to further identify various plaque classifications and coronary stenosis. The other 50 CAD patients acted as testing dataset that is evaluated by comparing the auto-reconstructed CCTA images with traditional CCTA images on the condition that DSA images are regarded as the reference method. Receiver operating characteristic (ROC) analysis was used for statistical analysis to compare CCTA-AI with DSA and traditional CCTA in the aspect of detecting coronary stenosis and plaque features.
RESULTS
RESULTS
AI significantly reduces time for post-processing and diagnosis comparing to the traditional methods. In identifying various degrees of coronary stenosis, the diagnostic accuracy of CCTA-AI is better than traditional CCTA (AUC
CONCLUSION
CONCLUSIONS
The proposed CCTA-AI allows the generation of auto-reconstructed CCTA images from a series of 2D CT images. This approach is relatively accurate for detecting ≥50% stenosis and analyzing plaque features compared to traditional CCTA.
Identifiants
pubmed: 32712571
pii: S0169-2607(20)31484-X
doi: 10.1016/j.cmpb.2020.105651
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
105651Informations de copyright
Copyright © 2020. Published by Elsevier B.V.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no conflict of interest.