Survey on - Identification of Coronary Artery Disease using Deep Learning

2020 ◽  
Author(s):  
M. JayaSree ◽  
L. Koteswara Rao
Keyword(s):  
Coronary Artery Disease ◽  
Deep Learning ◽  
Coronary Artery ◽  
Artery Disease

scholarly journals Diagnostic performance of deep learning-based vascular extraction and stenosis detection technique for coronary artery disease

2020 ◽  
Vol 93 (1113) ◽  
pp. 20191028 ◽  
Author(s):  
Meng Chen ◽  
Ximing Wang ◽  
Guangyu Hao ◽  
Xujie Cheng ◽  
Chune Ma ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Deep Learning ◽  
Coronary Artery ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Operating Characteristics ◽  
Stenosis Detection ◽  
Artery Disease

Objective: To investigate the diagnostic performance of deep learning (DL)-based vascular extraction and stenosis detection technology in assessing coronary artery disease (CAD). Methods: The diagnostic performance of DL technology was evaluated by retrospective analysis of coronary computed tomography angiography in 124 suspected CAD patients, using invasive coronary angiography as reference standard. Lumen diameter stenosis ≥50% was considered obstructive, and the diagnostic performances were evaluated at per-patient, per-vessel and per-segment levels. The diagnostic performances between DL model and reader model were compared by the areas under the receiver operating characteristics curves (AUCs). Results: In patient-based analysis, AUC of 0.78 was obtained by DL model to detect obstructive CAD [sensitivity of 94%, specificity of 63%, positive predictive value of 94%, and negative predictive value of 59%], While AUC by reader model was 0.74 (sensitivity of 97%, specificity of 50%, positive predictive value of 93%, negative predictive value of 73%). In vessel-based analysis, the AUCs of DL model and reader model were 0.87 and 0.89 respectively. In segment-based analysis, the AUCs of 0.84 and 0.89 were obtained by DL model and reader model respectively. It took 0.47 min to analyze all segments per patient by DL model, which is significantly less than reader model (29.65 min) (p < 0.001). Conclusion: The DL technology can accurately and effectively identify obstructive CAD, with less time-consuming, and it could be a reliable diagnostic tool to detect CAD. Advances in knowledge: The DL technology has valuable prospect with the diagnostic ability to detect CAD.

Deep learning-based intravascular ultrasound segmentation for the assessment of coronary artery disease

2021 ◽  
Author(s):  
Takeshi Nishi ◽  
Rikiya Yamashita ◽  
Shinji Imura ◽  
Kazuya Tateishi ◽  
Hideki Kitahara ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Deep Learning ◽  
Coronary Artery ◽  
Intravascular Ultrasound ◽  
Artery Disease

scholarly journals Automatic Diagnosis of Coronary Artery Disease in SPECT Myocardial Perfusion Imaging Employing Deep Learning

2021 ◽  
Vol 11 (14) ◽  
pp. 6362
Author(s):  
Nikolaos Papandrianos ◽  
Elpiniki Papageorgiou
Keyword(s):  
Coronary Artery Disease ◽  
Deep Learning ◽  
Coronary Artery ◽  
Myocardial Perfusion Imaging ◽  
Myocardial Perfusion ◽  
Perfusion Imaging ◽  
Emission Computed Tomography ◽  
Automatic Diagnosis ◽  
Spect Mpi ◽  
Artery Disease

Focusing on coronary artery disease (CAD) patients, this research paper addresses the problem of automatic diagnosis of ischemia or infarction using single-photon emission computed tomography (SPECT) (Siemens Symbia S Series) myocardial perfusion imaging (MPI) scans and investigates the capabilities of deep learning and convolutional neural networks. Considering the wide applicability of deep learning in medical image classification, a robust CNN model whose architecture was previously determined in nuclear image analysis is introduced to recognize myocardial perfusion images by extracting the insightful features of an image and use them to classify it correctly. In addition, a deep learning classification approach using transfer learning is implemented to classify cardiovascular images as normal or abnormal (ischemia or infarction) from SPECT MPI scans. The present work is differentiated from other studies in nuclear cardiology as it utilizes SPECT MPI images. To address the two-class classification problem of CAD diagnosis, achieving adequate accuracy, simple, fast and efficient CNN architectures were built based on a CNN exploration process. They were then employed to identify the category of CAD diagnosis, presenting its generalization capabilities. The results revealed that the applied methods are sufficiently accurate and able to differentiate the infarction or ischemia from healthy patients (overall classification accuracy = 93.47% ± 2.81%, AUC score = 0.936). To strengthen the findings of this study, the proposed deep learning approaches were compared with other popular state-of-the-art CNN architectures for the specific dataset. The prediction results show the efficacy of new deep learning architecture applied for CAD diagnosis using SPECT MPI scans over the existing ones in nuclear medicine.

Sign in / Sign up

Export Citation Format

Share Document