Deep Learning Algorithm Predicts Angiographic Coronary Artery Disease in Stable Patients Using Only a Standard 12-lead Electrocardiogram

2021 ◽  
Author(s):  
Michael Leasure ◽  
Utkars Jain ◽  
Adam Butchy ◽  
Jeremy Otten ◽  
Veronica A. Covalesky ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Deep Learning ◽  
Coronary Artery ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Artery Disease

scholarly journals Feasibility of using deep learning to detect coronary artery disease based on facial photo

2020 ◽  
Vol 41 (46) ◽  
pp. 4400-4411 ◽  
Author(s):  
Shen Lin ◽  
Zhigang Li ◽  
Bowen Fu ◽  
Sipeng Chen ◽  
Xi Li ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Deep Learning ◽  
Coronary Artery ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Test Group ◽  
Cross Sectional ◽  
Deep Learning Algorithm ◽  
Increased Risk ◽  
Artery Disease

Abstract Aims Facial features were associated with increased risk of coronary artery disease (CAD). We developed and validated a deep learning algorithm for detecting CAD based on facial photos. Methods and results We conducted a multicentre cross-sectional study of patients undergoing coronary angiography or computed tomography angiography at nine Chinese sites to train and validate a deep convolutional neural network for the detection of CAD (at least one ≥50% stenosis) from patient facial photos. Between July 2017 and March 2019, 5796 patients from eight sites were consecutively enrolled and randomly divided into training (90%, n = 5216) and validation (10%, n = 580) groups for algorithm development. Between April 2019 and July 2019, 1013 patients from nine sites were enrolled in test group for algorithm test. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated using radiologist diagnosis as the reference standard. Using an operating cut point with high sensitivity, the CAD detection algorithm had sensitivity of 0.80 and specificity of 0.54 in the test group; the AUC was 0.730 (95% confidence interval, 0.699–0.761). The AUC for the algorithm was higher than that for the Diamond–Forrester model (0.730 vs. 0.623, P < 0.001) and the CAD consortium clinical score (0.730 vs. 0.652, P < 0.001). Conclusion Our results suggested that a deep learning algorithm based on facial photos can assist in CAD detection in this Chinese cohort. This technique may hold promise for pre-test CAD probability assessment in outpatient clinics or CAD screening in community. Further studies to develop a clinical available tool are warranted.

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
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