scholarly journals Heart Coronary Artery Segmentation and Disease Risk Warning Based on a Deep Learning Algorithm

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 140108-140121
Author(s):  
Can Xiao ◽  
Yi Li ◽  
Yimin Jiang
Keyword(s):  
Deep Learning ◽  
Coronary Artery ◽  
Disease Risk ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Risk Warning ◽  
Coronary Artery Segmentation

scholarly journals The effect of confounding data features on a deep learning algorithm to predict complete coronary occlusion in a retrospective observational setting

2021 ◽  
Author(s):  
Rob Brisk ◽  
Raymond Bond ◽  
Dewar Finlay ◽  
James McLaughlin ◽  
Alicja Piadlo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Artery ◽  
Learning Algorithm ◽  
Human Subjects ◽  
Coronary Artery Occlusion ◽  
Artery Occlusion ◽  
Balloon Occlusion ◽  
Single Coronary Artery ◽  
Deep Learning Algorithm ◽  
Using Data

Abstract Aims Deep learning (DL) has emerged in recent years as an effective technique in automated ECG analysis. Methods and results A retrospective, observational study was designed to assess the feasibility of detecting induced coronary artery occlusion in human subjects earlier than experienced cardiologists using a DL algorithm. A deep convolutional neural network was trained using data from the STAFF III database. The task was to classify ECG samples as showing acute coronary artery occlusion, or no occlusion. Occluded samples were recorded after 60 s of balloon occlusion of a single coronary artery. For the first iteration of the experiment, non-occluded samples were taken from ECGs recorded in a restroom prior to entering theatres. For the second iteration of the experiment, non-occluded samples were taken in the theatre prior to balloon inflation. Results were obtained using a cross-validation approach. In the first iteration of the experiment, the DL model achieved an F1 score of 0.814, which was higher than any of three reviewing cardiologists or STEMI criteria. In the second iteration of the experiment, the DL model achieved an F1 score of 0.533, which is akin to the performance of a random chance classifier. Conclusion The dataset was too small for the second model to achieve meaningful performance, despite the use of transfer learning. However, ‘data leakage’ during the first iteration of the experiment led to falsely high results. This study highlights the risk of DL models leveraging data leaks to produce spurious results.

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.

Explainable Deep Learning Algorithm for Distinguishing Incomplete Kawasaki Disease by Coronary Artery Lesions on Echocardiographic Imaging (Preprint)

2021 ◽  
Author(s):  
Haeyun Lee ◽  
Yongsoon Eun ◽  
Jae Youn Hwang ◽  
Lucy Youngmin Eun
Keyword(s):  
Deep Learning ◽  
Coronary Artery ◽  
Kawasaki Disease ◽  
Learning Algorithm ◽  
Clinical Manifestations ◽  
Learning Networks ◽  
Coronary Artery Lesions ◽  
Incomplete Kawasaki Disease ◽  
Deep Learning Algorithm

BACKGROUND Incomplete Kawasaki disease (KD) has often been misdiagnosed due to a lack of the clinical manifestations of classic KD. However, it is associated with a markedly higher prevalence of coronary artery lesions. Identifying coronary artery lesions by echocardiography is important for the timely diagnosis of and favorable outcomes in KD. Moreover, similar to KD, coronavirus disease 2019, currently causing a worldwide pandemic, also manifests with fever; therefore, it is crucial at this moment that KD should be distinguished clearly among the febrile diseases in children. OBJECTIVE In this study, we aimed to validate a deep learning algorithm for classification of KD and other acute febrile diseases. METHODS We obtained coronary artery images by echocardiography of children (n = 88 for KD; n = 65 for pneumonia). We trained six deep learning networks (VGG19, Xception, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) using the collected data. RESULTS SE-ResNext50 showed the best performance in terms of accuracy, specificity, and precision in the classification. SE-ResNext50 offered a sensitivity of 82.64% and a specificity of 58.12%. CONCLUSIONS The results of our study suggested that deep learning algorithms have similar performance to an experienced cardiologist in detecting coronary artery lesions to facilitate the diagnosis of KD. CLINICALTRIAL none

Coronary artery calcium score quantification using a deep-learning algorithm

2020 ◽  
Vol 75 (3) ◽  
pp. 237.e11-237.e16 ◽  
Author(s):  
W. Wang ◽  
H. Wang ◽  
Q. Chen ◽  
Z. Zhou ◽  
R. Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Artery ◽  
Coronary Artery Calcium ◽  
Learning Algorithm ◽  
Coronary Artery Calcium Score ◽  
Calcium Score ◽  
Deep Learning Algorithm
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