Deep Learning–Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography

Kwon, Joon; Lee, Soo-Youn; Jeon, Ki‐Hyun; Lee, Yeha; Kim, Kyung Hee; Park, Jinsik; Oh, Byung Hee; Lee, Myong Mook

doi:10.1161/jaha.119.014717

Cited by 137 publications

(90 citation statements)

References 35 publications

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“…Attia et al developed deep-learning algorithms for screening cardiac contractile dysfunction, predicting the occurrence of atrial fibrillation during sinus rhythm, approximating age and sex, and detecting hyperkalemia using raw ECG data and demonstrated its feasibility 11,12,24,25 . Our study showed that a deep-learning-based algorithm using ECG could outperform cardiologists in diagnosing left ventricular hypertrophy and valvular heart disease 14,15,26,27 . We used a sensitivity map to visualize the regions of the ECGs that were used for decision-making by the DLA.…”

Section: Discussionmentioning

confidence: 81%

“…Deep learning is a type of artificial intelligence approach that extracts and uses meaningful patterns from complex digital data and has recently been used to analyze ECGs for diagnosing an arrhythmia, heart failure, left ventricular hypertrophy, valvular heart disease, age, and sex [11][12][13][14][15][16] . To develop a reliable MI detecting method based on ECG, we used deep learning.…”

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confidence: 99%

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Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

Cho¹,

Kwon

Kim

et al. 2020

Sci Rep

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Rapid diagnosis of myocardial infarction (MI) using electrocardiography (ECG) is the cornerstone of effective treatment and prevention of mortality; however, conventional interpretation methods has low reliability for detecting MI and is difficulty to apply to limb 6-lead ECG based life type or wearable devices. We developed and validated a deep learning-based artificial intelligence algorithm (DLA) for detecting MI using 6-lead ECG. A total of 412,461 ECGs were used to develop a variational autoencoder (VAE) that reconstructed precordial 6-lead ECG using limb 6-lead ECG. Data from 9536, 1301, and 1768 ECGs of adult patients who underwent coronary angiography within 24 h from each ECG were used for development, internal and external validation, respectively. During internal and external validation, the area under the receiver operating characteristic curves of the DLA with VAE using a 6-lead ECG were 0.880 and 0.854, respectively, and the performances were preserved by the territory of the coronary lesion. Our DLA successfully detected MI using a 12-lead ECG or a 6-lead ECG. The results indicate that MI could be detected not only with a conventional 12 lead ECG but also with a life type 6-lead ECG device that employs our DLA.

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Section: Discussionmentioning

confidence: 81%

mentioning

confidence: 99%

Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

Cho¹,

Kwon

Kim

et al. 2020

Sci Rep

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“…Furthermore, these previous studies all focus on prediction, but specific ECG patterns used by DNNs are rarely visualized. 3 , 5 – 8 Visualization of such features takes advantage of the feature discovery embedded in DNNs and will help clinicians to interpret ECGs more accurately and possibly facilitate discovery of novel features.…”

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confidence: 99%

Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning

Leur

Taha

Bos

et al. 2021

Circ: Arrhythmia and Electrophysiology

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Background - Electrocardiogram (ECG) interpretation requires expertise and is mostly based on physician recognition of specific patterns, which may be challenging in rare cardiac diseases. Deep neural networks (DNN) can discover complex features in ECGs and may facilitate the detection of novel features which possibly play a pathophysiological role in relatively unknown diseases. Using a cohort of phospholamban (PLN) p.Arg14del mutation carriers, we aimed to investigate whether a novel DNN-based approach can identify established ECG features, but moreover we aimed to expand our knowledge on novel ECG features in these patients. Methods - A DNN was developed on 12-lead median beat ECGs of 69 patients and 1380 matched controls and independently evaluated on 17 patients and 340 controls. Differentiating features were visualized using Guided Grad-CAM++. Novel ECG features were tested for their diagnostic value by adding them to a logistic regression model including established ECG features. Results - The DNN showed excellent discriminatory performance with a c-statistic of 0.95 (95% confidence interval 0.91-0.99) and sensitivity and specificity of 0.82 and 0.93, respectively. Visualizations revealed established ECG features (low QRS voltages and T-wave inversions), specified these features (e.g. R and T-wave attenuation in V2/V3) and identified novel PLN-specific ECG features (e.g. increased PR-duration). The logistic regression baseline model improved significantly when augmented with the identified features (p<0.001). Conclusions - A DNN-based feature detection approach was able to discover and visualize disease-specific ECG features in PLN mutation carriers and revealed yet unidentified features. This novel approach may help advance diagnostic capabilities in daily practice.

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“…The application of deep learning technology in the cardiovascular eld for arrhythmias, dyskalemia, and valvular heart disease had become popularized recently. [19][20][21][27][28][29] However, no large scale study has been designed to apply deep learning technology for MI detection. Previous DLMs for MI detection by ECG were analyzed mainly from the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG Database.…”

Section: Discussionmentioning

confidence: 99%

A Deep-learning Algorithm With the Real World Validation for Detecting Acute Myocardial Infarction

Liu¹,

Lin²,

Tsai³

et al. 2020

Preprint

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BackgroundThe initial detection and diagnosis of ST-segment or non-ST-segment elevation myocardial infarction (STEMI or NSTEMI) definitely rely on a 12-lead electrocardiogram (ECG). Delay or misdiagnosis is not unusual by subjective interpretation. Our aim is to develop a DLM as a diagnostic support tool to detect MI based on a 12-lead ECG and to evaluate the performance of this model.MethodsThis study included 1,051 ECGs from 737 coronary angiography (CAG)-validated STEMI patients, 697 ECGs from 287 CAG-validated NSTEMI patients, and 140,336 not-MI ECGs from 76,775 patients at emergency departments. DLM was trained and validated for the performance using 80% and 20% of the ECGs, respectively. A human-machine competition was conducted. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the performance of DLM and experts. STEMI versus not-STEMI, and MI versus not-MI were evaluated by DLM.ResultsThe AUCs of DLM for identifying STEMI and MI were 0.976 and 0.944 in the human-machine competition, respectively, which were significantly better than those of our best clinicians. In the real world setting, DLM presented with AUC of 0.995/0.916 with corresponding sensitivities of 96.9%/77.0%, and specificities of 96.2%/92.9% in the identification of STEMI and MI, respectively. Furthermore, DLM demonstrated sufficient diagnostic capacity for STEMI without the aid of troponin I (TnI) (AUC= 0.996) with corresponding sensitivity and specificity of 98.4% and 96.9%. The AUC of combined DLM and the first recorded TnI for the detection of NSTEMI were increased to 0.978 with corresponding sensitivity and specificity of 91.6% and 96.7%, which was better than that of DLM (0.877) or TnI (0.949) alone. ConclusionsDLM may serve as a diagnostic decision tool to assist intensive or emergency medical system-based networks and frontline physicians in identifying STEMI and NSTEMI in a timely and precise manner to prevent delay or misdiagnosis, and thereby to facilitate subsequent reperfusion therapy.

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Deep Learning–Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography

Cited by 137 publications

References 35 publications

Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning

A Deep-learning Algorithm With the Real World Validation for Detecting Acute Myocardial Infarction

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