Table 1.
Examples of artificial intelligence applications in cardiology.
| Diagnostic modality/type of data used | Application | Study methodology | Reference(s) |
|---|---|---|---|
| Echocardiography | Identification of echocardiographic views | A convolutional neural network was used to distinguish between 15 standard echocardiographic views with an accuracy of 97.8% | Madani et al. 2018 [28] |
| Differentiating CP from RCM | The model was based on an associative memory classifier algorithm. Echocardiograms of 50 patients with CP, 44 with RCM and 47 controls were used to train the model | Sengupta et al. 2016 [29] | |
| Fully automated echocardiogram interpretation and detection of selected clinical conditions | A convolutional neural network was trained on 14,035 echocardiograms to identify views, perform the segmentation of heart chambers, determine ejection fraction and other measurements and finally to detect a number of clinical conditions (cardiomyopathy, cardiac amyloidosis and pulmonary arterial hypertension with the C statistics of 0.93, 0.87, and 0.85, respectively) | Zhang et al. 2018 [30] | |
| CT | Calculating CS based on CT-angiography scans. (May obviate the need for a separate CS scan; thus, reducing the radiation dose) | The authors designed a convolutional neural network that processes each of the three axes (axial, saggital, coronal) separately. The model was trained using a total of 250 hand-annotated exams | Wolterink et al. 2016 [31] |
| Calculating FFR values based on cardiac CT | The models created using convolutional neural networks have some advantages (including shorter computation times) over the clinically validated approach based on computational fluid dynamics while maintaining a non-inferior performance | Coenen et al. 2018 [32] Tesche et al. 2018 [33] |
|
| Predicting all-cause mortality based on cardiac CT and clinical variables | 25 clinical and 44 CT-derived variables of over 10,000 patients were used to train the iterative Logit Boost algorithm. The resulting model could predict a 5-year mortality rate with the c-statistic of 0.79 | Motwani et al. 2017 [34] | |
| CT scan denoising — improving readability of acquired images while also reducing the necessary radiation exposure | The authors obtained scans using 20% and 100% of the clinical radiation dose. The model based on generative adversarial network architecture was trained to generate full-quality images based on the images acquired with a low radiation dose | Wolterink et al. 2017 [35] | |
| Detecting significant coronary lesions based on the motion of the LV myocardium | The complex model consisted of a convolutional neural network (for the myocardium segmentation), an unsupervised convolutional autoencoder (for the extraction of the myocardium characteristics) and a support vector classifier | Zreik et al. 2018 [36] | |
| Predicting cardiac death after myocardial perfusion SPECT imaging | A total of 122 features (both the clinical data and variables derived from SPECT scans) of over 8,000 patients were used to train the multiple ML models. A model based on SVM outperformed baseline logistic regression as well as random forests | Haro Alonso et al. 2019 [37] | |
| Detecting the presence and location of significant coronary artery stenosis based on SPECT images | In these multicenter studies, all patients underwent myocardial perfusion imaging and coronary angiography within 6 months. A deep neural network was trained to predict obstructive coronary disease based on SPECT myocardial perfusion images | Betancur et al. 2018, 2019 [38, 39] | |
| Predicting MACE using a combination of clinical data and myocardial perfusion SPECT images | 28 clinical variables, 17 stress test variables, and 25 imaging variables of 2,619 patients were analyzed. The ML model was based on the Logit Boost algorithm | Betancur et al. 2018 [40] | |
| MRI | Segmentation of heart structures, automatic measurement of the LV end-diastolic volume and other values | A fully convolutional neural network was trained using pixel-annotated MRI images from 4,875 patients. The model was able to perform highly accurate automatic measurements and delineation of heart structures | Bai et al. 2018 [41] |
| Detecting abnormalities of aortic valve | The authors developed a novel strategy for training medical ML models using unlabeled imaging data. They created a weakly-supervised model capable of diagnosing aortic valve abnormalities in MRI scans | Fries et al. 2019 [42] | |
| Objective assessment of atrial scarring for patients with AF | The authors developed a complete pipeline for atrial scarring segmentation. A classification algorithm based on SVM was used | Yang et al. 2018 [43] | |
| Diagnosing pulmonary hypertension based on cardiovascular MRI | The model was trained using 220 MRI scans of patients who had also underwent right heart catheterization | Swift et al. 2020 [44] | |
| Coronary angiography | Segmentation of coronary vessels from angiograms | The model was based on a U-Net architecture (a type of a deep neural network). 3,302 still images of coronary arteries were used to train the model | Yang et al. 2019 [45] |
| ECG signal | Diagnosing ALVD using ECG only | The ECG signals and echocardiographic data of 97,829 patients were used (the time between ECG and echocardiography was less than 2 weeks). A model based on a neural network could predict ALVD with a sensitivity and specificity of 86%. The initial study laid the groundwork for a prospective evaluation and the ongoing clinical trial | Attia et al. 2019 [21,22] |
| Detecting paroxysmal AF based on contemporary 12-lead ECG taken on SR | The authors have shown that it is possible to identify an ‘electrocardiographic signature’ of paroxysmal AF in a routine 10-second 12-lead ECG. The use of a convolutional neural network allowed the detection of signals invisible to the human eye | Attia et al. 2019 [20] | |
| Predicting the development of moderate to severe MR based on 12-lead ECG using a deep neural network | The AUROC in external validation of 10,865 cases was 0.877. Positively diagnosed patients also had a higher chance of developing MR in the future. Additionally, the authors used visualization techniques that helped understand which parts of an ECG influence the decisions of their algorithm | Kwon et al. 2020 [46] | |
| EHR | Predicting cardiovascular risk based on records from primary care | 30 variables identified within the primary health records of 378,256 patients were analyzed. The authors used a number of ML algorithms including logistic regression, random forests and neural networks | Weng et al. 2017 [47] |
| Predicting the in-hospital mortality rate, readmission and a prolonged length of stay based on raw electronic health records | Multi-year medical histories stored in EHRs linked to 216,221 hospitalizations were converted into over 46 billion data points, each representing a result, clinical event, physician’s note etc. An ensemble of three types of neural networks was trained to predict various clinical endpoints with high accuracy | Rajkomar et al. 2018 [48] | |
| Predicting the probability of in-hospital death at the time of admission | The model was crated based on retrospective data but validated prospectively and externally in 3 different hospitals. A total number of over 75,000 admissions were used to create and validate the model. The AUROC was 0.86 in an external validation | Brajer et al. 2020 [49] | |
| Clinical data | Predicting readmission of patients with heart failure | An EHR-wide feature selection (over 4,000 variables were considered) and a model based on logistic regression was developed to predict the 30-day readmission rates | Shameer et al. 2017 [50] |
| Predicting long-and short-term mortality after ACS | In these papers various ‘classical’ ML models (support vector machines, random forests, xgboost) were developed to predict mortality after acute coronary syndromes using clinical data | Shouval et al. 2017 [51] Wallert et al. 2017 [52] Pieszko et al. 2018, 2019 [53, 54] |
|
| Predicting the risk of MACE and bleeding after ACS | The data on over 24,000 patients with ACSs were pooled from 4 randomized controlled trials. The ML algorithm demonstrated superiority over traditional risk scores | Gibson et al. 2020 [55] | |
| Selecting the right patients for CRT | Classical ML algorithms were applied to predict survival after CRT implantation. The model based on random forest showed the best performance | Kalscheur et al. 2018 [56] |
ACS — acute coronary syndrome; AF — atrial fibrillation; ALVD — asymptomatic left ventricular dysfunction; CP — constrictive pericarditis; CRT — cardiac resynchronization therapy; CS — Calcium Score; CT — computed tomography; EHR — electronic health records; ECG — electrocardiogram; FFR — fractional flow reserve; LV — left ventricle; MACE — major adverse cardiac events; ML — machine learning; MR — mitral regurgitation; MRI — magnetic resonance imaging; RCM — restrictive cardiomyopathy; SR — sinus rhythm; SVM — support vector machines; SPECT — single-photon emission computed tomography