Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review
Abstract
:1. Introduction
2. Materials and Methods
- (1)
- Duplicate and non-relevant records were removed;
- (2)
- Resources whose titles and abstracts were not relevant to the topic were excluded;
- (3)
- Non-retrieved resources were removed;
- (4)
- Conference papers, reviews, Ph.D. theses, and sources that did not contain information about the Metaverse, AI, and XR in the context of cardiology used were excluded.
3. Digital Twins in Cardiology—The Heart Digital Twin
4. Extended Reality in Cardiology
5. Artificial Intelligence-Based Support in Cardiology
5.1. Application of the You-Only-Look-Once (YOLO) Algorithm
5.2. Genetic Algorithms
5.3. Artificial Neural Networks
5.4. Convolutional Neural Networks
5.5. Recurrent Neural Networks
5.6. Spiking Neural Networks
5.7. Generative Adversarial Networks
5.8. Graph Neural Networks
5.9. Transformers
5.10. Quantum Neural Networks
5.11. Evaluation Metrics in Medical Image Segmentation
6. Data and Data Security Issues Connected with the Metaverse and Artificial Intelligence
7. Ethical Issues Connected with the Metaverse and Artificial Intelligence
8. Discussion and Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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XR Technology Type | HDM Type | AI Support | Perception of Real Surrounding | Application Field | References |
---|---|---|---|---|---|
MR | HoloLens 2 | No | Yes | Visualization of ultrasound-guided femoral arterial cannulations | [47] |
MR | HoloLens 2 | No | Yes | USG visualization | [48] |
MR | HoloLens 2 | No | Yes | Visualization of heart structures | [49] |
MR | HoloLens 2 | No | Yes | Operation planning | [50] |
MR | HoloLens | No | Yes | Operation planning | [51] |
MR | HoloLens 2 | No | Yes | Visualization of heart structures | [52] |
MR | HoloLens 2 | No | Yes | Visualization of heart structures | [53] |
AR | mobile phone | No | Yes | Diagnosis of the heart | [54] |
AR | none | No | Yes | Virtual pathology stethoscope detection | [55] |
AR | none | Yes | Yes | Eye-tracking system | [56] |
AR | none | Yes | Yes | Detection of semi-opaque markers in fluoroscopy | [57] |
VR | Simulator Stanford Virtual Heart | No | No | Visualization of heart structures | [58] |
VR | Simulator Stanford Virtual Heart | No | No | Visualization of heart structures | [59] |
VR | Meta-CathLab (concept) | No | No | Merging interventional cardiology with the Metaverse | [60] |
VR | VR glasses | Yes | No | Sleep stage classification—concept | [61] |
VR | Virtualcpr: mobile application | Yes | No | Training in cardiopulmonary resuscitation techniques | [62] |
VR | none | Yes | No | Diagnostic of cardiovascular diseases—visualization | [63] |
VR | none | No | No | Cardiovascular education | [61] |
AI/ML Model | Application Fields (In General) | Application Fields (In Cardiology) | References |
---|---|---|---|
ANNs | classification, pattern recognition, image recognition, natural language processing (NLP), speech recognition, recommendation systems, prediction, cybersecurity, object manipulation, path planning, sensor fusion | prediction of atrial fibrillation, acute myocardial infarctions, and dilated cardiomyopathy detection of the structural abnormalities in heart tissues | [85] [86] |
RNNs | ordinal or temporal problems (language translation, speech recognition, NLP image captioning), time series prediction, music generation, video analysis, patient monitoring, disease progression prediction | segmentation of the heart and subtle structural changes cardiac MRI segmentation | [87] [88] |
LSTMs | ordinal or temporal problems (language translation, speech recognition, NLP, image captioning), time series prediction, music generation, video analysis, patient monitoring, disease progression prediction | segmentation and classification of 2D echo images segmentation and classification of 3D Doppler images segmentation and classification of video graphics images and detection of the AMI in echocardiography | [89] [90] |
CNNs | pattern recognition, segmentation/classification, object detection, semantic segmentation, facial recognition, medical imaging, gesture recognition, video analysis | cardiac image segmentation to diagnose CAD cardiac image segmentation to diagnose Tetralogy of Fallot localization of the coronary artery atherosclerosis detection of cardiovascular abnormalities detection of arrhythmia detection of coronary artery disease prediction of the survival status of heart failure patients prediction of cardiovascular disease LV dysfunction screening prediction of premature ventricular contraction detection | [91,92] [93] [94] [95] [96,97,98,99,100,101,102,103,104,105,106,107] [108] [109] [110] [111,112] |
Transformers | NLP, speech processing, computer vision, graph-based tasks, electronic health records, building conversational AI systems and chatbots | coronary artery labeling prediction of incident heart failure arrhythmia classification cardiac abnormality detection segmentation of MRI in case of cardiac infarction classification of aortic stenosis severity LV segmentation heart murmur detection myocardial fibrosis segmentation ECG classification | [113,114] [115] [116,117,118,119] [120] [121] [122,123] [118,124,125] [126] [118] [127] |
SNNs | pattern recognition, cognitive robotics, SNN hardware, brain–machine interfaces, neuromorphic computing | ECG classification detection of arrhythmia extraction of ECG features | [128,129,130] [131,132,133] [134] |
GANs | image-to-image translation, image synthesis and generation, data generation for training, data augmentation, creating realistic scenes | CVD diagnosis segmentation of the LA and atrial scars in LGE CMR images segmentation of ventricles based on MRI scans left ventricle segmentation in pediatric MRI scans generation of synthetic cardiac MRI images for congenital heart disease research | [135] [136] [137] [138] [139] |
GNNs | graph/node classification, link prediction, graph generation, social/biological network analysis, fraud detection, recommendation systems | classification of polar maps in cardiac perfusion imaging analysis of CT/MRI scans prediction of ventricular arrhythmia segmentation of cardiac fibrosis diagnosis of cardiac condition: LV motion in cardiac MR cine images automated anatomical labeling of coronary arteries prediction of CAD automation of coronary artery analysis using CCTA screening of cardio, thoracic, and pulmonary conditions in chest radiograph | [140,141] [142] [141] [141] [143] [144] [145] [146] [147] |
QNNs | optimization of hardware operations, user interfaces | classification of ischemic heart disease | [97] |
GA | optimization techniques, risk prediction, gene therapies, medicine development | classification of heart disease | [148] |
Network Type | Evaluation Metrics | Input | Output | Xr Connection | Dt Contention | Reference |
---|---|---|---|---|---|---|
ANN | accuracy 94.32% | ECG recordings | binary classification of normal and ventricular ectopic beats | No | No | [131] |
ROI 89.00% | Echocardiography | Automatic measurement of left ventricular strain | No | No | [163] | |
accuracy 91.00% | Electronic health records | classification and prediction of cardiovascular diseases | No | No | [162] | |
RNN-LSTM | accuracy 80.00% F1 score 84.00% | 3D Doppler images | heart abnormalities classification | No | No | [89] |
accuracy 97.00% F1 score 97.00% | 2D echo images | heart abnormalities classification | No | No | [89] | |
(1) accuracy 85.10% (2) accuracy 83.20% | echocardiography images | automated classification of acute myocardial infarction: (1) classification of the left ventricular long-axis view; (2) classification of short-axis view (papillary muscle level) | No | No | [90] | |
accuracy 93.10% | coronary computed tomography angiography | diagnostic of the coronary artery calcium | No | No | [175] | |
accuracy 90.67% | ECG recordings | prediction of the arrhythmia | No | No | [98] | |
RNN | IoU factor 92.13% | MRI cardiac images | estimation of the cardiac state: sequential four-chamber view left ventricle wall segmentation | No | No | [116] |
CNN | accuracy 95.92% | ECG recordings | binary classification of normal and ventricular ectopic beats | No | No | [131] |
IoU factor 61.75% | MRI cardiac images | estimation of the cardiac state: sequential four-chamber view left ventricle wall segmentation | No | No | [116] | |
accuracy 94.00% F1 score 95.00% | 2D echo images | heart abnormalities classification | No | No | [89] | |
accuracy 98.00% F1 score 98.00% | 3D Doppler images | heart abnormalities classification | No | No | [89] | |
accuracy 92.00% | ECG recordings | ECG classification | No | No | [183] | |
accuracy 88.00% | Electronic health records | heart disease prediction | No | No | [135] | |
accuracy 98.82% | ECG recordings | prediction of heart failure and arrhythmia | No | No | [102] | |
accuracy 95.13% | Electronic health records | prediction of the survival status of heart failure patients | No | No | [108] | |
accuracy 99.60% | ECG recordings | estimation of the fetal heart rate | No | No | [207] | |
accuracy 99.10% | heart audio recordings | heart disease classification | No | No | [208] | |
accuracy 97.00% | heart sound signals | classification of heart murmur | No | No | [209] | |
accuracy 98.95% | heart sound signals | classification of heart sound signals | No | No | [210] | |
ROC curve 0.834 | heart sound signals | prediction of obstructive coronary artery disease | No | No | [211] | |
accuracy 85.25% | MRI image scans | chronic disease prediction | No | No | [212] | |
accuracy 99.10% | heart sound signals | diagnosis of cardiovascular disease | No | No | [213] | |
CNN-LSTM | accuracy 99.52% Dice coef. 0.989 ROC curve 0.999 | ECG recordings | prediction of congestive heart failure | No | No | [214] |
accuracy 96.66% | Heart disease Cleveland UCI dataset | prediction of the heart disease | No | No | [215] | |
accuracy 99.00% | ECG recordings | prediction of the heart failure | No | No | [106] | |
SNN | ROC curve 0.99 | ECG recordings | ECG classification | No | No | [181] |
accuracy 97.16% | ECG recordings | binary classification of normal and ventricular ectopic beats | No | No | [131] | |
accuracy 93.60% | ECG recordings | ECG classification | No | No | [182] | |
accuracy 85.00% | ECG recordings | ECG classification | No | No | [180] | |
accuracy 84.41% | ECG recordings | ECG classification | No | No | [129] | |
accuracy 91.00% | ECG recordings | ECG classification | No | No | [183] | |
GNN | Dice coef. 0.82 | cardiac MRI images | prediction diverticular arrhythmia | No | No | [141] |
ROC curve 0.739 | CT image scan | prediction of coronary artery disease | No | No | [145] | |
AUC 0.821 | chest radiographs | screening of cardio, thoracic, and pulmonary conditions | No | No | [147] | |
ROC area 0.98 | 12-lead ECG record | remote monitoring of surface electrocardiograms | No | No | [192] | |
GAN | accuracy 99.08% Dice coef. 0.987 | CT image scan | cardiac fat segmentation | No | No | [52] |
accuracy 98.00% | ECG recordings | ECG classification | No | No | [216] | |
accuracy 95.40% | ECG recordings | ECG classification | No | No | [217] | |
accuracy 68.07% | CTG signal dataset | fetal heart rate signal classification | No | No | [218] | |
Dice coef. 0.880 | MRI image scans | segmentation of the left ventricle | No | No | [138] | |
Transformers | accuracy 96.51% | Cleveland dataset | prediction of cardiovascular diseases | No | No | [219] |
accuracy 98.70% | heart sound signals—Mel-spectrogram, bispectral analysis, and Phonocardiogram | heart sound classification | No | No | [220] | |
Dice coef. 0.861 | 12-lead ECG record | arrhythmia classification | No | No | [221] | |
Dice coef. 0.0004 | ECG recordings | arrhythmia classification | No | No | [222] | |
Dice coef. 0.980 | ECG recordings | arrhythmia classification | No | No | [223] | |
Dice coef. 0.911 | ECG recordings | classification of ECG recordings | No | No | [134] | |
GA | - | laboratory data, patient medical history, ECG, physical examinations, and echocardiogram (Z-Alizadeh Sani dataset) | determination of the parameters to prediction of the coronary artery disease (next SVM-based classifier was applied) | No | No | [157] |
QNN | accuracy 84.60% | Electronic health records | classification of ischemic cardiopathy | No | No | [97] |
accuracy 91.80% Dice coef. 0.918 | X-ray coronary angiography | stenosis detection | No | No | [202] |
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Rudnicka, Z.; Proniewska, K.; Perkins, M.; Pregowska, A. Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review. Electronics 2024, 13, 866. https://doi.org/10.3390/electronics13050866
Rudnicka Z, Proniewska K, Perkins M, Pregowska A. Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review. Electronics. 2024; 13(5):866. https://doi.org/10.3390/electronics13050866
Chicago/Turabian StyleRudnicka, Zofia, Klaudia Proniewska, Mark Perkins, and Agnieszka Pregowska. 2024. "Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review" Electronics 13, no. 5: 866. https://doi.org/10.3390/electronics13050866