Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive Heart Health
Abstract
1. Introduction
2. Methodology
3. Components of the Digital Cardiovascular Twin
3.1. Sensory Layer
3.2. Machine Learning and Deep Learning for Data Interpretation and Prediction
Task (Cardiac Monitoring) | Method Class | Data (Brief) | Key Performance (Metric) | Where It Excels (Comparative) | Limitations/Caveats in CVD Use | Best-Fit Scenarios (Applicability) | References |
---|---|---|---|---|---|---|---|
LV systolic dysfunction screening (LVEF ≤ 40%) from 12-lead ECG | Transformer (foundation; pretrain → finetune) | Pretrained on 8.5 M ECGs; fine-tuned for LVEF/other tasks | AUROC 0.86 (internal with 1% labels); 0.87 external; boosts low-label tasks | Captures long-range temporal patterns; excellent label- efficiency; strong multi-task transfer | Requires massive pretraining compute; deployment still needs careful calibration/ interpretability | Population-scale ECG pre-screen → triage to echocardiography; low-label health-system settings | [57] |
Near-term AF risk (≤14 days) from patch single-lead ECG | Deep learning (attention/temporal; multimodal) | 459,889 ambulatory single-lead recordings (10 min–24 h) | AUC 0.80 (1-day horizon, “All features” model) | Early warning on AF-free ECG; integrates HRV + rhythm + demographics | Retrospective, device- specific; risk of distribution shift across wearables; prospective validation needed | Wearable AF surveillance and early-warning gating for patch/consumer ECG programs | [58] |
Arrhythmia classification (12-lead; Chapman) | Graph Neural Network (lead-graph) | 10,646 subjects; 12 leads; 7 classes | Accuracy 99.82%, Specificity 99.97% (GCN-WMI) | Encodes inter-lead relations; strong multi-lead performance | Limited for single-lead wearables; sensitive to lead configuration | In-clinic 12-lead analysis; multi-lead Holter/offline QA where inter-lead coupling matters | [59] |
Coronary CTA: vessel extraction and anatomical labeling | GCN on vascular graphs | 104 CCTA; 10 segment classes (AHA) | Tree-extraction 0.85; overall labeling 0.74 | Preserves tree topology; better anatomical consistency than CNN-only | Dependent on reliable centerline/segmentation; calcifications/gaps still problematic | Pre-procedural planning; CAD quantification pipelines with human oversight | [60] |
HF drug-response prediction/trajectory modeling (EHR) | Spatiotemporal GNN + Transformer (patient-visit graphs) | 11,627 HF patients (Mayo Clinic EHR) | Outperformed baselines across 5 drug classes; best RMSE 0.0043 (NT-proBNP) | Learns longitudinal + relational patterns; subgrouping improves prediction and interpretability | Site-specific coding/ practice → transferability/ harmonization needed; privacy/PII governance | Hospital CDSS for HF titration; digital-twin personalization of therapy trajectories | [61] |
Cardiac MRI segmentation (quality-aware automation) | Bayesian deep learning (uncertainty quantification) | Multi-center CMR; benchmarked Bayesian vs. non-Bayesian UQ | UQ triage cuts “poor” segmentations to 5%; only 31–48% cases require review | Safety guardrails; robust to OOD noise/blur (method- dependent) | Extra compute; needs workflow integration for human-in-the-loop review | Semi-automated CMR pipelines where safe triage and QC trump raw speed | [62] |
Cuff-less blood pressure from wearables (PPG/ECG) | Transformer-hybrid (CNN+Transformer) | Two large wearables datasets: CAS-BP and Aurora-BP | CAS-BP: DBP 0.9 ± 6.5, SBP 0.7 ± 8.3 mmHg; Aurora-BP: DBP −0.4 ± 7.0, SBP −0.4 ± 8.6 mmHg; MAE below SOTA | Learns global temporal dependencies; fuses handcrafted + learned features | Domain/calibration drift across devices/skin tones/contexts; prospective ambulatory validation needed | Ambulatory BP trending and coaching with periodic calibration; patient-facing wearables | [63] |
4. AI Agents and Medical LLMs for Personalized Intervention
4.1. Generative AI and Medical LLMs
4.2. Proactive AI Agents
5. Discussion
6. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
AMI | Ambient Intelligence |
AUC | Area Under the Curve |
BLE | Bluetooth Low Energy |
CNN | Convolutional Neural Network |
CT | Computed Tomography |
CVD | Cardiovascular Disease |
DL | Deep Learning |
DLT | Distributed Ledger Technology |
ECG | Electrocardiography |
EDA | Electrodermal Activity |
EHR | Electronic Health Record |
EMG | Electromyography |
GNN | Graph Neural Network |
HR | Heart Rate |
HRV | Heart Rate Variability |
ICU | Intensive Care Unit |
IoMT | Internet of Medical Things |
IoT | Internet of Things |
LLM | Large Language Model |
LoRA | Low-Rank Adaptation |
LSTM | Long Short-Term Memory |
MAE | Mean Absolute Error |
ML | Machine Learning |
MRI | Magnetic Resonance Imaging |
NLP | Natural Language Processing |
PPG | Photoplethysmography |
PRS | Polygenic Risk Score |
RR | R-R Interval |
SDNN | Standard Deviation of NN Intervals |
SHAP | Shapley Additive Explanations |
SVM | Support Vector Machine |
VQA | Visual Question Answering |
GDPR | General Data Protection Regulation |
HL7 | Health Level 7 |
FHIR | Fast Healthcare Interoperability Resources |
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Device | Sensor Type | Measured Parameters | Connectivity | Edge Capabilities | Role in Architecture | References |
---|---|---|---|---|---|---|
Zephyr BioHarness | ECG, Accelerometer | HR, HRV, RR intervals, posture | Bluetooth | RR detection, motion filtering | Wearable + Edge | [23] |
Empatica E4 | PPG, EDA, Temp, ACC | HR, SpO2, GSR, skin temp | Bluetooth, USB | On-device preprocessing | Wearable Layer | [24] |
Polar H10 | ECG | HR, RR intervals | Bluetooth | Onboard HRV analysis | Sensor + Edge Layer | [24,26] |
Xiaomi Smart Band 7 | PPG, Accelerometer | HR, SpO2, sleep, steps | BLE | Basic HR tracking | Low-power Wearable | [23] |
Shimmer3 GSR+ | PPG, GSR | HR, GSR, skin conductanc | Bluetooth | Raw signal logging | Research-grae Sensor Layer | [25] |
Hexoskin Smart Shirt | ECG, Respiration, ACC | HR, respiration rate, HRV | Bluetooth | On-board buffering | Multimodal Wearable | [27] |
Raspberry Pi + MAX30102 | PPG | HR, SpO2, RR, RMSSD, SDNN | I2C, Wi-Fi | Python (3.12)-based HRV computation | Edge + AI Input | [28] |
Custom EMG on ESP32 | EMG (Digital) | RMS, MAV, ZC, SSC (muscle fatigue) | GPIO, UART | Real-time signal classification | Sensor + Edge Layer | [23,26] |
Winsen ZPHS01B | Gas, Temp, Humidity | CO2, Temp, RH | UART/I2C | Digital signal output | Environmental Layer | [31] |
Genomic API | Digital API | SNPs, PRS, gene markers | REST API | Cloud analytics | Genetic Input Layer | [29,30,31] |
Model Name | Input Data | Base Architecture | Fine-Tuning Method | Target Disease | Reported Accuracy | Reference(s) |
---|---|---|---|---|---|---|
Heart Sense Transformer | ECG Image + IoT Sensor Data | Transformer | Custom ECG dataset fine-tuning | Heart Disease | 92.6% | [61] |
ECG-DL13 | ECG Signal | CNN + Transfer Learning | Small ECG datasets | 13 Heart Conditions | 94.2% | [62] |
EchoMed-CNN | Echocardiogram | CNN | Pretrained CNN layers fine-tuned | Endocarditis | 90.4% | [63] |
Foundation ECG | Single-lead ECG | Transformer (Echo-FM) | Per clinical label fine- tuning | Multiple CVD conditions | 88.9% | [64] |
Deep Ensemble ECG | ECG Image | Ensemble CNN models | 8 models fine-tuned on private ECG dataset | Arrhythmia and related CVD | 95.1% | [65] |
IoT-ENN Hybrid | Wearable ECG sensor signals | Epistemic Neural Network | Optimized with Boosted Sooty Tern Algorithm | Cardiac Arrhythmias | 91.3% | [66] |
Model or Topic | Application Area | Architecture | Notable Features | References |
---|---|---|---|---|
ChatGPT in Science and Healthcare | Clinical communication, research | Transformer (Decoder-only) | Conversational assistant, summarization | [67] |
AI/ML in Pathology and Medicine | Pathology, diagnostics, education | General overview (including transformers) | Foundational concepts for generative/nongenerative AI | [68] |
LLMs in Ophthalmology | Ophthalmology | BERT, GPT-4, PubMed-BERT, ClinicalBERT | Assessment in exams, clinical notes | [69] |
Overview of Generative Models | NLP, vision, general AI | GANs, Autoencoders, Diffusion, Transformer | Technical evolution of generative AI | [70] |
Generative AI in Healthcare | Clinical documentation, diagnostics | BioGPT, GatorTronGPT, ClinicalBERT | Multimodal applications, LLM integration | [71] |
ICN Conference Papers | Image classification, EEG-based detection | CNN, SVM, Decision Trees | Use of medical datasets like HAM10000 | [72] |
Multilingual LLMs with LoRA | Chatbot, smart cities | BLOOM-7B1 with LoRA + DeepSpeed | Synthetic dataset creation with prompting | [73] |
Medical VQA with Generative Models | Visual Question Answering | Transformer-based generative models | Image-text integration in medical domain | [74,82] |
Recent Advances in Medical LLMs | Summarization, clinical assistant | BERT, GPT-3, PaLM, LLaMA | Pretraining on MIMIC-III and other datasets | [75] |
Clinical LLMs in Mental Health | Psychiatry, psychotherapy support | ChatGPT, BERT-based | Taxonomy of chatbot evaluation, AI-in-the-loop therapy | [83,84] |
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Tasmurzayev, N.; Amangeldy, B.; Imanbek, B.; Baigarayeva, Z.; Imankulov, T.; Dikhanbayeva, G.; Amangeldi, I.; Sharipova, S. Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive Heart Health. Sensors 2025, 25, 5272. https://doi.org/10.3390/s25175272
Tasmurzayev N, Amangeldy B, Imanbek B, Baigarayeva Z, Imankulov T, Dikhanbayeva G, Amangeldi I, Sharipova S. Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive Heart Health. Sensors. 2025; 25(17):5272. https://doi.org/10.3390/s25175272
Chicago/Turabian StyleTasmurzayev, Nurdaulet, Bibars Amangeldy, Baglan Imanbek, Zhanel Baigarayeva, Timur Imankulov, Gulmira Dikhanbayeva, Inzhu Amangeldi, and Symbat Sharipova. 2025. "Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive Heart Health" Sensors 25, no. 17: 5272. https://doi.org/10.3390/s25175272
APA StyleTasmurzayev, N., Amangeldy, B., Imanbek, B., Baigarayeva, Z., Imankulov, T., Dikhanbayeva, G., Amangeldi, I., & Sharipova, S. (2025). Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive Heart Health. Sensors, 25(17), 5272. https://doi.org/10.3390/s25175272