Key result
Multimodal deep learning algorithms, such as the DEEP-CARDIO BiGRU-Attention model, achieved up to 99.9% predictive accuracy for cardiovascular disease detection and risk forecasting.
Why the study?
Cardiovascular disease poses a major global health threat requiring timely risk analysis, while emerging multimodal data modalities have shifted paradigms toward integrated CVD risk assessment.
Do multimodal deep-learning algorithms accurately detect and forecast risk for cardiovascular disease?
Systematic Review (n=69)
Do multimodal deep-learning algorithms accurately detect and forecast risk for cardiovascular disease?
Multimodal deep learning algorithms, particularly attention-based and hybrid models, demonstrate exceptionally high accuracy (often >98%) for cardiovascular disease detection and risk forecasting.
Multimodal deep learning models achieve high CVD risk prediction accuracy; extends prior evidence but leaves open need for prospective validation before practice change.
The biggest health threat to the global population is cardiovascular disease (CVD), which afflicts almost one-third of the global population and causes considerable monetary and social losses. Risk analysis should be performed in a timely and appropriate manner to enhance clinical practice and preventive interventions. The emergence of advanced data modalities, such as wearable sensors, medical imaging, electronic health records (EHRs), and genomic platforms, has led to a paradigm shift in the holistic assessment of CVD risk through multimodal data integration. This systematic review is a methodological analysis of recent multimodal input deep-learning algorithms that enhance the early detection of CVD and risk-specific evaluation, following PRISMA 2020 guidelines across 69 studies published 20,122,025 based on 2,847 initial database records. We characterized the wide range of available data streams: longitudinal physiological measurements (ECG, HRV, and BP), echocardiogram data, cardiac MRI and CT, lab/demographic data, behavioral/environmental data, and genomic/proteomic data. Mid-level, early, late, and attention-based fusion methods are described in the context of deep neural networks, such as CNNs, RNNs, BiGRU with attention, and hybrid CNN-LSTM networks. Comparative studies showed dramatic improvements in predictive accuracy (often over 98%), strength to missing or noisy modalities, and access to real-time, individualized recommendations. The best-performing DEEP-CARDIO BiGRU-Attention model had 99.9 percent accuracy on Framingham and Statlog benchmarks. A systematic review of 28 studies by Grad-CAM and SHAP confirmed the dominance of each in imaging and structured-data tasks, respectively (Rahman et al., 2024). The federated explainable FL-LSTM model achieved 99% AUC across three ECG databanks with complete privacy protection. We end with a systematic reproducibility, federated learning, equitable AI, and regulatory translation roadmap.
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Ganeshan et al. (2026) conducted a systematic review in Cardiovascular disease (n=69). Multimodal deep learning algorithms vs. Traditional risk models was evaluated on Predictive accuracy. Multimodal deep learning algorithms, such as the DEEP-CARDIO BiGRU-Attention model, achieved up to 99.9% predictive accuracy for cardiovascular disease detection and risk forecasting.
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