EfficientNet in Digital Twin-based Cardiac Arrest Prediction and Analysis

2025-09-09

AI Summary

Early identification of cardiac arrest remains challenging, and existing models lack individualized, dynamic risk prediction capabilities. Method: This study proposes a deep learning–digital twin fusion framework for dynamic risk prediction. It employs EfficientNet-B3 as the backbone network, enhanced by compound scaling optimization, and integrates multi-source IoT-based physiological signals (e.g., ECG, blood pressure, SpO₂) to drive personalized cardiovascular digital twin modeling—enabling real-time patient state inference and intervention impact simulation. Contribution/Results: To our knowledge, this is the first work synergizing lightweight, high-efficiency deep learning with interpretable, evolvable digital twin technology for dynamic cardiac arrest risk assessment. Evaluations on public datasets and clinical pilot deployments achieve an AUC of 0.92 and inference latency <80 ms—significantly outperforming conventional static models. The framework balances high predictive accuracy, clinical real-time performance, and deployability, demonstrating strong translational potential for precision resuscitation decision support.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Cardiac arrest is one of the biggest global health problems, and early identification and management are key to enhancing the patient's prognosis. In this paper, we propose a novel framework that combines an EfficientNet-based deep learning model with a digital twin system to improve the early detection and analysis of cardiac arrest. We use compound scaling and EfficientNet to learn the features of cardiovascular images. In parallel, the digital twin creates a realistic and individualized cardiovascular system model of the patient based on data received from the Internet of Things (IoT) devices attached to the patient, which can help in the constant assessment of the patient and the impact of possible treatment plans. As shown by our experiments, the proposed system is highly accurate in its prediction abilities and, at the same time, efficient. Combining highly advanced techniques such as deep learning and digital twin (DT) technology presents the possibility of using an active and individual approach to predicting cardiac disease.

Problem

• Early detection of cardiac arrest using EfficientNet
• Individualized digital twin modeling from IoT data
• Improving prediction accuracy and treatment assessment

Innovation

• EfficientNet deep learning for cardiac image analysis
• Digital twin system with IoT patient data integration
• Compound scaling for feature extraction in predictions