Enterprise AI Analysis
IoMT-Driven Alzheimer's Prediction with TL & XAI for Healthcare 5.0
This study proposes an innovative IoMT-driven Alzheimer's prediction framework, combining transfer learning (ResNet152) with explainable AI (XAI) to achieve high accuracy and interpretability. Leveraging a publicly available Kaggle MRI dataset and Conditional Wasserstein GAN for class balancing, the model demonstrates strong potential for early, accurate, and transparent Alzheimer's staging in patient-centric Healthcare 5.0 ecosystems.
Quantifiable Impact on Healthcare Operations
Our framework delivers unprecedented accuracy and interpretability, translating directly into improved patient outcomes and operational efficiency for healthcare providers.
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
IoMT-Enabled Data Acquisition & Monitoring
The Internet of Medical Things (IoMT) collects and transmits health data in real-time, enabling patient-centric, real-time monitoring and data-driven diagnosis for Alzheimer's. Devices monitor behavioral patterns, physiological signals, and cognitive functions, facilitating early diagnosis and continuous patient monitoring within Healthcare 5.0 ecosystems.
Transfer Learning with ResNet152
Leveraging pre-trained ResNet152 models on large datasets significantly accelerates training, reduces the need for extensive labeled data, and enhances prediction accuracy for Alzheimer's staging. This approach focuses on fine-tuning deeper layers to capture disease-specific structural patterns while preserving low-level features.
Interpretable AI with XAI Techniques
Integrating multi-method XAI (Grad-CAM, SHAP, LIME, LRP) provides transparent, interpretable insights into the model's decision-making. These techniques highlight clinically relevant brain regions like the Hippocampus and ventricles, increasing clinician trust and validating biological plausibility.
Advanced Data Preprocessing & Augmentation
To address class imbalance, Conditional Wasserstein GAN (WGAN-GP) augmentation was applied to the training set, generating synthetic images conditioned on AD stages. This created a balanced dataset while maintaining anatomical reliability and preventing data leakage from synthetic samples.
97.77%
Overall Prediction Accuracy Achieved
Our IoMT-driven framework achieves state-of-the-art diagnostic performance, ensuring precise and reliable Alzheimer's staging, critical for early intervention in Healthcare 5.0.
Enterprise Process Flow
Data Acquisition
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Preprocessing (WGAN-GP)
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ResNet152-TL Training
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XAI Interpretation (Grad-CAM, SHAP, LIME, LRP)
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Prediction & Clinical Decision Support
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IoMT Deployment & Continuous Monitoring
| Model/Approach | Key Features/Methods | Accuracy (%) | Advantages of Our Approach |
|---|---|---|---|
| LSTM [34] (2022) | RNN, LSTM | 88.24% |
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| AlexNet [41] (2022) | CNN, TL (last 3 layers) | 91.7% |
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| DenseNet201 [43] (2024) | DL (VGG, DenseNet) | 96% |
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| 3D-CNN [45] (2025) | 3D-CNN, DAG | 96.86% |
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| Proposed ResNet152-TL-XAI Model | IoMT Integration, ResNet152-TL, XAI (Grad-CAM, SHAP, LIME, LRP), WGAN-GP | 97.77% |
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IoMT-Driven Precision: A New Era in Alzheimer's Management
Our framework integrates IoMT devices for real-time patient data acquisition, ResNet152 transfer learning for efficient and accurate prediction, and multi-method Explainable AI for transparent decision-making. This enables early, accurate, and interpretable Alzheimer's staging, supporting timely diagnosis, personalized interventions, and continuous patient monitoring within a Healthcare 5.0 ecosystem. Clinicians gain actionable insights and enhanced trust, moving beyond traditional, episodic care to a proactive, patient-centric model.
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Your AI Implementation Roadmap
A structured approach to integrating IoMT-driven AI for Alzheimer's prediction into your healthcare workflow.
Phase 1: Discovery & Data Infrastructure Setup
Assess current data sources, define IoMT device integration points, and establish secure cloud infrastructure for real-time data acquisition and storage, adhering to HIPAA/GDPR.
Phase 2: Model Adaptation & XAI Framework Development
Fine-tune ResNet152 with your specific datasets, implement WGAN-GP for robust class balancing, and integrate multi-method XAI (Grad-CAM, SHAP, LIME, LRP) for interpretability.
Phase 3: Clinical Validation & Regulatory Alignment
Conduct rigorous clinical validation using diverse, multi-site patient cohorts, obtain necessary regulatory approvals, and integrate the system with existing EHR/FHIR systems for seamless workflow.
Phase 4: Scalable Deployment & Continuous Monitoring
Deploy the IoMT-AI framework in a scalable, secure environment, enable real-time patient monitoring dashboards for clinicians, and establish MLOps for continuous model optimization and drift detection.
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