VolodymyrPugachov
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Digital Heart Model: Initial Research Launch 🚀
I am excited to announce the launch of research on the Digital Heart Model (DHM), an AI-driven digital twin designed to transform personalized cardiovascular care. DHM integrates multimodal data, focusing initially on cardiac imaging, histopathological imaging, and ECG data, to predict patient outcomes and optimize interventions.
Initial Model and Dataset Overview:
Base Model: Multimodal AI foundation combining Convolutional Neural Networks (CNN), Vision Transformers (ViT), and Graph Neural Networks (GNN).
Datasets: Cardiac MRI and CT imaging datasets, histopathological cardiac tissue images, and extensive ECG waveform data.
Expected Results from First Iteration:
Cardiac event prediction (e.g., myocardial infarction) accuracy: AUC ≥ 0.90
Arrhythmia detection and classification accuracy: AUC ≥ 0.88
Enhanced segmentation accuracy for cardiac imaging: Dice Score ≥ 0.85
🔍 Next Steps:
Conducting initial retrospective validation.
Preparing for prospective clinical validation.
Stay tuned for updates as we redefine cardiovascular precision medicine!
Connect with us for collaboration and insights!
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11 days ago
Digital Heart Model: Initial Research Launch 🚀
I am excited to announce the launch of research on the Digital Heart Model (DHM), an AI-driven digital twin designed to transform personalized cardiovascular care. DHM integrates multimodal data, focusing initially on cardiac imaging, histopathological imaging, and ECG data, to predict patient outcomes and optimize interventions.
Initial Model and Dataset Overview:
Base Model: Multimodal AI foundation combining Convolutional Neural Networks (CNN), Vision Transformers (ViT), and Graph Neural Networks (GNN).
Datasets: Cardiac MRI and CT imaging datasets, histopathological cardiac tissue images, and extensive ECG waveform data.
Expected Results from First Iteration:
Cardiac event prediction (e.g., myocardial infarction) accuracy: AUC ≥ 0.90
Arrhythmia detection and classification accuracy: AUC ≥ 0.88
Enhanced segmentation accuracy for cardiac imaging: Dice Score ≥ 0.85
🔍 Next Steps:
Conducting initial retrospective validation.
Preparing for prospective clinical validation.
Stay tuned for updates as we redefine cardiovascular precision medicine!
Connect with us for collaboration and insights!
replied to
their
post
11 days ago
Digital Heart Model: Initial Research Launch 🚀
I am excited to announce the launch of research on the Digital Heart Model (DHM), an AI-driven digital twin designed to transform personalized cardiovascular care. DHM integrates multimodal data, focusing initially on cardiac imaging, histopathological imaging, and ECG data, to predict patient outcomes and optimize interventions.
Initial Model and Dataset Overview:
Base Model: Multimodal AI foundation combining Convolutional Neural Networks (CNN), Vision Transformers (ViT), and Graph Neural Networks (GNN).
Datasets: Cardiac MRI and CT imaging datasets, histopathological cardiac tissue images, and extensive ECG waveform data.
Expected Results from First Iteration:
Cardiac event prediction (e.g., myocardial infarction) accuracy: AUC ≥ 0.90
Arrhythmia detection and classification accuracy: AUC ≥ 0.88
Enhanced segmentation accuracy for cardiac imaging: Dice Score ≥ 0.85
🔍 Next Steps:
Conducting initial retrospective validation.
Preparing for prospective clinical validation.
Stay tuned for updates as we redefine cardiovascular precision medicine!
Connect with us for collaboration and insights!
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Digital Heart Model: Initial Research Launch 🚀 I am excited to announce the launch of research on the Digital Heart Model (DHM), an AI-driven digital twin designed to transform personalized cardiovascular care. DHM integrates multimodal data, focusing initially on cardiac imaging, histopathological imaging, and ECG data, to predict patient outcomes and optimize interventions. Initial Model and Dataset Overview: Base Model: Multimodal AI foundation combining Convolutional Neural Networks (CNN), Vision Transformers (ViT), and Graph Neural Networks (GNN). Datasets: Cardiac MRI and CT imaging datasets, histopathological cardiac tissue images, and extensive ECG waveform data. Expected Results from First Iteration: Cardiac event prediction (e.g., myocardial infarction) accuracy: AUC ≥ 0.90 Arrhythmia detection and classification accuracy: AUC ≥ 0.88 Enhanced segmentation accuracy for cardiac imaging: Dice Score ≥ 0.85 🔍 Next Steps: Conducting initial retrospective validation. Preparing for prospective clinical validation. Stay tuned for updates as we redefine cardiovascular precision medicine! Connect with us for collaboration and insights!
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