Healthcare
Federated task-adaptive learning for personalized selection of human IVF-derived embryos
This research introduces FedEmbryo, a distributed AI system for In-vitro fertilization (IVF) to improve embryo selection while preserving patient data privacy. It employs a federated task-adaptive learning (FTAL) approach with a hierarchical dynamic weighting adaptation (HDWA) mechanism to handle diverse tasks and data heterogeneity across multiple clinical sites. Experiments show FedEmbryo outperforms local models and state-of-the-art federated learning methods in morphological evaluation and live-birth outcome prediction, making it a potential practical tool for enhancing IVF clinical decision-making.
Key Metrics of Impact
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Improved AUC for live-birth prediction (internal test sets)
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Improved AUC for live-birth prediction (external test sets)
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Average improvement in blastocyst formation prediction (AUC)
Deep Analysis & Enterprise Applications
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FedEmbryo Architecture
Performance Comparison
Interpretability of AI Predictions
Privacy Preservation
FedEmbryo uses a federated task-adaptive learning (FTAL) approach with a hierarchical dynamic weighting adaptation (HDWA) mechanism to balance task attention and client aggregation, enabling simultaneous learning of diverse tasks while mitigating heterogeneity.
Enterprise Process Flow
Client Local Model Training
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Loss Ratios Feedback
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Server Aggregation (HDWA)
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Updated Global Model Redistribution
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Enhanced Embryo Selection
FedEmbryo consistently outperforms local models and state-of-the-art federated learning methods across various morphological assessment tasks and live-birth predictions, demonstrating superior robustness against data heterogeneity.
| Method | Key Advantages |
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| Local Model |
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| FedAvg/FedProx/FedDWA |
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| FedEmbryo |
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FedEmbryo's use of integrated gradients (IG) and SHAP values provides clinicians with visual explanations of AI predictions, highlighting key morphological features and clinical factors influencing live-birth outcomes, fostering trust and adaptability.
Enhancing Clinician Trust with Explainable AI
Challenge: Clinicians often view AI as a 'black box,' hindering adoption. Understanding the AI's reasoning for embryo selection is crucial for trust and integration into clinical practice.
Solution: FedEmbryo integrates IG and SHAP methods to generate saliency maps for embryo images and quantify the impact of clinical factors. This allows clinicians to visualize which specific embryo features (e.g., nuclei, cell symmetry) and patient data (e.g., maternal age) are most influential in predictions.
Impact: By providing clear visual and quantitative explanations, FedEmbryo enables clinicians to validate AI decisions against their expertise, identify overlooked areas, and gain confidence in the system. This transparency significantly enhances trust and facilitates AI integration into IVF decision-making processes.
FedEmbryo addresses privacy concerns by utilizing federated learning, allowing multiple clinical sites to collaboratively train models without sharing raw patient data, thus complying with regulations like HIPAA.
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Data Privacy Preserved
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Projected Annual Savings
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