Cardiac CT Foundation Model Using Causal Patient-Level Representations
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Solution Overview
Problem
Existing medical imaging analysis, particularly in cardiac CT scans, faces challenges such as inter-observer variability, data scarcity, image quality issues, and the need for large, diverse training datasets, which limits the performance of AI models and introduces complexity in multi-modal data integration and interpretation.
Innovation Solution
A whole medical image foundation model is trained using self-supervised learning models and deep learning networks to combine local image data sections into patient-level representations, incorporating causal variables and unsupervised clustering, enabling robust prediction tasks and handling variations in image acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation of cardiac CT scans is performed by radiologists and cardiologists, then diagnostic expertise and anatomical understanding are applied, but inter-observer variability and time consumption increase
Solution Approach 1:
The patent replaces the manual mechanical interpretation process with an automated AI system that uses deep learning models to analyze cardiac CT images. The system automatically performs image segmentation, feature extraction, and disease classification, eliminating the need for manual review while maintaining diagnostic accuracy.
Solution Approach 2:
The patent introduces an AI-based image analysis system as an intermediary between image acquisition and clinical decision-making. This intermediary process automates the interpretation workflow, providing consistent and reproducible results without the variability inherent in human observation.
2Reliability
If large, diverse training datasets are used to train AI models for medical imaging, then model robustness and generalization improve, but data availability is constrained by patient privacy and regulatory requirements
Solution Approach 1:
The patent uses synthetic data generation techniques to create artificial training datasets that mimic real medical imaging data. By generating synthetic cardiac CT images with various pathologies and anatomical variations, the system can train robust AI models without requiring access to large volumes of actual patient data, thus preserving patient privacy while improving model reliability.
3Adaptability or versatility
If multiple imaging modalities and clinical data sources are integrated, then comprehensive patient assessment is achieved, but data processing complexity and alignment challenges increase
Solution Approach 1:
The patent divides the multi-modal data integration process into separate modular components, each handling specific data types (e.g., imaging data, clinical data, laboratory results). Each module processes its designated data type independently using specialized algorithms, then the results are integrated at a higher level, reducing overall system complexity while maintaining comprehensive assessment capabilities.
Data Source
AI summary
A computer implemented method for training a whole medical image foundation model, including: receiving a plurality of medical image datasets; extracting local sections of image data from the plurality of medical image datasets; obtaining one or more causal variables associated with the local sections and/or patient; training one or more self-supervised learning models based on the local sections of image data and the causal variables; combining the one or more trained self-supervised learning models with a deep learning network configured to combine a latent representation of the local sections of image data from the one or more trained self-supervised learning models into a patient-level representation; and combining, with the one or more trained self-supervised learning models and the deep learning network, at least one further network or function configured to accept the patient-level representation as input, the at least one further network or function operable to perform one or more patient-specific prediction tasks.