Multi-Modality Medical Image Recommendation Engine
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Solution Overview
Problem
Conventional medical imaging systems store images from different modalities in separate systems, making it difficult to diagnose and treat medical conditions using multiple images effectively.
Innovation Solution
A computer-implemented method that maps regions of interest from multiple medical images across different imaging modalities, generates annotation and clinical data, and uses a machine learning classifier to determine medical recommendations, including diagnosis, treatment, and research suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If medical images from different modalities are stored in separate systems, then each system can maintain its own data structure and processing workflow, but it becomes difficult to integrate and analyze multiple images for diagnosis and treatment
Solution Approach 1:
The patent merges multiple separate medical imaging systems into a unified system that can store, retrieve, and process images from different modalities (CT, MRI, ultrasound, etc.) together. The unified system integrates disparate data sources while maintaining their individual characteristics, enabling comprehensive analysis for diagnosis and treatment planning.
Solution Approach 2:
The system is designed with universal capabilities to handle multiple imaging modalities through a common interface and processing framework. It can accommodate various image types, annotation formats, and data structures while providing consistent functionality for integration, analysis, and medical recommendation generation.
2Reliability
If multiple medical images are integrated into a single system, then comprehensive analysis for diagnosis and treatment becomes possible, but the system complexity and data management burden increase
Solution Approach 1:
The system segments the complex task of multi-modal image integration into manageable components: image acquisition modules, annotation generation modules, feature extraction modules, and analysis modules. Each component handles specific aspects of the data pipeline, reducing overall system complexity while enabling comprehensive diagnostic analysis.
Solution Approach 2:
The system introduces intermediary elements such as standardized data formats, mapping mechanisms between different imaging coordinate systems, and intermediate representation layers that facilitate integration of diverse modalities. These intermediaries simplify data management by providing uniform interfaces between heterogeneous sources.
3Measurement precision
If manual annotation and analysis of multiple medical images is performed, then detailed examination of each image is possible, but the time and resources required increase significantly
Solution Approach 1:
The system performs preliminary automated actions including pre-segmentation of anatomical structures, pre-identification of potential regions of interest, and pre-alignment of multiple images. These preliminary steps reduce the manual workload required for detailed analysis while maintaining high precision in region identification.
Solution Approach 2:
The system implements feedback mechanisms where automated analysis results are refined through iterative processes. Initial automated annotations provide starting points that are refined based on feedback from multiple imaging modalities and clinical data, improving precision while reducing the time required compared to purely manual methods.
Data Source
AI summary
Automatically determining a medical recommendation for a patient based on multiple medical images from multiple different medical imaging modalities. In some embodiments, a method may include receiving a first and second medical images of a patient from first and second medical imaging modalities, mapping a first region of interest (ROI) on the first medical image to a second ROI on the second medical image, generating first annotation data related to the first ROI and second annotation data related to the second ROI, generating first medical clinical data related to the first ROI and second medical clinical data related to the second ROI, inputting, into a machine learning classifier, the first and second annotation data and the first and second medical clinical data, and automatically determining, by the machine learning classifier, a medical recommendation for the patient related to a medical condition of the patient.