Breast Image and Genetic Risk Modeling for Multi-Organ Assessment
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Solution Overview
Problem
Current cancer detection methods suffer from high false positives and negatives, and there is a need for a comprehensive health assessment system to determine cancer risk and related risks to other organs, particularly in women with low estimated cardiovascular disease risk.
Innovation Solution
A system utilizing AI/machine learning models trained with patient data to analyze breast images and genetic data, determining breast cancer risk and risks to other organs, including heart, kidney, lungs, pancreas, and brain, through a multi-layered approach that includes feature extraction, pattern recognition, and real-time feedback loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI/machine learning models are used to analyze breast images and genetic data, then measurement precision of cancer risk assessment is improved, but device complexity increases
Solution Approach 1:
The system divides the comprehensive health assessment into multiple specialized machine learning models, each trained for specific organ risk assessment (breast cancer, cardiovascular disease, lung cancer, etc.). This segmentation allows each model to focus on specific patterns and data types, improving measurement precision while managing complexity through modular architecture.
Solution Approach 2:
The system creates a universal AI platform that handles multiple functions: analyzing breast images, processing genetic data, assessing cancer risks, evaluating cardiovascular risks, and predicting treatment responses. This multi-functional approach consolidates what would otherwise require separate systems, managing device complexity while providing comprehensive assessment.
2Reliability
If comprehensive multi-organ risk assessment is provided, then reliability of health evaluation is improved, but loss of time in processing increases
Solution Approach 1:
The machine learning models are pre-trained on extensive datasets containing breast images, genetic data, and clinical outcomes from multiple patients. This preliminary training establishes baseline risk assessments and pattern recognition capabilities, allowing the system to provide comprehensive multi-organ risk evaluation without requiring time-consuming analysis during actual patient assessment.
Solution Approach 2:
The system processes multiple types of data (breast images, genetic data, clinical history) and assesses multiple organs simultaneously through parallel machine learning model execution. This continuous processing of diverse data streams in an integrated manner ensures comprehensive reliability while minimizing processing time through efficient concurrent operations.
3Measurement precision
If detailed feature extraction from images and genetic data is performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system introduces specialized machine learning models as intermediary components between raw data (breast images, genetic data) and risk assessment outputs. These intermediary models perform automated feature extraction, identifying relevant patterns and characteristics without requiring manual analysis, thereby improving measurement precision while managing processing complexity through algorithmic mediation.
Data Source
AI summary
According to an embodiment, disclosed is a system comprising a processor configured to receive an image of a breast of a patient and patient data comprising genetic data; extract features from the image and the patient data, using one or more machine learning models, wherein the features comprise a presence of a calcification and a calcification pattern to generate a breast calcification vector; augment the breast calcification vector with the genetic data; determine, using the machine learning models, a first risk for a breast cancer; a second risk to one or more organs of the patient, wherein the organs comprises one or more of heart, kidney, lungs, pancreas, and brain; predict, a third risk based on one or more of a healing response, a tumor flow and growth, inflammation and degeneration, a disease relapse, an adverse event, and a clinical response; and determine, an overall risk to the patient.