Breast Wearable Impedance Sensing for Lower False Cancer Detection
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Solution Overview
Problem
Current breast cancer detection methods, such as mammography, suffer from high false negative and false positive errors.
Innovation Solution
A wearable electrode-based sensor system that includes an electrode array, power source, signal generator, detector, and machine learning logic for semi-continuous and long-term detection of breast cancer, utilizing electrical impedance measurements and machine learning algorithms to analyze tissue characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mammography is used for breast cancer detection, then cancer can be detected, but false negative and false positive errors occur
Solution Approach 1:
The breast tissue is divided into multiple regions with electrode arrays placed at different locations (e.g., upper outer quadrant, lower inner quadrant). Each electrode pair measures impedance in specific tissue segments, allowing localized detection and reducing false positives by identifying precise abnormal regions rather than whole-breast screening
Solution Approach 2:
Electrical impedance measurements serve as an intermediary parameter between physical tissue properties and cancer detection. The system measures impedance magnitude and phase angle as intermediate indicators that reflect tissue composition changes, providing additional diagnostic information beyond traditional mammography
2Reliability
If wearable sensor system is used for semi-continuous detection, then detection reliability improves, but device complexity increases
Solution Approach 1:
The wearable device integrates multiple functions into a single system: electrode arrays for impedance measurement, temperature sensors for thermal monitoring, machine learning processing for data analysis, and wireless communication for data transfer. This multi-functional integration reduces the need for multiple separate devices while maintaining detection reliability
Solution Approach 2:
The system includes machine learning logic that automatically processes sensor data, identifies patterns indicating cancer, and generates detection results without requiring constant manual intervention. The device performs self-calibration and adaptive learning to improve accuracy over time, reducing operational complexity
3Measurement precision
If multiple sensors are combined for comprehensive detection, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent combines electrical impedance sensing, temperature monitoring, and machine learning analysis into a single integrated wearable device. Multiple electrode arrays are merged with temperature sensors and processing units to create a unified system that simultaneously captures multiple physiological parameters, improving tissue characterization without requiring separate devices
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides accurate and reliable detection of breast cancer by analyzing tissue impedance over time, reducing false positives and negatives through machine learning-based analysis.
Implementation Method 1
utilizing electrical impedance measurements and machine learning algorithms to analyze tissue characteristics
Data Source
AI summary
A series of electrodes and a machine learning system are configured to detect changes in biological tissue over time. Specifically, the system may include a wearable device configured to detect changes in tissue response to an electrical signal that may be indicative of cancerous tissue, e.g., breast cancer and/or other types of cancer. The system optionally combines measurements from bioimpedance sensors, miniaturized ultrasound arrays, temperature sensors, and/or printed microwave planar antenna to detect changes in breast tissue composition and vascularity.