Portable AI Screening Device for Early Breast Cancer Detection
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Solution Overview
Problem
Current breast cancer screening methods, such as mammography, are invasive, irradiating, and not sensitive, leading to late detection and high false positives, while existing non-invasive methods are expensive and complex, making early cancer screening inaccessible due to cost and complexity.
Innovation Solution
A portable, non-invasive, and non-irradiating cancer screening system using artificial intelligence (AI) and chemically sensitive sensors to analyze urine samples, detecting volatile organic compounds (VOCs) for early breast cancer detection, which can be used at home or in clinical settings without the need for trained personnel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mammography is used for breast cancer screening, then early detection capability is improved, but radiation exposure and pain increase
Solution Approach 1:
The patent replaces the mechanical/radiation-based mammography system with a chemical sensing system using electronic noses and sensors that detect volatile organic compounds in breath or urine samples. This substitution eliminates radiation exposure and physical discomfort while maintaining early detection capability through chemical biomarker analysis.
Solution Approach 2:
The patent introduces volatile organic compounds (VOCs) as intermediary substances that carry information about cancer presence. Instead of directly imaging breast tissue with radiation, the system detects VOCs in breath or urine that are produced by cancer metabolism, providing an indirect but safe detection method.
2Measurement precision
If MRI or echography is used for breast cancer screening, then detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent employs relatively simple, inexpensive sensor arrays and portable electronic nose devices instead of complex, expensive MRI or echography equipment. These simpler devices can be deployed more widely and require less infrastructure, reducing both device complexity and operational costs while maintaining effective detection capability.
3Loss of information
If image-based techniques are used for breast cancer screening, then visualization capability is improved, but ability to detect tumors in dense tissue worsens
Solution Approach 1:
The patent changes the detection parameter from physical imaging (light waves, sound waves) to chemical composition analysis. By measuring the chemical signature of VOCs in breath or urine samples, the system bypasses the problem of tissue density interference that plagues image-based techniques, as chemical analysis is not affected by physical tissue properties.
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
The system provides a low-cost, pain-free, and highly sensitive method for early breast cancer detection with over 95% accuracy for metastatic breast cancer, reducing the severity of breast cancer through early-stage diagnosis and reducing healthcare expenses.
Implementation Method 1
a set of chemically sensitive sensors allocated in an analysis chamber of the portable medical screening device, the set of chemically sensitive sensors being adapted to detect volatile organic compounds (VOCs) in the test sample
Data Source
AI summary
A system, a method and a device for screening a disease in a subject are provided. The system comprises a portable medical screening device including a collection chamber to collect a test sample of a subject; a set of chemically sensitive sensors; an analysis chamber to allocate the set of chemically sensitive sensors to detect VOCs in the test sample and to generate an output signal indicative of the presence or absence of VOC in the test sample; and a first processing unit operatively connected to the set of chemically sensitive sensors to receive the generated output signal. The system also comprises an artificial intelligence-based classification software to determine an outcome regarding the disease by processing and classifying the generated output signal. The classification software can be executed by the first processing unit, a software application installed on a computer device or a remote processing unit.


