Tissue Diffractometer Diagnostics for Early Breast Cancer Detection
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Solution Overview
Problem
Absorptive imaging-based mammography techniques for breast cancer detection suffer from poor contrast and diagnostic challenges, necessitating improved in situ diagnostic tools for early detection and diagnosis.
Innovation Solution
A system comprising tissue diffractometers operatively coupled to a computer database over a network, using data analytics algorithms to process diffraction pattern and image data for providing computer-aided diagnostic indicators, potentially integrated with mammography and employing machine learning algorithms for enhanced diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If absorptive imaging-based mammography techniques are used for breast cancer detection, then the diagnostic tool is widely available and easy to operate, but the contrast is poor and diagnostic accuracy is reduced
Solution Approach 1:
The patent combines multiple diagnostic modalities (mammography, ultrasound, MRI, genetic testing, pathological analysis) into a single integrated diagnostic system. This merging of different imaging and analysis techniques allows the system to overcome the poor contrast limitation of mammography alone while maintaining wide availability and ease of operation through standardized interfaces and centralized processing.
Solution Approach 2:
The diagnostic system is designed to perform multiple functions including imaging, data acquisition, pattern recognition, and diagnostic indicator generation across different modalities. This multi-functionality enables the system to provide comprehensive cancer detection capabilities while maintaining a unified operational interface, thus improving diagnostic accuracy without proportionally increasing operational complexity.
2Measurement precision
If advanced data analytics algorithms and machine learning are employed to process diffraction pattern data, then diagnostic precision is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary data processing, feature extraction, and pattern recognition on diffraction patterns and imaging data before final diagnostic analysis. By pre-processing data to extract relevant features and reduce dimensionality, the system reduces the computational burden on machine learning algorithms while maintaining high diagnostic precision through focused analysis of key diagnostic indicators.
Solution Approach 2:
The patent introduces intermediate processing layers including data normalization, feature extraction modules, and pattern matching algorithms that act as intermediaries between raw data and final diagnostic conclusions. These intermediary processing steps simplify the computational complexity by transforming complex raw data into standardized features that are more efficient for machine learning algorithms to process while preserving diagnostic information.
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
Enhances the accuracy of early cancer detection by providing computer-aided diagnostic indicators through advanced data processing and analysis, improving diagnostic precision and efficacy.
Implementation Method 1
one or more tissue diffractometers operatively coupled to a computer database over a network, wherein a tissue diffractometer of the one or more tissue diffractometers is configured for acquisition and transfer of in situ image data, in situ diffraction pattern data
Implementation Method 2
In some embodiments, the one or more tissue diffractometers are configured to perform small angle X-ray scattering (SAXS) measurements
Implementation Method 3
In some embodiments, the one or more tissue diffractometers are configured to perform wide angle X-ray scattering (WAXS) measurements
Data Source
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Figure 3A~3B
AI summary
Provided herein are diffractometer-based global in situ diagnostic systems and uses thereof. The systems may comprise one or more tissue diffractometers that are configured for acquiring in situ diffraction data for a subject, e.g., a patient, and that are operatively coupled to a computer database over a network. The one or more tissue diffractometers may be configured for transfer of data such as image data, diffraction pattern data, subject data, or any combination thereof to the computer database over the network. The systems may further comprise one or more computer processors operatively coupled to the tissue diffractometers, which computer processors may be configured to receive the data from the tissue diffractometers, transmit the data to the computer database, and process the data using a data analytics algorithm which may provide a computer-aided diagnostic indicator for the individual subject.