Laser Spectroscopy Device for In Vivo Lesion Detection
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Solution Overview
Problem
Existing laser spectroscopy technologies for in-vivo disease diagnosis lack accuracy in detecting lesion tissue as they rely on threshold values in specific wavelength regions, failing to guarantee precise detection.
Innovation Solution
A laser spectroscopy-based device that incorporates a spectrometer for non-discrete spectrum measurement and a machine learning-based method for lesion tissue detection, using a lesion tissue detection learning model to analyze continuous spectrum data from the time a laser is projected onto a sample, including preprocessing steps like normalization and principle component analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If threshold-based detection in specific wavelength regions is used, then the device complexity is reduced, but the measurement precision deteriorates
Solution Approach 1:
The patent changes the detection parameter from discrete threshold values in specific wavelength regions to continuous spectrum analysis across all wavelengths. The spectrometer measures the entire spectrum of generated light, and machine learning models process this comprehensive spectral data to detect lesion tissue, thereby improving measurement precision without significantly increasing device complexity
Solution Approach 2:
The patent introduces machine learning models as an intermediary between the spectrometer and the detection decision. These models process the continuous spectrum data and identify patterns that indicate lesion tissue, enabling accurate detection while maintaining relatively simple hardware architecture
2Measurement precision
If non-discrete spectrum measurement of all generated light is performed, then the measurement precision is improved, but the loss of time increases
Solution Approach 1:
The patent applies preprocessing operations (normalization, principle component analysis) to the spectrum data before classification. This preliminary processing reduces the dimensionality and complexity of the data, enabling faster machine learning inference while maintaining the benefits of comprehensive non-discrete spectrum measurement
Solution Approach 2:
The patent replaces traditional mechanical or manual analysis methods with machine learning-based automated classification. This substitution enables rapid processing of continuous spectrum data, reducing the time penalty associated with comprehensive measurement
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
This approach enables more accurate detection of lesions by analyzing all generated light spectra, supporting both in-vivo and ex-vivo applications, and improves diagnostic accuracy beyond threshold-based methods.
Implementation Method 1
a spectrometer configured to measure a spectrum of generated light which is generated by a laser projected onto a sample
Data Source
AI summary
According to an embodiment of the present disclosure, there is provided a laser spectroscopy-based independent device, including: a spectrometer configured to measure a spectrum of generated light which is generated by a laser projected onto a sample; and a disease analysis module configured to determine whether there is lesion tissue by applying a lesion tissue detection learning model to a result of non-discrete spectrum measurement, which is measured by the spectrometer, wherein the spectrometer is configured to measure spectra of all generated light that is generated from a time when the laser is projected onto the sample.