LIBS Cancer Diagnosis with Machine Learning and Copper Substrate
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Solution Overview
Problem
Current methods for diagnosing and monitoring cancer, particularly in its early stages, are inadequate due to the lack of noninvasive, rapid, and accurate screening tests, especially for forms like epithelial ovarian cancer, pancreatic cancer, and melanoma, which often go unnoticed until they metastasize.
Innovation Solution
The integration of Laser-Induced Breakdown Spectroscopy (LIBS) with machine learning algorithms to analyze biological fluids such as blood, cerebrospinal fluid, urine, and saliva, using a substrate like copper to enhance signal-to-noise ratio and classification accuracy, allowing for the discrimination between healthy and cancerous samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional screening tests are used for early cancer detection, then the diagnostic process is well-established, but the tests are invasive, time-consuming, and lack accuracy for early-stage detection
Solution Approach 1:
The patent replaces traditional mechanical/biological screening methods with LIBS (Laser-Induced Breakdown Spectroscopy), a physics-based optical technique. The LIBS system uses laser pulses to generate plasma from biological fluids, and spectroscopic analysis to detect elemental compositions, thereby substituting invasive mechanical procedures with a noninvasive optical measurement system that provides rapid and accurate early cancer detection
Solution Approach 2:
The patent changes the measurement parameters from traditional biochemical markers to elemental composition analysis using LIBS. By detecting specific elemental ratios and spectral signatures in biological fluids, the system achieves high accuracy in early cancer detection while maintaining noninvasiveness, as the elemental composition provides unique pathological information without requiring tissue extraction
2Productivity
If LIBS is used for cancer diagnosis, then noninvasive and rapid screening is achieved, but the signal-to-noise ratio and classification accuracy need enhancement
Solution Approach 1:
The patent introduces an intermediary substrate as a mediator between the biological fluid sample and the laser beam. This substrate enhances the LIBS signal by providing a favorable surface for fluid deposition and plasma generation, thereby improving the signal-to-noise ratio and classification accuracy while maintaining the rapid and noninvasive screening capabilities of LIBS
Solution Approach 2:
The patent employs composite material strategies by combining the biological fluid with specific substrate materials that enhance spectroscopic signals. This composite approach allows the system to achieve both high productivity (rapid screening) and high measurement precision (improved signal-to-noise ratio) simultaneously
3Measurement precision
If machine learning algorithms are applied to LIBS spectral data, then classification accuracy exceeds 70%, but the system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-processing the LIBS spectral data and training machine learning algorithms offline before actual diagnosis. Spectral preprocessing steps (normalization, baseline correction) and model training are performed in advance, so that during actual screening, the system only needs to execute pre-trained classification algorithms, thereby achieving high accuracy without significantly increasing operational complexity
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 non-invasive, minimally invasive diagnosis and monitoring of cancer progression with high accuracy, exceeding 70% classification accuracy, effectively identifying cancerous samples through optimized laser-substrate coupling and machine learning analysis.
Implementation Method 1
focusing light from a laser light source on the sample deposited on the predetermined substrate; energy and pulse length of the laser light source being configured to cause ablation of the sample and the predetermined substrate and forming of a plasma
Implementation Method 2
collecting optical emission from the plasma, providing collected optical emission to a spectroscopic acquisition component; the spectroscopic acquisition component providing information on spectral data
Data Source
AI summary
Systems and methods for diagnosing or monitoring progress of a pathology using laser induced breakdown spectroscopy (LIBS) and machine learning are disclosed.