Real-Time Raman Spectroscopy for Cancer Detection with Feature Filtering
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Solution Overview
Problem
Current Raman spectroscopy methods for cancer detection face challenges due to instrumentation and biological distortions, such as dark current, detector and optic responses, fluorescence background, peak misalignment, peak width heterogeneity, spectral saturation, cosmic ray interference, ambient light interference, and low Raman signal levels, leading to poor classification accuracy and inability to filter out contaminants for real-time surgical use.
Innovation Solution
A Raman system employing data quality assessment, feature extraction, and supervised learning models that filter out contaminants and assess Raman data against known biomarkers, using adaptive laser power and exposure time, cosmic ray and ambient light interference detection, and unsupervised feature selection to enhance classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If full Raman spectra are used for training classification algorithms, then complete spectral information is captured, but classification accuracy deteriorates due to noise and contaminants in non-significant portions
Solution Approach 1:
The patent extracts only the significant Raman spectral features that contain relevant tissue classification information, separating them from non-significant portions that contain noise and contaminants. This selective extraction improves classification accuracy while preserving essential spectral information.
Solution Approach 2:
The patent applies different processing qualities to different portions of the Raman spectrum. Significant spectral regions undergo rigorous filtering and validation, while non-significant regions are either enhanced or discarded based on their contribution to classification accuracy. This local quality approach optimizes the balance between information retention and noise reduction.
2Loss of time
If Raman spectroscopy is implemented in real-time surgical settings, then immediate tissue classification is achieved, but data quality deteriorates due to multiple additive distortions and contaminants
Solution Approach 1:
The patent implements preliminary data quality assessment and contaminant filtering before classification. By pre-identifying and removing known sources of contamination (cosmic rays, ambient light, fluorescence background) and establishing quality thresholds, the system ensures that only high-quality spectra are used for real-time classification, maintaining both speed and accuracy.
Solution Approach 2:
The patent incorporates feedback mechanisms where classification results and data quality metrics are continuously monitored. When data quality falls below thresholds or classification confidence is low, the system can request additional measurements or adjust processing parameters, ensuring reliable real-time classification despite surgical environment challenges.
3Use of energy by moving object
If adaptive laser power and exposure time are used, then Raman signal levels are optimized, but system complexity increases due to additional control mechanisms
Solution Approach 1:
The patent implements dynamic adjustment of laser power and exposure time based on real-time signal quality assessment. The system adapts these parameters during acquisition to optimize Raman signal levels while maintaining data quality within acceptable ranges. This dynamic control balances signal optimization with manageable system complexity through automated feedback loops.
Data Source
AI summary
Provided are Raman systems and methods for detection of cancerous tissues in tumor margins in real time during surgical procedures. The systems include a laser excitation source, a probe, a spectrometer and a camera specifically designed for use of Raman spectroscopy in real time during surgery. The inventive methods pertain to quality assessment of Raman data, comparison of obtained Raman data against known biomarkers and classification of tissue as cancerous or non-cancerous on the basis of corresponding Raman data. The systems and methods allow for previously unavailable detection and classification of tissue with Raman spectroscopy in real time surgical applications, including the identification of cancer sub-types.