Dynamic Raman Profiling With AI Features for Diagnosis

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Solution Overview

Problem

There is a need for accurate, non-invasive methods for diagnosing biological conditions such as neurological and neurodegenerative disorders and cancers based on profiling biomarkers from non-invasively obtained biological samples.

Innovation Solution

A method involving Raman profiling of biological samples like hair, teeth, and nails, combined with artificial intelligence data analysis, to predict diagnostic status by processing Raman spectra and extracting temporal dynamics using dimensionality reduction and recurrence quantification analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Object-affected harmful factors

If non-invasive methods are used to obtain biological samples for diagnosis, then patient comfort and safety are improved, but diagnostic accuracy and reliability may deteriorate

Engineering Contradiction:

Improvepatient discomfortVSAvoiddiagnostic accuracy

Core Design Contradiction:

Object-affected harmful factorsVSReliability

Solution Approach 1:

The patent segments the diagnostic process into multiple independent analysis dimensions: Raman spectral analysis, temporal dynamics analysis, and machine learning classification. Each segment processes different aspects of the biological sample data, allowing comprehensive diagnosis while using minimal invasive samples like hair or saliva

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent combines multiple analytical approaches (Raman spectroscopy, temporal dynamics, machine learning) into a composite diagnostic system. This composite methodology processes biological samples non-invasively while achieving high diagnostic accuracy through the synergistic integration of multiple analysis techniques

Inventive Principle:

Principle #40Composite materials

2Reliability

If traditional invasive diagnostic methods are used, then diagnostic accuracy is improved, but patient discomfort and procedural complexity increase

Engineering Contradiction:

Improvediagnostic accuracyVSAvoidprocedural complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent extracts the essential diagnostic information from easily obtainable biological samples (hair, saliva, nails) without requiring invasive procedures. The Raman spectroscopy technique extracts molecular fingerprints from these samples, and machine learning algorithms extract diagnostic patterns, eliminating the need for complex invasive procedures while maintaining diagnostic accuracy

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent replaces complex mechanical invasive procedures with optical detection methods. Raman spectroscopy uses light scattering to obtain molecular information non-invasively, and machine learning algorithms replace complex manual diagnostic procedures with automated pattern recognition, significantly reducing procedural complexity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Ease of operation

If non-invasive biological samples are used, then sample collection ease is improved, but biomarker detectability and measurement precision may deteriorate

Engineering Contradiction:

Improvesample collection easeVSAvoidbiomarker detectability

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent uses Raman spectroscopy, which relies on inelastic scattering of light (a form of electromagnetic vibration) to detect molecular vibrations in biomarkers. This physical vibration-based detection method enables precise measurement of biomarkers in non-invasive samples like hair and saliva, overcoming the detectability limitations

Inventive Principle:

Principle #18Mechanical vibration

Solution Approach 2:

The patent applies machine learning algorithms that transform and analyze spectral parameters to enhance biomarker detectability. The algorithms identify subtle patterns and correlations in the Raman spectral data that are not apparent through traditional methods, improving measurement precision while maintaining ease of sample collection

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250325192A1Systems and Methods for Dynamic Raman Profiling of Biological Diseases and Disorders and Feature Engineering Methods Thereof

Publication Date: 2025.10.23 MT SINAI SCHOOL OF MEDICINE

AI summary

The present disclosure provides methods and systems for predicting a subject's diagnostic status with respect to a disease or disorder. The method may comprise exposing a biological sample of the subject to a laser, acquiring a plurality of Raman spectra from the exposed biological sample, processing the plurality of Raman spectra to generate a spatial map of the plurality of Raman spectra, and predicting a subject's diagnostic status with respect to disease or disorder based at least in part on the spatial map of the plurality of Raman spectra. The analyzing may comprise determining temporal dynamics of underlying biological processes.