Hyperspectral Skin Detection via Machine Learning Inverse Models
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Solution Overview
Problem
Current methods for detecting skin cancers, such as melanoma, are often invasive or limited by single-band imaging, leading to missed diagnoses in early stages, and there is a need for non-invasive, efficient techniques for early detection of skin-related ailments.
Innovation Solution
A multi-band hyperspectral sensor system that uses machine learning to map constitutive skin parameters to spectral signatures, enabling early detection of skin cancers by estimating skin parameters and determining their distribution across various skin locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual examination by trained health care worker is used, then the diagnostic method is simple and non-invasive, but the detection precision is low leading to missed early stage diagnoses
Solution Approach 1:
The patent transitions from single-band imaging to multi-band hyperspectral imaging, adding spectral dimensionality to the detection process. This enables the system to capture reflectance data across multiple wavelength bands (400-1000 nm), providing richer information for detecting early stage skin cancers that single-band imaging cannot identify.
Solution Approach 2:
The system measures multiple spectral parameters across different wavelength bands rather than a single parameter. By analyzing reflectance spectra at multiple wavelengths and mapping them to constitutive skin parameters (melanin, hemoglobin, water content), the system achieves higher detection precision while maintaining non-invasive operation.
2Measurement precision
If single band imaging is used, then the device complexity is low, but the measurement precision is insufficient for early cancer detection
Solution Approach 1:
The system performs preliminary mapping of spectral signatures to constitutive skin parameters using a forward model before actual detection. This pre-established relationship between spectral data and skin parameters (melanin, hemoglobin, water) enables faster and more accurate detection without requiring complex real-time analysis of all spectral data.
Solution Approach 2:
The patent introduces spectral signatures as an intermediary between the raw multi-band imaging data and the final detection results. By mapping spectral signatures to constitutive skin parameters, the system creates an intermediate representation that simplifies the detection process and improves precision for identifying early stage skin cancers.
3Measurement precision
If multi-band hyperspectral sensor is used, then the detection precision is improved, but the device complexity increases
Solution Approach 1:
The hyperspectral sensor system is designed to perform multiple functions: capturing reflectance data across multiple wavelength bands, mapping spectral signatures to skin parameters, and detecting various skin conditions (melanoma, basal cell carcinoma, actinic keratosis). This multi-functionality justifies the increased device complexity by providing comprehensive diagnostic capabilities that single-band systems cannot achieve.
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 allows for non-invasive, early detection of skin cancers and other conditions by quantitatively computing biological parameters, predicting the growth and spread of malignant tumors, and providing valuable information for pharmaceutical and cosmetic applications.
Implementation Method 1
a multiband hyperspectral sensor configured to obtain multi-spectral data
Data Source
AI summary
A method of detecting a skin condition may include employing a multiband hyperspectral sensor to obtain multi-spectral data, employing the multi-spectral data to map constitutive skin parameters to corresponding spectral signatures via a forward model that enables generation of a set of samples including a plurality of parameters mapped to a plurality of spectral signatures, utilizing the set of samples to employ machine learning to generate an inverse model to enable mapping of a spectral signature of skin of a patient to corresponding skin parameters, estimating constitutive skin parameters of the skin of the patient based on the inverse model, and determining a distribution of the constitutive parameters for one or more skin locations.