Advanced spectral sensing device for biometric diagnostics

The integration of a standard RGB camera with a single pixel detector and machine learning models enhances spectral resolution, addressing the complexity and cost of conventional systems, enabling accurate spectral analysis and real-time microbiome monitoring.

WO2026036143A1 Publication Date: 2026-02-12ANI BIOME INC
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Patent Information

Application Number
PCT/US2025/041531
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-08-09
Filing Date
2025-08-11
Publication Date
2026-02-12

AI Technical Summary

Technical Problem

Conventional multispectral and hyperspectral imaging systems are costly, complex, and bulky, limiting their widespread adoption due to the need for specialized optical components and technical expertise, while standard RGB cameras lack the spectral resolution for detailed material and biological analysis, and traditional microbiome analysis is expensive, labor-intensive, and time-consuming, hindering real-time health monitoring.

Method used

A method that enhances spectral resolution using a standard RGB camera with a single pixel detector spectroradiometer and machine learning models to predict spectral values, and a multimodal machine learning system combining spectral imaging, conventional photography, and user-reported data for microbiome composition prediction, enabling non-invasive daily scans.

Benefits of technology

Enables accurate spectral analysis without specialized hardware, facilitating widespread deployment of spectral imaging in consumer devices for health monitoring and microbiome analysis, providing timely and personalized insights.

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Abstract

A spectral sensing device and associated methods for enhanced diagnostic capabilities across medical fields are disclosed. The device incorporates multispectral, hyperspectral, and photoplethysmography technologies to analyze biological features including tongue, oral cavity, saliva, teeth, and other tissues. The system transforms any standard RGB camera into a multispectral imaging device by training machine learning models to associate RGB values with spectral values inside and outside the visible spectrum using measurements from a single pixel detector spectroradiometer. A multimodal neural network with separate processing branches analyzes spectral images, non-spectral images, and tabular data to predict microbiome composition. The system generates health risk assessments by comparing predicted microbial abundances to thresholds associated with various medical conditions. Near-infrared sensing enables blood flow monitoring, subcutaneous imaging, and assessment of physiological parameters. Convolutional neural networks enable spectral reconstruction without requiring the physical device, while supporting continuous monitoring in various form factors.
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