Mobile Camera Hemoglobin Determination From Lower Eyelid Images
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Solution Overview
Problem
Existing methods for measuring hemoglobin levels require invasive techniques or specialized equipment, limiting accessibility to health monitoring and diagnosis, especially in regions with limited medical resources.
Innovation Solution
A non-invasive method using a machine learning pipeline on a non-augmented mobile device camera to analyze an image of the eye, specifically the lower eyelid portion, to determine hemoglobin levels, incorporating demographic and medical history data for personalized healthcare.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive techniques or specialized equipment are used to measure hemoglobin levels, then measurement precision is improved, but ease of operation and accessibility deteriorate
Solution Approach 1:
The patent replaces invasive mechanical blood drawing procedures with non-invasive image analysis of the lower eyelid. The machine learning pipeline processes images captured by standard mobile device cameras to determine hemoglobin levels, eliminating the need for physical blood collection while maintaining measurement capability.
Solution Approach 2:
The patent uses visual information from images of the lower eyelid as a proxy copy to infer hemoglobin levels. Instead of directly measuring blood, the system analyzes color and other visual characteristics of the eyelid tissue, which reflect hemoglobin concentration, thereby avoiding direct invasive measurement.
2Measurement precision
If specialized equipment like optical reflectance photometers is used, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces expensive, specialized optical reflectance photometers with images captured by inexpensive mobile device cameras. The system leverages the ubiquitous, low-cost camera hardware already present in smartphones and mobile phones, eliminating the need for dedicated medical equipment.
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary between simple camera images and hemoglobin measurement. The ML pipeline processes the visual data from standard cameras to extract meaningful hemoglobin information, bridging the gap between simple imaging hardware and accurate medical measurement.
3Measurement precision
If image processing and machine learning operations are added to the pipeline, then measurement precision is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent performs image preprocessing operations such as white balance adjustment and denoising in advance before the final hemoglobin measurement. These preliminary actions prepare the image data to optimize subsequent machine learning analysis, improving measurement accuracy while managing processing requirements.
Data Source
AI summary
Disclosed herein are systems, methods, computer-readable media, and techniques for determining health indicators from images taken using a non-augmented mobile device. Methods are presented for processing images comprising the lower eyelid portion of an eye and determining levels of said health indicators using one or more machine learning algorithms. One such health indicator is hemoglobin, the level of which can be used to determine at least indications of general health or presence of an anemia condition in a subject.