Problematic Cellular Entity Detection With 3D Fluorescence Correction
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Solution Overview
Problem
Existing methods for detecting problematic cellular entities, such as pathogens and cancerous tissues, are cumbersome, require specialized facilities, and are often inaccurate due to interference from background light and variations in autofluorescence intensity caused by distance and curvature, leading to delayed diagnosis and contamination detection.
Innovation Solution
A device using a multispectral camera and three-dimensional image capturing sensor, combined with an analysis model, compensates for distance and curvature variations to accurately detect and classify problematic cellular entities by analyzing fluorescence and reflectance patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional detection methods are used, then specialized facilities and equipment are required, but the detection process becomes cumbersome and time-consuming
Solution Approach 1:
The patent replaces complex mechanical detection systems (microscopes, culture media, specialized facilities) with an optical-based imaging system using a smartphone camera. The fluorescence detection is achieved through software processing of images captured by a standard camera, eliminating the need for specialized mechanical detection equipment while maintaining detection accuracy.
Solution Approach 2:
The patent creates a digital copy (image) of the fluorescent signal using a smartphone camera instead of requiring direct observation through complex optical microscopes. The fluorescence pattern is captured as an image that can be processed and analyzed computationally, replacing the need for specialized viewing facilities.
2Productivity
If fluorescence imaging is used to detect cellular entities, then detection speed is improved, but accuracy deteriorates due to background light interference and autofluorescence variations
Solution Approach 1:
The patent applies preliminary processing to the captured images by computing the difference between images taken at different time points. This preliminary differencing operation removes background light interference and static autofluorescence patterns before the actual analysis, enabling accurate detection without requiring complex real-time filtering during the measurement process.
Solution Approach 2:
The patent uses feedback by comparing the captured fluorescence image with a reference image or previous time point images. The system analyzes changes in fluorescence intensity and patterns over time, using this feedback to distinguish true cellular entity signals from background variations and autofluorescence, thereby improving detection accuracy while maintaining speed.
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
Enables quick, accurate, and cost-effective detection and classification of cellular entities, facilitating timely treatment and contamination assessment.
Implementation Method 1
an imaging sensor to capture light emitted by the target in response to illumination of the target by the light sources
Implementation Method 2
a three-dimensional image capturing sensor to receive light reflected by the target in response to the illumination of the target by the light sources
Data Source
AI summary
A device for examining a target includes an imaging module and an interfacing module. The interfacing module includes a processor to analyze, using an analysis model, a first image of the first plurality of images, which is a fluorescence-based image comprising fluorescence from the target. The processor analyzes, using the analysis model, a three-dimensional image of the target to determine variation in intensity of the light emitted across a spatial region of the target by compensating for variation in distance across the spatial region of the target from the three-dimensional image capturing sensor and for variation in curvature across the spatial region of the target. The processor detects, using the analysis model, presence of a problematic cellular entity in the target based on the analysis. The analysis model is trained for detecting presence of problematic cellular entities in targets.