Portable Parasite Detection Device Using Machine Learning
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Solution Overview
Problem
Current methods for detecting parasites in food animals, such as coccidiosis in poultry, are time-intensive and often inaccurate, even when performed by skilled technicians or veterinarians, leading to inefficiencies in disease management and potential economic losses.
Innovation Solution
A portable device equipped with a microscope, camera, and machine-learning software that automatically counts, speciates, and determines the infectivity of parasite eggs, using a trained model to analyze images and provide rapid, on-site results without the need for highly skilled personnel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual parasite detection methods are used by skilled technicians or veterinarians, then measurement precision can be maintained, but productivity is reduced due to time-intensive processes
Solution Approach 1:
The patent replaces manual mechanical inspection methods with an automated imaging and machine learning analysis system. The device uses a camera to capture images of parasite eggs in fecal samples, then applies trained machine learning models to automatically identify and count different parasite species, eliminating the need for manual microscopic examination while maintaining high accuracy.
Solution Approach 2:
The system enables self-service parasite detection by providing an automated device that can be operated without requiring skilled technicians or veterinarians. The machine learning model automatically performs the complex task of parasite identification and enumeration, allowing farm workers or producers to conduct diagnostic testing themselves, thereby increasing accessibility and productivity.
2Measurement precision
If manual parasite detection methods are used, then measurement precision can be maintained, but loss of time increases due to time-intensive processes
Solution Approach 1:
The patent replaces time-consuming manual examination with automated image capture and machine learning analysis. The system rapidly processes images of parasite eggs through algorithmic analysis, delivering results in minutes rather than the hours required for manual microscopic examination by skilled personnel.
Solution Approach 2:
The machine learning model is pre-trained on extensive datasets of parasite images before deployment. This preliminary training enables the system to rapidly recognize and classify parasite species during actual detection without requiring real-time expert analysis, significantly reducing detection time while maintaining accuracy.
3Productivity
If automated device is used for parasite detection, then productivity is improved through rapid analysis, but device complexity increases due to integration of microscope, camera, and machine-learning model
Solution Approach 1:
The patent combines multiple functions into a single integrated device: the microscope objective for magnification, the camera for image capture, and the machine learning processing unit for analysis are merged into one portable system. This integration simplifies operation compared to using separate equipment while maintaining high productivity through automated multi-functional operation.
Solution Approach 2:
The device is designed as a multi-functional portable system that can identify multiple different parasite species (including coccidia and other intestinal parasites) using the same hardware platform and machine learning model framework. This universality reduces the need for multiple specialized devices while achieving high detection productivity across various parasite types.
4Loss of time
If automated device is used for parasite detection, then loss of time is reduced through rapid on-site results, but device complexity increases due to integration of multiple components
Solution Approach 1:
The patent integrates the microscope objective, camera, lighting system, and machine learning processing into a single portable device that delivers rapid on-site results. This consolidation enables fast detection without requiring multiple separate equipment pieces, reducing both detection time and operational complexity compared to traditional laboratory-based approaches.
Solution Approach 2:
The system replaces complex manual procedures with automated image capture and algorithmic analysis. The machine learning model automatically processes images and generates diagnostic results without requiring manual manipulation or interpretation, achieving rapid detection while simplifying the operational workflow despite the sophisticated underlying technology.
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
The device enables rapid, accurate, and inexpensive parasite detection, facilitating effective control measures, reducing animal euthanasia, and improving decision-making for food animal producers while creating a central database for data sharing and model improvement.
Implementation Method 1
The device can include a light source. The light source can illuminate a field of view of the sample specimen in the chamber.
Implementation Method 2
The device can further include a microscope objective to magnify the field of view of the sample specimen.
Implementation Method 3
The device can include a camera. The camera can image the field of view of the sample specimen. The camera can further produce an on-site dataset of images.
Data Source
AI summary
A device can automatically count, speciate, and determine infectivity of eggs of parasites in food animals. The device can include a chamber that can receive a sample specimen. Additionally, the device can include a light source. The light source can illuminate a field of view of the sample specimen in the chamber. The device can further include a microscope objective to magnify the field of view of the sample specimen. The device can include a camera. The camera can image the field of view of the sample specimen. The camera can further produce an on-site dataset of images. Additionally, the camera can provide the on-site dataset of images to a trained machine-learning model for analysis of at least one species of parasites.