Throat Image Analysis With Clinical Factors for Rapid Infection Detection
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Solution Overview
Problem
Current methods for diagnosing viral or bacterial infections, such as COVID-19 or streptococcal infections, are slow, difficult to administer, especially in children, and can be inaccurate, posing risks to healthcare providers.
Innovation Solution
A detection system that analyzes image data of a subject's throat and associated clinical factors using a chained model comprising an image data model and a classifier to predict disease states, including the presence of viral or bacterial infections, leveraging machine learning techniques like convolutional neural networks and supervised learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional culturing methods are used to diagnose viral or bacterial infections, then diagnostic accuracy can be maintained, but the diagnosis process becomes slow and time-consuming
Solution Approach 1:
The patent replaces the mechanical biological culturing process with an optical imaging and machine learning analysis system. The system captures images of the throat using a smartphone camera and applies deep learning algorithms to detect infections, eliminating the need for time-consuming laboratory culturing while maintaining diagnostic accuracy.
Solution Approach 2:
The patent creates a digital copy of the throat condition through imaging and uses this copy for analysis instead of requiring physical culturing of biological samples. The machine learning model analyzes the visual copy (image data) to diagnose infections, significantly reducing the time required while preserving diagnostic capability.
2Reliability
If traditional culturing methods are used for infection diagnosis, then diagnostic capability is maintained, but the administration becomes difficult especially in children
Solution Approach 1:
The patent replaces the complex mechanical procedure of administering culturing tests with a simple optical imaging procedure. The system only requires capturing images of the throat, which is much easier to perform, especially in children who cannot cooperate with invasive culturing procedures.
Solution Approach 2:
The system enables self-administration of the diagnostic test. Users can capture their own throat images using a smartphone, and the machine learning model automatically performs the diagnosis without requiring trained medical personnel to administer complex culturing procedures.
3Measurement precision
If traditional culturing methods are used for infection diagnosis, then diagnostic thoroughness is maintained, but healthcare providers are put at risk
Solution Approach 1:
The patent replaces direct contact with biological samples required in culturing methods with non-contact optical imaging. This eliminates the exposure risk to healthcare providers while maintaining the ability to thoroughly diagnose infections through advanced image analysis by machine learning models.
Data Source
AI summary
A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image data model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image data model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.