Throat Image Analysis for Streptococcal Infection Detection
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Solution Overview
Problem
Current methods for diagnosing sore throats, particularly distinguishing between viral and bacterial infections, are slow, inaccurate, and prone to over-prescription of antibiotics, leading to unnecessary medication use and antibiotic resistance.
Innovation Solution
A detection system utilizing a chained model that combines image analysis of a subject's throat with clinical factors to predict disease states, eliminating the need for laboratory tests by using an image model trained on throat images and clinical data to generate disease metrics, which are then classified for accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional culturing methods are used to diagnose bacterial infections, then diagnostic accuracy can be achieved, but the diagnostic time is extended up to 72 hours
Solution Approach 1:
The patent replaces the mechanical/biological culturing process with an optical imaging system using a portable device that captures throat images and uses machine learning algorithms to detect streptococcal infections, reducing diagnostic time from 72 hours to minutes while maintaining accuracy
Solution Approach 2:
The system creates a digital copy of the throat condition through imaging and analyzes it using trained machine learning models, eliminating the need for physical culturing and enabling rapid diagnosis without sacrificing diagnostic precision
2Productivity
If antibiotics are prescribed based on clinical bias rather than accurate diagnosis, then treatment can be provided quickly, but antibiotic overuse increases leading to resistance
Solution Approach 1:
The system provides objective feedback through machine learning-based diagnosis that reduces clinical bias, enabling practitioners to make evidence-based decisions about antibiotic prescription and avoid unnecessary treatment of viral infections
Solution Approach 2:
The portable device enables practitioners to perform accurate self-diagnosis of streptococcal infections at the point of care, eliminating the need for referral to laboratories and enabling immediate, accurate treatment decisions
3Measurement precision
If traditional culturing methods are used, then bacterial presence can be detected, but the process is difficult to administer properly especially in children
Solution Approach 1:
The patent replaces the complex manual culturing process with a simple imaging-based system that requires only capturing a picture of the throat, making it easy to administer to children and other patients without cooperation difficulties
Solution Approach 2:
The system uses digital imaging to create a record of the throat condition that can be analyzed automatically, eliminating the need for difficult manual sample collection and culturing procedures
4Ease of operation
If a simple imaging system is used without clinical factors, then the system is easier to operate, but the disease state prediction accuracy is reduced
Solution Approach 1:
The patent merges image analysis with clinical factor integration in a unified machine learning model, allowing the system to maintain simplicity of operation while achieving high prediction accuracy through combined data inputs
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 model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.