Neural Network Throat Image Analysis for Remote Strep Detection
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Solution Overview
Problem
Current telehealth diagnostic methods for streptococcus pharyngitis are limited, as they require in-person evaluation for throat swabs, and existing machine learning systems lack effective image segmentation for throat images, leading to inaccurate clinical assessments and overprescription of antibiotics.
Innovation Solution
A computer-implemented method using an electronic neural network trained on oral cavity-related data from reference subjects to generate prediction scores for streptococcus pharyngitis, allowing remote diagnosis through oral cavity images, symptom data, and physical examination data, and providing therapy recommendations based on these scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If telehealth services are provided remotely using digital communication technologies, then accessibility and convenience are improved, but diagnostic accuracy and reliability deteriorate due to lack of in-person evaluation
Solution Approach 1:
The patent introduces an AI-based image analysis system as an intermediary between the patient's remote throat image upload and the clinician's diagnosis. This intermediary automatically analyzes throat images for streptococcus pharyngitis indicators, providing objective measurement data that enhances diagnostic reliability while maintaining remote service accessibility.
Solution Approach 2:
The patent replaces the mechanical/in-person clinical evaluation process with an automated electronic image analysis system. Instead of requiring physical presence for throat swabbing and visual inspection, the system uses digital image processing and machine learning algorithms to detect disease indicators, thereby maintaining diagnostic accuracy while enabling remote service delivery.
2Productivity
If clinical prediction tools such as Centor or McIsaac scales are used, then diagnostic speed is improved, but measurement precision deteriorates with only 51-56% positive predictive value
Solution Approach 1:
The patent changes the parameters used for diagnosis from subjective clinical score criteria to objective image-based features. The AI system analyzes specific visual parameters in throat images such as tonsil morphology, exudate characteristics, and pharyngeal inflammation patterns, transforming the diagnostic basis from imprecise scoring to precise visual measurement while maintaining rapid assessment capability.
3Productivity
If automated image segmentation methods are implemented, then analysis efficiency is improved, but manufacturing precision deteriorates due to lack of specialized throat image segmentation capability
Solution Approach 1:
The patent applies segmentation by dividing the throat image analysis into distinct regional components. The system segments the oral cavity image to identify and analyze specific regions such as tonsils, pharynx, and surrounding tissues separately, allowing specialized processing for each anatomical structure and improving overall segmentation accuracy for throat-specific features.
Solution Approach 2:
The patent implements local quality by applying different analysis methods and parameters to different regions of the throat image. Each anatomical structure (tonsils, pharyngeal walls, uvula) receives tailored analysis appropriate to its characteristics, enhancing segmentation precision for each local region rather than applying a uniform processing approach to the entire image.
Data Source
AI summary
Examples may provide an electronic neural network (ENN) that has been trained on a set of training data that comprises sets of features extracted from oral cavity-related data obtained from reference subjects. The oral cavity-related data obtained from the reference subjects are each labeled with a positive or negative disease state ground truth classification for a given reference subject. Predictions for a positive or negative disease state classification for the given reference subject are made based on the oral cavity-related data obtained from the given reference subject, which predictions are compared to the ground truth classification for the given reference subject when the ENN is trained. The ENN outputs a prediction score for the disease state in a test subject that is indicated by a set of features extracted from oral cavity-related data obtained from the test subject when the set of features is passed through the ENN.