Segmented Throat Imaging for Consumer-Grade Infection Screening
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Solution Overview
Problem
Existing diagnostic tools for common illnesses face challenges in image quality, data analysis, and integration of artificial intelligence with consumer-grade hardware, limiting their effectiveness in remote diagnostics and telemedicine.
Innovation Solution
A wellness management application that segments throat images into anatomical structures, applies independently trained machine learning models to each segment, and aggregates predictions for accurate infection detection, integrated with telehealth systems for efficient healthcare delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are applied to analyze throat images for infection detection, then diagnostic accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the throat image into multiple anatomical regions (tonsils, uvula, tongue, palate, etc.) and applies separate machine learning models to each region. This segmentation allows complex diagnostic tasks to be divided into simpler, specialized models, making the overall system more manageable while maintaining high diagnostic accuracy through region-specific analysis.
Solution Approach 2:
The patent introduces a cloud-based processing system as an intermediary between the consumer device and the machine learning models. The consumer device captures images and transmits them to the cloud, where sophisticated ML models perform the analysis. This intermediary approach enables accurate diagnosis without requiring complex computational hardware at the consumer end, thus reducing device complexity while maintaining diagnostic precision.
2Ease of operation
If consumer-grade hardware is used for image capture, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent uses a cloud-based processing system as an intermediary that receives images from consumer-grade devices and performs sophisticated analysis. The cloud system compensates for the limitations of consumer hardware by providing advanced image processing algorithms and computational resources, thereby maintaining measurement precision while preserving the ease of operation associated with consumer devices.
Solution Approach 2:
The patent replaces the need for expensive professional imaging hardware with consumer-grade cameras by substituting the analysis mechanism. Instead of relying on high-quality physical hardware, the system uses AI-powered software algorithms in the cloud to extract meaningful diagnostic information from images captured by simple consumer devices, thus achieving precision without the complexity of specialized hardware.
3Measurement precision
If multiple machine learning models are applied to different image segments, then diagnostic accuracy is improved, but processing time increases
Solution Approach 1:
The patent divides the throat image into distinct anatomical segments and applies specialized machine learning models to each segment in parallel. This segmentation enables concurrent processing of different regions, significantly reducing total processing time compared to sequential analysis. Each segment can be analyzed simultaneously, maintaining high diagnostic accuracy while improving processing efficiency.
Solution Approach 2:
The patent applies machine learning models to only the necessary anatomical segments relevant to infection detection, rather than analyzing every possible region exhaustively. By focusing computational resources on the most pertinent areas (tonsils, uvula, palate, etc.), the system achieves accurate diagnosis with reduced processing time, avoiding unnecessary analysis of irrelevant regions.
Data Source
AI summary
A wellness management application enables automated diagnosis of a throat infection based on a throat image. A captured image is segmented into a plurality of image segments corresponding to different anatomical structures. A set of machine learning models are applied to the respective image segments to generate respective prediction scores indicative of likelihood of infection. Each of the set of machine learning models are independently trained based on labeled images of the corresponding anatomical structure. The results of the respective models may be aggregated to generate an aggregate prediction. Furthermore, various visual representations may be generated that illustrate respective contributions of different regions of the image to the prediction. The wellness management application may be integrated with a telehealth system to facilitate diagnosis and treatment of infections.