Ensemble ML Respiratory Screening Using Voice and Image Signals

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Solution Overview

Problem

Clinical diagnosis of COVID-19 is time-consuming and expensive, especially in areas with limited resources, and existing computer-implemented methods often rely on a single channel of information, leading to lower accuracy and specificity in detecting respiratory infections.

Innovation Solution

Implementing ensemble machine-learning models that combine multiple channels of input data, including audio and image analysis, using smartphones or other devices to detect COVID-19 through features like MFCCs, mel-spectrograms, and biometric images, with data augmentation and ensemble learning techniques to enhance accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If clinical diagnosis methods are used to detect COVID-19, then diagnostic accuracy is improved, but time consumption and cost increase

Engineering Contradiction:

Improvediagnostic accuracyVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces complex mechanical clinical diagnostic systems with an acoustic-based detection system using machine learning. The system analyzes cough audio signals to detect COVID-19, substituting physical clinical examinations and laboratory tests with computational acoustic analysis that provides rapid results without requiring extensive medical infrastructure

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces acoustic signals as an intermediary medium for disease detection. Instead of directly examining patients through complex clinical procedures, the system uses cough audio recordings as an intermediate carrier that contains diagnostic information, which is then processed through machine learning models to identify COVID-19 cases

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If single-channel information is used for detection, then device complexity is reduced, but detection accuracy decreases

Engineering Contradiction:

Improvedetection accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent merges multiple independent detection channels into a unified ensemble system. It combines acoustic analysis, demographic information, and clinical data through an ensemble machine learning framework, where multiple classifiers work together to improve overall detection accuracy while maintaining manageable system complexity through modular architecture

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS12444502B2Ensemble machine-learning models to detect respiratory syndromes

Publication Date: 2025.10.14 THE COVID DETECTION FOUNDATION D B A VIRUFY

AI summary

Provided is a process including: obtaining, with one or more processors, a set of data comprising a plurality of patient records, selecting a subset of the plurality of parameters for inputs into a machine learning system, generating a classifier using the machine learning system based on the training data and the subset of the plurality of parameters for inputs; receiving, with one or more processors, patient record of a first user; performing an analysis, with one or more processors, to identify acoustic measures from a voice sample of the first user.