COPD Prediction from Audio Using AI Breath Analysis
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Solution Overview
Problem
Conventional methods for diagnosing chronic obstructive pulmonary disease (COPD) are invasive, costly, and require skilled operators, and spirometry fails to provide etiological diagnosis or detect obstructive-restrictive defects, making them inefficient for widespread and timely detection.
Innovation Solution
An electronic device using artificial intelligence to predict COPD from audio inputs based on short-winded breath determination, employing AI models, recurrent neural networks, and modular neural networks to analyze respiratory sounds and speaking patterns, providing a non-invasive and cost-effective diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spirometry is used for COPD diagnosis, then diagnostic accuracy is improved, but device complexity and operational difficulty increase due to requirement of skilled operators and multiple breathing maneuvers
Solution Approach 1:
The patent replaces the mechanical spirometry system with an acoustic detection system using microphones and AI algorithms. Instead of requiring mechanical breathing maneuvers and skilled operators, the system uses audio signals captured during normal speech to detect respiratory patterns, thereby reducing operational complexity while maintaining diagnostic accuracy.
Solution Approach 2:
The system enables self-service diagnosis by automatically analyzing respiratory patterns from audio inputs without requiring skilled operators. The AI model processes speech recordings and automatically identifies COPD indicators, allowing patients to undergo diagnosis without professional intervention for each measurement.
2Measurement precision
If spirometry is used for COPD diagnosis, then diagnostic capability is improved, but loss of time increases due to multiple breathing maneuvers and setup requirements
Solution Approach 1:
The system performs preliminary audio recording during normal speech activities, capturing respiratory patterns in advance. This eliminates the need for dedicated time-consuming breathing maneuvers, as the acoustic data is already collected during routine communication, thereby reducing overall diagnosis time.
Solution Approach 2:
The acoustic detection operates continuously during speech without interrupting normal communication. Unlike spirometry which requires discrete breathing maneuvers, the system continuously monitors respiratory patterns throughout the speech act, maintaining useful diagnostic action without time loss.
3Measurement precision
If conventional COPD diagnosis methods are used, then diagnostic accuracy is improved, but ease of operation deteriorates due to patient effort requirements and anatomical constraints
Solution Approach 1:
The patent substitutes mechanical breathing maneuvers with acoustic analysis of normal speech. Patients无需 exert special effort or modify their breathing patterns, as the system analyzes respiratory sounds captured during ordinary communication, significantly improving ease of operation while maintaining diagnostic accuracy.
Solution Approach 2:
The system changes the measurement parameter from mechanical lung volume (spirometry) to acoustic characteristics (audio signals). This parameter transformation allows diagnosis during normal speech without requiring patients to perform specific breathing exercises, thereby improving comfort and ease of operation.
4Ease of operation
If AI-based audio analysis is used for COPD prediction, then ease of operation and cost-effectiveness are improved, but measurement precision may be affected by acoustic noise and speech variability
Solution Approach 1:
The patent introduces an intermediary processing layer using AI algorithms that mediate between raw audio signals and diagnostic conclusions. This intermediary system filters out acoustic noise and speech variability through sophisticated signal processing, transforming potentially imprecise audio data into accurate COPD predictions while maintaining operational simplicity.
Solution Approach 2:
The system incorporates feedback mechanisms where the AI model continuously refines its analysis based on the audio input. By iteratively processing speech signals and adjusting its detection parameters, the system compensates for acoustic noise and speech variability, maintaining high measurement precision despite the simplified operational interface.
Data Source
AI summary
An electronic device and method for chronic pulmonary disease prediction from audio input based on short-winded breath determination using artificial intelligence is disclosed. The electronic device receives an audio input associated with a user. The electronic device applies an Artificial Intelligence (AI) model to detect a short-winded breath duration that corresponds to a time duration between an end of a first spoken word and a start of a second spoken word succeeding the first spoken word. The electronic device detects a speaking pattern. The electronic device applies a Recurrent neural network (RNN) model to reconstruct a set of short-winded breath audio samples. The electronic device generates an audio sample dataset and a set of audio features. The electronic device applies a modular neural network model on the generated audio sample dataset and on the generated set of audio features to determine a set of chronic obstructive pulmonary disease (COPD) metrics.