Digital Stethoscope Bovine Lung Sound Analysis
Solution Overview
Problem
Current methods for diagnosing bovine respiratory diseases are inefficient due to the lack of automated systems for analyzing auscultated sounds, which are complicated by the thick musculature and fat layers of cattle, making it difficult to obtain accurate lung sound analysis for timely and effective treatment.
Innovation Solution
A system and method using a digital stethoscope to collect and analyze bovine lung sounds, converting them into numerical lung scores through mathematical operations and algorithms, comparing these scores to threshold levels to diagnose and recommend treatments, with additional filtering techniques to eliminate noise and improve analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional auscultation methods are used by caregivers, then diagnosis can be made based on trained ear assessment, but the diagnosis lacks objectivity and precision due to reliance on subjective evaluation rather than measured data
Solution Approach 1:
The patent replaces the mechanical/subjective human ear assessment system with an electronic/digital auscultation system that captures, processes, and analyzes lung sounds objectively. The digital stethoscope converts acoustic signals into digital data that can be manipulated by software algorithms, transforming a subjective mechanical process into an objective digital measurement system.
Solution Approach 2:
The patent introduces software programs and algorithms as intermediaries between the digital stethoscope and the caregiver. These intermediaries automatically process the raw digital audio data, extract relevant features, and generate diagnostic recommendations, eliminating the need for caregivers to directly interpret complex acoustic patterns while maintaining diagnostic accuracy.
2Measurement precision
If stethoscopic evaluation is performed on cattle, then lung sounds can be obtained for disease detection, but the thick musculature, heavy hide, and fat layers complicate sound acquisition and reduce measurement accuracy
Solution Approach 1:
The patent applies preliminary signal processing operations to the captured lung sounds before analysis. The software performs preprocessing steps including noise filtering, signal enhancement, and feature extraction that prepare the data for accurate analysis, compensating for the degradation caused by cattle's body structures before the actual diagnosis is made.
Solution Approach 2:
The patent transforms the raw audio signal parameters into different representations that make the diagnostic features more apparent. By converting time-domain signals into frequency-domain representations and extracting specific acoustic features, the system changes the parameters of the data to reveal disease indicators that would be obscured in the original signal by body tissue interference.
3Productivity
If automated diagnostic systems are developed for bovine auscultation, then productivity and objectivity improve, but the lack of existing bovine sound data limits the system's effectiveness
Solution Approach 1:
The patent performs preliminary data collection and analysis to build a reference database of bovine lung sounds. By gathering and processing auscultation data from multiple cattle, the system creates a baseline library of normal and abnormal lung sounds that can be used for comparison in future diagnoses, progressively reducing the information gap.
Solution Approach 2:
The patent implements feedback mechanisms where the automated system's diagnostic outputs are compared with actual disease outcomes and treatment responses. This feedback loop allows the system to learn from real-world results, refine its algorithms, and improve its reference database over time, converting initial data limitations into a self-improving system.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient and accurate prediction of respiratory problems in cattle, allowing for timely diagnosis and treatment recommendations based on objective criteria, improving health assessment and biosecurity in feed yards.
Implementation Method 1
Stethoscopic evaluation of bovine lung sounds is used to gather data on the sounds. Collection of lung sounds is preferably obtained by a digital/electronic stethoscope that is capable of expressing sounds in the form of a spectrogram.
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
A system and method are provided for diagnosis of bovine respiratory diseases using auscultation techniques wherein the acoustic characteristics of a recorded spectrogram is compared with existing data enabling a caregiver to make a diagnosis for a diseased animal. Lung sounds are obtained by use of an electronic stethoscope, and the sounds are stored as digital data. Signal conditioning is used to place the data in a desired format and to remove undesirable noise associated with the recorded sounds. An algorithm is applied to data, and lung scores are calculated. The lung scores are then categorized into vanous levels of perceived pathology/disease within the animal based upon baseline data that categorizes the lung scores. From the lung scores, a caregiver may then associate a diagnosis, prognosis, and a recommended treatment. Analysis software in the present invention generates the lung scores from the recorded sounds.