Recurrent Neural Network for Canine CKD Risk Scoring
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Solution Overview
Problem
There is a need for effective systems and methods to early detect chronic kidney disease (CKD) in dogs and provide customized recommendations to reduce associated health risks.
Innovation Solution
A computer system utilizing a processor and memory that processes biomarkers such as urine specific gravity, creatinine, blood urea nitrogen, and demographic information using a recurrent neural network to determine a probability risk score for CKD, and provides tailored recommendations for therapeutic interventions, dietary changes, and renal sparing strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional CKD detection methods are used, then the system is simple and easy to operate, but the detection precision and early identification capability are insufficient
Solution Approach 1:
The patent replaces traditional mechanical/manual CKD detection methods with an artificial intelligence system using recurrent neural networks and machine learning algorithms. This substitution enables automated processing of biomarker data (creatinine, BUN, urine specific gravity) and demographic information to generate probability risk scores, significantly improving detection precision while managing complexity through software-based solutions.
Solution Approach 2:
The patent introduces an AI-based prediction model as an intermediary between raw biomarker data and CKD diagnosis. This intermediary process automatically analyzes multiple parameters (creatinine, BUN, urine specific gravity, demographic information) and generates probability risk scores, enhancing detection capability without requiring complex manual interpretation procedures.
2Reliability
If AI-based prediction models are implemented, then early detection capability is improved, but the ease of operation decreases due to complex data processing requirements
Solution Approach 1:
The patent implements a self-service system where the AI model automatically processes biomarker data and demographic information without requiring manual intervention. The recurrent neural network autonomously generates probability risk scores from input data (creatinine, BUN, urine specific gravity), and the system automatically provides customized recommendations, reducing operational complexity despite enhanced detection reliability.
Solution Approach 2:
The patent performs preliminary processing of biomarker data and demographic information before final diagnosis. The AI model pre-processes multiple parameters (creatinine, BUN, urine specific gravity) and generates probability risk scores in advance, making the detection process more reliable while automating operations to maintain ease of use.
3Measurement precision
If multiple biomarkers and demographic information are processed, then the detection accuracy increases, but the loss of time for data processing increases
Solution Approach 1:
The patent employs a recurrent neural network that continuously processes biomarker data and demographic information in an automated flow. The system maintains continuous useful action by automatically analyzing multiple parameters (creatinine, BUN, urine specific gravity, demographic information) and generating probability risk scores without interruption, improving assessment accuracy while minimizing processing time through uninterrupted automated operation.
Solution Approach 2:
The patent performs preliminary organization and preparation of biomarker data and demographic information before AI analysis. By pre-processing and structuring the data (creatinine, BUN, urine specific gravity, demographic information) in advance, the system enables faster and more accurate susceptibility assessment, reducing the time loss associated with processing multiple parameters.
Data Source
AI summary
The presently disclosed subject matter relates to methods or systems for identifying susceptibility of a dog to develop chronic kidney disease (CKD). The method, for example, can include receiving at least one of one or more biomarkers or demographic information of a dog. The method can also include processing at least one of the one or more biomarkers or demographic information of the dog using a prediction model. The prediction model can include a recurrent neural network. In addition, the method can determine a probability risk score of the dog for developing CKD based on the processed one or more biomarkers or demographic information.