Deep Learning ECG Model for Non-Invasive CKD Screening
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Solution Overview
Problem
Current methods for screening chronic kidney disease (CKD) are inadequate due to their asymptomatic nature, lack of specificity, and requirement for invasive tests, leading to delayed diagnosis and increased morbidity and mortality.
Innovation Solution
A deep-learning model trained on electrocardiographic (ECG) data from 12-lead and 1-lead ECGs is developed to predict CKD stages, including end-stage renal disease, using convolutional neural networks and validated with large datasets to provide non-invasive and cost-effective screening.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional CKD screening methods are used, then diagnosis can be made, but the methods are invasive and have low specificity leading to delayed diagnosis
Solution Approach 1:
The patent uses ECG data as an intermediary marker to indirectly detect CKD. Instead of directly measuring kidney function through invasive tests, the system analyzes cardiac electrical patterns that are affected by kidney disease, providing a non-invasive screening method with high reliability
Solution Approach 2:
The patent replaces traditional mechanical/invasive kidney function tests with an electrical signal-based approach. By substituting direct kidney measurement with ECG analysis, the system achieves both non-invasiveness and diagnostic reliability through machine learning algorithms
2Measurement precision
If invasive tests are used for CKD screening, then diagnosis accuracy may improve, but patient burden and morbidity increase
Solution Approach 1:
The system uses routinely collected ECG data that already exists in healthcare records, allowing the data to serve itself for dual purposes: cardiac assessment and kidney disease screening. This eliminates the need for separate invasive kidney tests, maintaining detection accuracy while avoiding additional patient burden
Solution Approach 2:
The patent transforms the presence of ECG data, which is typically used only for cardiac evaluation, into a beneficial dual-purpose tool for kidney disease detection. What was previously a single-use resource becomes a multi-functional diagnostic asset, improving patient outcomes without additional harm
3Measurement precision
If deep learning models are trained on large datasets, then detection accuracy improves, but computational complexity and training time increase
Solution Approach 1:
The patent segments the CKD detection task into multiple stages: initial CKD presence detection, followed by stage classification. This segmentation allows the model to achieve high overall accuracy while managing complexity through hierarchical processing, where each stage builds on previous results
Solution Approach 2:
The system performs preliminary filtering and feature extraction from ECG data before feeding it to the deep learning model. This preliminary action pre-processes the data to reduce dimensionality and highlight relevant features, decreasing the computational burden during model training and inference while maintaining detection accuracy
Data Source
AI summary
A method for analyzing kidney health in an individual comprises receiving data associated with one or more cardiac characteristics of the individual; inputting the data associated with the one or more cardiac characteristics of the individual into a machine learning model; and receiving an output from the machine learning model indicative of the kidney health of the individual. The output of the machine learning model can include an indication of the presence of chronic kidney disease (CKD) in the individual or an indication of the absence of CKD in the individual. The indication of the presence of CKD in the individual can include an indication of the CKD stage of the individual, which may include mild CKD, moderate-severe CKD, or end-stage renal disease ESRD).