Machine Learning Biomarker Extraction for UPJO Severity Classification
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Solution Overview
Problem
Current methods for diagnosing ureteropelvic junction obstruction (UPJO) rely heavily on subjective interpretation of time-activity curves and single-metric analyses, leading to high inter-observer variability and insufficient accuracy in determining the severity of obstruction, necessitating repeated testing and potentially delaying or misjudging the need for surgical intervention.
Innovation Solution
A system and method utilizing machine learning to extract a set of biomarkers from time-activity curves obtained through diuretic renography or functional magnetic resonance urography, allowing for a more accurate and predictive classification of UPJO severity, including the use of a linear support vector machine classifier and logistic regression to generate a clinical diagnosis and recommend treatment based on probability analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective interpretation of time-activity curves and single-metric analyses are used for UPJO diagnosis, then the diagnostic process is simple and quick, but the measurement precision and reliability are low leading to high inter-observer variability
Solution Approach 1:
The patent replaces subjective human interpretation (mechanical/visual analysis) with automated machine learning algorithms and signal processing. The system uses computational methods to extract multiple biomarkers from time-activity curves and applies classification algorithms (e.g., support vector machines, random forests) to objectively determine UPJO severity, eliminating inter-observer variability while maintaining diagnostic accuracy.
Solution Approach 2:
The patent segments the diagnostic process into multiple independent biomarker extractions from different features of time-activity curves. Instead of relying on a single metric, the system extracts multiple biomarkers (e.g., drainage half-time, clearance rates, curve morphology features) and combines them through machine learning classification, thereby improving measurement precision through multi-parameter analysis.
2Reliability
If repeated testing is performed to improve diagnostic confidence, then the reliability of diagnosis improves, but the loss of time and productivity decrease due to multiple testing sessions
Solution Approach 1:
The patent introduces machine learning classification algorithms as an intermediary that processes multiple biomarkers simultaneously and generates a probabilistic diagnosis. This intermediary synthesizes information from multiple features in a single analysis pass, providing high diagnostic confidence without requiring repeated physical testing sessions, thereby reducing time loss while maintaining reliability.
Solution Approach 2:
The patent changes the diagnostic approach from single-metric analysis to multi-parameter analysis. By extracting and analyzing multiple biomarkers simultaneously through machine learning, the system achieves higher diagnostic confidence in a single testing session rather than requiring repeated tests of single metrics, thus reducing the time loss associated with multiple testing sessions.
3Ease of operation
If standardized criteria for surgical intervention are established, then the ease of operation improves, but the adaptability to individual patient cases may be reduced
Solution Approach 1:
The patent implements a dynamic diagnostic system that adapts to individual patient cases through machine learning classification. The system processes multiple biomarkers and generates probabilistic diagnoses tailored to each patient's specific presentation. The standardized output format (probability scores and classification categories) provides ease of operation, while the underlying multi-parameter analysis maintains adaptability to individual variations in UPJO severity and presentation.
Data Source
AI summary
Systems, apparatuses, and methods for diagnosing ureteropelvic junction obstruction. A set of biomarkers may be extracted from each of one or more time-activity curves associated with diuresis renography and/or functional magnetic resonance urography of one or more kidneys of a patient. One or more calculations can be performed based on the set of biomarkers to identify uretero-pelvic junction obstruction and a classification of severity or criticality thereof.