Uroflow Classification Model for BOO vs DU Diagnosis
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Solution Overview
Problem
Existing diagnostic methods for urology patients with urination difficulties, such as bladder outlet obstruction (BOO) and detrusor underactivity (DU), rely heavily on invasive imaging techniques and expert intuition, limiting accurate differentiation between these conditions.
Innovation Solution
A machine learning model trained to process uroflow measurement data, using a multidimensional technique to classify patients into BOO or DU classes without invasive tests, by projecting data onto an orthogonal subspace to define a discriminant hyperplane for classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive imaging techniques are used to diagnose urology patients, then diagnostic accuracy is improved, but patient comfort and procedure complexity worsen
Solution Approach 1:
The patent replaces invasive mechanical imaging techniques with a non-invasive machine learning system that processes uroflow measurement data. The system uses neural networks to classify patients into BOO or DU categories based on flow rate patterns, eliminating the need for intrusive imaging procedures while maintaining diagnostic accuracy.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between the uroflow measurement device and the diagnostic conclusion. This intermediary processes the flow rate data through trained neural networks, extracting meaningful patterns without requiring direct invasive examination of the patient's urinary tract.
2Productivity
If expert intuition is used to interpret uroflow curves, then diagnostic speed is improved, but measurement precision worsens
Solution Approach 1:
The patent substitutes expert human interpretation with an automated machine learning system. The neural network models process uroflow curve data objectively, eliminating subjective variability in expert interpretation while maintaining or improving differentiation accuracy between BOO and DU conditions through learned patterns from training data.
Solution Approach 2:
The machine learning system performs self-service by automatically interpreting uroflow data without requiring expert intervention. The trained models independently analyze flow rate patterns, make classifications, and provide diagnostic insights, freeing experts from routine interpretation tasks while maintaining consistent and accurate differentiation.
3Ease of operation
If conventional classification techniques are used on image data, then ease of operation is improved, but measurement precision worsens due to resolution limitations
Solution Approach 1:
The patent replaces conventional image-based classification techniques with a direct time-series data processing approach. Instead of relying on visual interpretation of plotted curves that are limited by image resolution, the system processes raw flow rate measurements directly through neural networks, achieving higher precision by working with the actual numerical data rather than graphical representations.
Data Source
Figure 1A~1D
Figure 2
Figure 3A~3B
AI summary
The present invention comprises a training method for training a machine learning machine for classifying male urology patients into at least a first class of patients with bladder outlet obstruction (BOO) and a second class of patients with detrusor underactivity (DU). The present invention also comprises the method for classifying a urology patient into at least the first class and the second class, as well as a system configured for classifying a urology patient into at least the first class or the second class. The starting data for the classification is data representative of a uroflow measurement of a patient.