Augmented Intelligence Urine Microscopy for Sediment Detection
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Solution Overview
Problem
Current urine sediment analysis methods require specialized training and equipment, are time-consuming, and automated systems fail to accurately identify clinically significant urinary sediments like casts and acanthocytes, leading to flawed clinical decisions.
Innovation Solution
A computer-implemented method using augmented intelligence and machine learning models to analyze urinary samples, including image classification, sediment detection with bounding boxes, and generation of clinical reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized manual analysis by experts is used, then identification accuracy is improved, but accessibility and time consumption worsen
Solution Approach 1:
The patent creates a digital copy of the expert analysis capability through machine learning models trained on expert-labeled urinary sediment images. The trained model replicates expert identification accuracy and makes it accessible through a web application, eliminating the need for physical presence of specialists at each testing location.
Solution Approach 2:
The patent replaces the mechanical system of manual expert examination with an automated computer vision system. The machine learning model automatically processes urinary sediment images, detecting and classifying sediments without human intervention, thereby improving accessibility while maintaining identification accuracy.
2Measurement precision
If specialized manual analysis by experts is used, then identification accuracy is improved, but time consumption worsens
Solution Approach 1:
The patent implements a self-service system where the machine learning model autonomously performs sediment detection and classification without requiring expert intervention. The system automatically processes images, generates results, and provides clinical interpretations, eliminating the time-consuming manual review process while maintaining high accuracy through trained algorithms.
Solution Approach 2:
The patent replaces the time-consuming manual expert examination process with an automated computer vision system that processes images rapidly. The machine learning model performs detection and classification in seconds, dramatically reducing time consumption while preserving identification accuracy through sophisticated algorithmic analysis.
3Productivity
If automated urine analyzers are used, then productivity is improved, but measurement precision worsens
Solution Approach 1:
The patent creates a digital copy of expert analytical capabilities through machine learning models trained on comprehensive datasets of urinary sediments including casts and acanthocytes. This copied expertise enables automated analysis to achieve measurement precision comparable to or exceeding traditional methods while maintaining high productivity through rapid processing.
Solution Approach 2:
The patent changes the analytical parameters by using deep learning algorithms that can detect subtle features and patterns in urinary sediment images that traditional automated analyzers miss. The model learns optimal detection thresholds and classification criteria from training data, improving measurement precision for identifying clinically significant sediments while maintaining automated productivity.
Data Source
AI summary
A computer-implemented method of performing, by a system of augmented intelligence, urinary analysis implemented on one or more processors and associated memory is provided. The method includes a first step of receiving an image of urine microscopy sample. The method includes a second step of detecting microscope light and a magnification technique. The method includes a third step of classifying said image according to the presence of urinary sediments by a machine learning model. The method includes a fourth step of identifying sediments in said image by said trained machine learning model if urinary sediments are present. The method includes a fifth step of classifying said urinary sediments by said trained machine learning model. The method includes a sixth step of generating a report of a presence or an absence of clinically significant urinary sediments in said urine microscopy sample.