CNN Infant Stool Classification System
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Solution Overview
Problem
Parents and caregivers face difficulties in objectively and accurately tracking and classifying the stool consistency of infants, as existing methods are cumbersome and prone to inter-observer variability, making it challenging to detect anomalies in digestive health.
Innovation Solution
A method utilizing a portable device with a camera to capture images of stool, which are processed by a pre-trained convolutional neural network (CNN) to provide fast and accurate classification results, eliminating the need for manual manipulation and reducing variability, with customizable CNN layers corresponding to stool analysis scale scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parents manually classify stool consistency using existing scales, then they can track stool patterns, but the process is difficult and prone to inter-observer variability
Solution Approach 1:
The patent replaces the manual mechanical classification process with an automated image recognition system using a convolutional neural network (CNN). The CNN processes stool images to automatically determine consistency scores, eliminating the need for parents to manually compare stool samples against reference scales. This substitution significantly improves measurement precision while maintaining ease of operation, as parents simply need to capture and submit images.
Solution Approach 2:
The patent uses digital images (copies) of stool samples instead of requiring direct physical examination or manual comparison with reference materials. The CNN analyzes these image copies to determine stool consistency, making the process easier for parents while maintaining or improving classification accuracy through automated image analysis.
2Reliability
If manual stool logging is performed, then stool patterns can be tracked over time, but the process is tedious and time-consuming
Solution Approach 1:
The patent replaces the tedious manual logging process with automated image analysis using a CNN. The system automatically processes stool images, determines consistency scores, and logs results without requiring parental intervention beyond image capture and submission. This automation maintains reliable tracking consistency while dramatically reducing the time required for stool analysis.
Solution Approach 2:
The system performs self-service by automatically analyzing stool images and generating consistency scores without requiring parental expertise or manual classification. The CNN independently processes images and produces results, eliminating the time-consuming manual logging process while ensuring consistent tracking over time.
3Productivity
If different caregivers manually assess stool consistency, then real-time tracking is possible, but inter-observer variability reduces objectivity
Solution Approach 1:
The patent replaces human observation with automated image analysis using a CNN. This substitution eliminates inter-observer variability by using a consistent algorithmic approach for all stool assessments, regardless of which caregiver performs the analysis. The CNN provides objective, reproducible results while maintaining real-time tracking capability across multiple caregivers.
Solution Approach 2:
The patent creates a universal system that works consistently across different caregivers through standardized image capture and automated CNN analysis. The system functions uniformly regardless of who performs the assessment, ensuring objectivity and consistency in stool consistency measurements across all users and settings.
Data Source
AI summary
The invention provides a method of analysing the consistency of stool, including the steps of: providing stool of an infant, capturing, with a portable device comprising a camera, an image of the stool, providing the captured image to an input layer of a pre-trained convolutional neural network, CNN, processing the captured image using the CNN to obtain, from a final layer of the CNN, a classification vector and to obtain information about a predicted score from the classification vector, wherein at least the final layer of the CNN has been customized so that each element of the classification vector corresponds to a score of a stool analysis scale, and storing information about the predicted store.