Stool Condition Detection Using Machine Learning Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Self-assessment of stool conditions through visual inspection is subjective and inconsistent, lacking robustness for periodic evaluations and accurate identification of medical conditions related to bowel movements.
Innovation Solution
A computer-readable medium and method for determining stool conditions by receiving images of stool, determining characteristics such as shape, texture, consistency, fragmentation, and volume, and performing a stool assessment using machine learning algorithms to correlate with medical conditions like Irritable Bowel Syndrome, Crohn's Disease, and Ulcerative Colitis, and providing intervention recommendations based on diet, lifestyle, or medication changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If self-assessment of stool conditions through visual inspection is used, then the evaluation process is simple and accessible, but the assessment consistency and accuracy deteriorate due to subjectivity
Solution Approach 1:
The patent replaces the manual visual inspection mechanism with an automated image processing system using machine learning algorithms. The system captures stool images and automatically analyzes characteristics such as shape, texture, consistency, fragmentation, and volume, eliminating human subjectivity while maintaining ease of use through automated processing.
Solution Approach 2:
The patent creates digital copies of stool samples through imaging technology, allowing multiple analyses of the same sample without physical manipulation. This enables consistent measurement of stool characteristics across different evaluations while preserving the original sample integrity.
2Measurement precision
If multiple stool characteristics are analyzed using machine learning, then the diagnostic accuracy improves, but the system complexity increases
Solution Approach 1:
The patent implements a multi-functional machine learning system that simultaneously analyzes multiple stool characteristics (shape, texture, consistency, fragmentation, volume) and performs various diagnostic functions. This single system handles diverse analytical tasks, improving diagnostic accuracy without proportionally increasing complexity through integrated processing.
Solution Approach 2:
The patent divides the complex diagnostic task into separate analytical modules, each focusing on specific stool characteristics. The machine learning system processes different features independently and integrates results, making the overall system more manageable and interpretable while maintaining high diagnostic accuracy.
3Reliability
If automated image-based stool assessment is implemented, then assessment consistency improves, but the time required for processing increases
Solution Approach 1:
The patent performs preliminary processing of stool images by pre-processing steps such as normalization, enhancement, and feature extraction before main analysis. This preparation work is done automatically and efficiently, reducing the time required for the core diagnostic assessment while maintaining consistent results.
Solution Approach 2:
The patent implements periodic processing where stool images are analyzed at scheduled intervals or triggered by specific events. This allows batch processing of multiple images, reducing overall processing time while maintaining assessment consistency through standardized periodic evaluation protocols.
Data Source
AI summary
Disclosed herein, in some aspects, are systems and methods for determining and/or monitoring a stool condition for a subject. In some embodiments, the stool condition is based on one or more images of stool of a subject. In some embodiments, the stool condition correlates with a stool assessment comprising i) a characterization of the stool according to a plurality of characteristics, and/or ii) identifying one or more medical conditions, illnesses, and/or diseases associated with the stool. In some embodiments, the stool condition is determined using one or more Artificial Intelligence engines using a trained data set. In some embodiments, the stool condition is based on one or more stool assessments performed for one or more stools corresponding to one or more bowel movements over a period of time.