Sensor-Integrated Urinary Catheter for AI-Based Infection Monitoring
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Solution Overview
Problem
Current urinary catheters increase the risk of urinary tract infections due to prolonged use and are inefficient in data processing, leading to misdiagnosis and high misdiagnosis rates, particularly in vulnerable populations.
Innovation Solution
A body fluid movement apparatus with integrated sensors and an AI system that reduces or eliminates noisy data, providing real-time health monitoring and diagnostic capabilities for conditions such as urinary tract infections, kidney disorders, and diabetes progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If urinary catheters are used for prolonged periods to manage urinary retention and incontinence, then patient care is improved, but the risk of urinary tract infections increases
Solution Approach 1:
The system performs preliminary monitoring and analysis of sensor data to detect early signs of infection or complications before they develop into serious problems. The AI engine continuously processes sensor readings to predict potential issues, allowing for preventive intervention that reduces infection risk during prolonged catheter use.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor various parameters (temperature, pressure, flow rate) and the AI engine analyzes this data in real-time. This feedback mechanism allows the system to detect changes indicating infection risk and alert healthcare providers, enabling timely intervention to prevent infections during extended catheterization.
2Productivity
If traditional clinical observation methods are used for health monitoring, then device complexity is minimized, but diagnostic efficiency and accuracy deteriorate
Solution Approach 1:
The system merges multiple sensor types (temperature, pressure, flow rate sensors) into a single integrated catheter assembly. This consolidation allows comprehensive monitoring through one device rather than multiple separate instruments, improving diagnostic efficiency while managing complexity through integration rather than multiplication of components.
Solution Approach 2:
The AI engine performs automated analysis of sensor data without requiring constant physician intervention. The system self-monitors patient conditions, automatically detects anomalies, and generates alerts only when intervention is needed. This reduces the burden on healthcare providers and improves diagnostic efficiency by continuously processing data without adding proportional complexity to the monitoring system.
3Measurement precision
If sensor data is collected continuously for accurate diagnosis, then measurement precision is improved, but data quality deteriorates due to noise
Solution Approach 1:
The AI engine serves as an intermediary between the raw sensor data and the diagnostic interpretation. It processes, filters, and cleans the noisy sensor readings, extracting meaningful information while removing artifacts and noise. This intermediary processing layer preserves measurement precision by selectively enhancing valid signals while discarding noise, thereby maintaining data quality despite continuous collection.
4Loss of information
If multiple sensors are integrated into the catheter for comprehensive monitoring, then information completeness is improved, but device complexity increases
Solution Approach 1:
The catheter is designed with multi-functional sensors that can detect multiple parameters (temperature, pressure, flow rate) using integrated sensor arrays. Each sensor serves multiple diagnostic purposes, and the AI engine analyzes combinations of these parameters together. This universal approach provides comprehensive information about patient status without requiring separate specialized sensors for each measurement, thereby limiting the increase in device complexity.
Data Source
AI summary
A body fluid movement apparatus includes a body fluid movement apparatus tube with a lumen, a proximal end, a distal end and a balloon coupled to the proximal end. The balloon is configured to be positioned in an interior of a bladder. The proximal end is configured to provide flow of body fluid from the bladder through the lumen, with a draining bag collecting body fluid from the bladder through the lumen. The drainage bag has an inlet port for receiving body fluid and an outlet port for draining body fluid from the drainage bag. The urinary catheter tube includes the proximal end and the proximal end, with a plurality of body fluid draining holes that receive body fluid from the bladder and allow it to be transported to and though the body fluid movement apparatus tube. One or more sensors are positioned in an interior of the catheter tube and are in contact with the patient's urine. The one or more sensors provide sensor data, at least a portion of sensor data being noisy data that contains one or more of errors, outliers, and inconsistencies. Logic resources provide preprocessing of the noisy data to create cleaned sensor data used for one or more of: identification, cleaning, and transforming of noisy data for the machine learning algorithms to produce the cleaned sensor data. An artificial intelligence system coupled to or including an AI database. The AI engine. with a plurality of machine learning algorithms, provide analysis of the cleaned sensor data used for medical monitoring of one or more medical conditions of the patient by the machine learning algorithms, the analysis of the cleaned sensor data being used for the medical monitoring of the patient.