Fluid Canister Image Analysis for Accurate Blood Loss Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for estimating blood loss during surgeries often result in overestimation or underestimation, leading to increased costs, wasted resources, and potential health risks, with a need for a more accurate and efficient method to quantify blood components in fluid canisters.
Innovation Solution
A system and method utilizing machine vision to analyze images of fluid canisters, identifying reference markers, estimating fluid volume and concentration of blood components, and correlating these to determine the quantity of blood components, which can be implemented on various computing devices or standalone systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual estimation methods are used for blood loss monitoring, then the process is simple and quick, but the accuracy of blood loss measurement is poor leading to overestimation or underestimation
Solution Approach 1:
The patent replaces manual visual estimation with an automated image processing system that captures images of the canister, processes them through algorithms to identify blood components, and calculates volume measurements. This substitution of mechanical/manual assessment with automated optical and computational systems directly improves measurement accuracy while managing system complexity through software automation.
Solution Approach 2:
The system creates a digital copy (image) of the physical canister contents and analyzes this copy through image processing techniques. By working with the optical copy rather than direct manual measurement, the system achieves more precise quantification of blood components while reducing human error in estimation.
2Measurement precision
If automated image processing is used to analyze canister contents, then measurement accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by capturing images at specific intervals during surgery rather than requiring continuous real-time analysis. The image processing algorithms are pre-configured with reference data and processing protocols, allowing rapid analysis when images are captured. This approach balances accuracy with time efficiency by avoiding continuous processing while maintaining measurement precision at key moments.
Solution Approach 2:
The system focuses image processing resources on analyzing only the relevant portions of the canister contents that contain blood components, rather than processing the entire image in detail. By applying partial action to the most critical areas, the system achieves high measurement accuracy while reducing overall processing time and computational burden.
3Reliability
If manual blood loss estimation is used, then resource consumption is low, but transfusion decisions may be incorrect due to inaccurate measurements
Solution Approach 1:
The system provides continuous feedback by monitoring blood component quantities in the canister and making this information available for clinical decision-making. The automated measurements feed back into the clinical workflow, allowing physicians to make more reliable transfusion decisions based on objective data rather than subjective estimation, thereby improving the reliability of transfusion timing and quantity decisions.
Solution Approach 2:
The monitoring system performs self-service by automatically capturing images, processing them through algorithms, and generating volume measurements without requiring constant manual intervention. This automation reduces the complexity burden on clinical staff while providing reliable data for decision-making, as the system serves itself by performing the measurement tasks that would otherwise require human effort.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A variation of a method for estimating a quantity of a blood component in a fluid canister includes: analysing an image of a fluid canister to estimate a fluid volume within the canister and a concentration of a blood component; and estimating a quantity of the blood component within the canister based on the estimated volume and the concentration of the blood component within the canister.