Ultrasound Bladder Volume Measurement with ML Parameter Extraction
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Solution Overview
Problem
Existing bladder volume measurement methods lack accuracy in determining optimal parameters for precise volume calculation using ultrasound images.
Innovation Solution
A bladder volume measurement device employing a feature providing unit to extract multiple feature factors, a parameter determining unit for selecting optimal parameters through machine learning, and a volume calculating unit to accurately measure bladder volume based on these parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple feature factors are extracted from ultrasound images, then the information available for volume calculation increases, but the complexity of determining optimal parameters increases
Solution Approach 1:
The patent extracts and selects only the most relevant feature factors (such as maximum diameter, minimum diameter, and bladder volume) from the multitude of available ultrasound image parameters. By using machine learning to identify and extract only the essential features rather than processing all possible parameters, the system reduces computational complexity while maintaining comprehensive information utilization.
Solution Approach 2:
The patent transforms raw ultrasound image data into standardized volumetric parameters through machine learning models. The system changes parameters from raw pixel data to calculated volume measurements, automatically determining optimal parameter combinations that maximize measurement accuracy while minimizing processing complexity.
2Ease of operation
If traditional measurement methods are used, then the measurement process is simple, but the accuracy of bladder volume measurement is insufficient
Solution Approach 1:
The patent implements self-service through automated machine learning models that automatically determine optimal measurement parameters without requiring manual intervention. The system self-calibrates by learning from training data and automatically selects the best feature combinations, maintaining operational simplicity while significantly improving measurement accuracy compared to traditional manual methods.
Solution Approach 2:
The patent replaces traditional manual measurement methods with automated machine learning algorithms. Instead of relying on simple geometric calculations or manual measurements, the system uses AI models to process ultrasound images and determine optimal volumetric parameters, achieving higher accuracy while keeping the user interface simple and easy to operate.
Data Source
AI summary
A bladder volume measuring device includes a feature providing unit, a parameter determining unit, and a volume calculating unit. The feature providing unit may provide a plurality of feature factors for calculating a volume of a bladder included in an ultrasound image. The parameter determining unit may determine a selection parameter corresponding to some of the feature factors. The volume calculating unit may calculate the volume of the bladder according to the selection parameter. The bladder volume measurement device using parameter extraction according to the present invention may extract an optimal selection parameter using a parameter determining unit machine-learning a plurality of feature factors extracted to calculate a volume of a bladder included in an ultrasound image and measuring the volume of the bladder based on the selection parameter, thereby more accurately measuring the bladder volume.