Ultrasound Fetal Growth Prediction Using Cloud Biometric Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting fetal growth rate using ultrasound imaging are limited by outdated gestational age and fetal growth tables that do not account for updated data and geographical variations, leading to low prediction accuracy.
Innovation Solution
An ultrasound imaging apparatus that utilizes a cloud server to receive and update fetal biometric data, allowing for real-time prediction of fetal growth rate based on actual measurements and comprehensive big data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-compiled GA and fetal growth tables are used for prediction, then the system is simple to operate, but the prediction accuracy is low due to outdated data and limited sample size
Solution Approach 1:
A cloud server acts as an intermediary between the ultrasound imaging apparatus and the fetal growth prediction system. The cloud server stores and processes large-scale fetal biometric data from multiple sources, performing complex data analysis remotely while the local apparatus only needs to transmit measurement data and receive prediction results, thus improving accuracy without significantly increasing local system complexity
Solution Approach 2:
The system transitions from using static, two-dimensional pre-compiled tables to a dynamic, multi-dimensional cloud-based database that incorporates geographical locations, multiple ultrasound apparatus data, and continuously updated fetal biometric information, enabling more accurate predictions through expanded data dimensions
2Adaptability or versatility
If pre-compiled tables with limited sample data are used, then the device complexity is low, but the adaptability to different geographical locations and populations is poor
Solution Approach 1:
The cloud server provides a universal platform that collects and processes fetal biometric data from multiple different ultrasound imaging apparatuses across various geographical locations. This multi-functional system serves diverse populations and regions simultaneously, making the prediction system adaptable to different geographical variations without requiring separate local systems for each region
Solution Approach 2:
The system implements feedback mechanisms where fetal biometric measurement data from ultrasound examinations is continuously transmitted to the cloud server, which then updates and refines the fetal growth prediction models based on accumulated real-world data, enabling the system to adapt and improve over time based on actual observations from different populations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves fetal growth prediction accuracy by using up-to-date fetal biometric data from a cloud server, reflecting individual and geographical variations, thereby enhancing the precision of fetal development assessment.
Implementation Method 1
transmit ultrasound signals generated by transducers of a probe to an object and receive information of echo signals reflected from the object
Implementation Method 2
receive information of echo signals reflected from the object
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Provided is an ultrasound imaging apparatus for predicting fetal growth rate, including: an ultrasound probe configured to transmit ultrasound signals to a fetus and receive ultrasound echo signals reflected from the fetus; a user inputter configured to receive pregnancy information regarding a patient from a user; a communicator configured to receive, from a cloud server, fetal biometric data related to the pregnancy information regarding the patient from among fetal biometric data prestored and accumulated in the cloud server; and a controller configured to generate an ultrasound image of the fetus by using the ultrasound echo signals, measure a size of a body part of the fetus on the ultrasound image, and predict the fetal growth rate based on the measured size of the body part of the fetus and the fetal biometric data received from the cloud server.