Machine Learning Anatomical Dimension Prediction
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Solution Overview
Problem
Conventional methods for measuring anatomical dimensions are inefficient and imprecise, often requiring external equipment and facilities, and lack systematic learning procedures to enhance accuracy.
Innovation Solution
The use of machine learning algorithms on mobile devices to digitize anatomical landmarks, determine linear dimensions, and make anatomical predictions based on trained models, eliminating the need for external equipment and improving precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional methods are used for measuring anatomical dimensions, then external equipment and facilities are required, but this increases device complexity and limits accessibility
Solution Approach 1:
The patent extracts the measurement functionality from complex external equipment and facilities, consolidating it into a portable device with integrated camera, display, and processor that can perform anatomical measurements without requiring specialized external equipment
Solution Approach 2:
The portable device is designed to perform multiple functions including capturing images, displaying reference lines, calculating pixel-to-distance ratios, and determining postural deviations, replacing multiple specialized devices with a single multi-functional unit
2Productivity
If conventional postural analysis methods are used, then time-consuming manual measurements are required, but this reduces productivity and efficiency
Solution Approach 1:
The patent replaces manual mechanical measurement processes with automated computer vision technology, using image processing algorithms to automatically detect anatomical landmarks and calculate postural deviations, eliminating time-consuming manual measurements
Solution Approach 2:
The system performs preliminary actions by pre-programming the pixel-to-distance ratio calculation and automated landmark detection algorithms, enabling rapid analysis without requiring manual setup or calibration during the measurement process
3Measurement precision
If conventional image analysis is used, then human interaction with digital displays can result in errors, but this reduces measurement precision
Solution Approach 1:
The system performs self-service by automatically processing images and calculating measurements without requiring human interaction with the digital display, using automated algorithms to detect landmarks and compute results, eliminating human error in the measurement process
Solution Approach 2:
The system incorporates feedback mechanisms by displaying reference lines and measurement results on the screen, allowing verification and adjustment while maintaining automated precision, combining machine accuracy with human oversight
Data Source
AI summary
A method for use of machine learning in computer-assisted anatomical prediction. The method includes identifying with a processor parameters in a plurality of training images to generate a training dataset, the training dataset having data linking the parameters to respective training images, training at least one machine learning algorithm based on the parameters in the training dataset and validating the trained machine learning algorithm, identifying with the processor digitized points on a plurality of anatomical landmarks in an image of a person displayed on a digital touch screen by determining linear anatomical dimensions of at least a portion of a body of the person in the displayed image using the validated machine learning algorithm and a scale factor for the displayed image, and making an anatomical circumferential prediction of the person based on the determined linear anatomical dimensions and a known morphological relationship.