Premorbid Bone Modeling With GAN Feedback for Orthopedic Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Obtaining premorbid bone models for orthopedic surgery planning is challenging due to the difficulty in acquiring sufficient training data that accurately reflects the 'before' and 'after' states of bones affected by pathology, as patients rarely undergo medical imaging prior to the onset of conditions like osteoarthritis or trauma, making it hard for machine learning models to map morbid bone models to appropriate premorbid models.
Innovation Solution
A generative adversarial network (GAN) is trained through multiple iterations to generate premorbid bone models by using a discriminator model to improve the confidence in distinguishing between generated and real premorbid bone models, allowing the generation of premorbid bone models without relying on extensive datasets of both morbid and premorbid bone models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used to map morbid bone models to premorbid models, then the model can process available imaging data, but the accuracy is insufficient due to lack of premorbid imaging data for training
Solution Approach 1:
The GAN generator is trained in advance to learn the mapping from morbid to premorbid bone models using only available morbid imaging data. The discriminator is pre-trained to recognize authentic premorbid models. This preliminary training enables the system to generate accurate premorbid models without requiring actual premorbid patient data.
Solution Approach 2:
The discriminator provides feedback to the generator during training by evaluating generated premorbid models and providing confidence scores. This feedback loop allows the generator to iteratively improve its model generation accuracy, learning from the discriminator's assessments of what constitutes a realistic premorbid bone model.
2Measurement precision
If a GAN is trained with discriminator feedback to generate premorbid bone models, then the accuracy and confidence level improve, but the training complexity and computational requirements increase
Solution Approach 1:
The training system is divided into two separate but coordinated components: a generator model that creates premorbid bone models and a discriminator model that evaluates them. This segmentation allows each component to be optimized independently while working together to achieve high accuracy in premorbid model generation.
3Reliability
If extensive datasets of both morbid and premorbid bone models are collected for training, then the machine learning model can be trained more accurately, but it becomes difficult to obtain sufficient premorbid data since patients rarely undergo imaging prior to pathology onset
Solution Approach 1:
Instead of requiring actual premorbid patient data, the system uses the GAN generator to create synthetic copies of premorbid bone models based on available morbid imaging data. These generated models serve as reliable training data without requiring physical collection of historical patient images.
Data Source
AI summary
A method comprising: for each respective training iteration of a plurality of training iterations: applying a generator machine learning (ML) model of a generative adversarial network (GAM) to input data to generate an output bone model for tire respective training iteration: applying a second ML model of the GAN to the output bone model to generate a discriminator output for the respective training iteration, wherein the discriminator output for the respective training iteration comprises a level of confidence that the first ML model generated the output bone model: determining a loss value for the respective training iteration based on the discriminator output tor the respective training iteration; and updating parameters of the first ML model or the second ML model based on the loss value for the respective training iteration.