Image-Based Modeling Platform for Clinical Variable Prediction
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Solution Overview
Problem
Manual interpretation of medical images for detecting and classifying diseases is time-consuming, requires radiological expertise, and is prone to inter-reader variability, limiting the efficiency and accuracy of clinical trials and healthcare treatments.
Innovation Solution
An image-based modeling platform that utilizes machine learning models, such as convolutional neural networks, to analyze medical images, predict clinical variables, and generate predictive models for mortality risk scores, treatment outcomes, and treatment planning, reducing the need for manual annotation and radiological expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation and annotation by radiologists is used, then detection accuracy and clinical expertise are maintained, but time consumption and inter-reader variability increase
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the patient's anatomy from medical images that can be repeatedly analyzed without additional time cost. This digital model serves as a replicable artifact that eliminates the need for repeated manual interpretations while maintaining consistent detection accuracy across multiple analyses.
Solution Approach 2:
The system performs preliminary automated annotation and region-of-interest identification before clinical review, pre-processing the images to highlight relevant features. This preliminary action reduces the time radiologists need to spend on basic identification tasks while maintaining their expertise-based detection accuracy.
2Reliability
If manual annotation by radiologists is used, then radiological expertise is utilized, but productivity and efficiency decrease
Solution Approach 1:
The patent introduces an automated image analysis algorithm as an intermediary between the raw medical images and the radiologist. This intermediary performs preliminary analysis, annotation, and feature extraction, allowing radiologists to focus on higher-level interpretation tasks that require their expertise, thereby improving overall workflow efficiency without compromising reliability.
Solution Approach 2:
The system segments the radiologist's workflow into automated components (image preprocessing, initial annotation, feature detection) and manual components (clinical interpretation, final diagnosis). This segmentation allows routine tasks to be automated for improved productivity while preserving radiological expertise for complex decision-making.
3Measurement precision
If only annotated regions of interest are correlated with outcomes, then analysis focus is maintained, but measurement completeness and information loss increase
Solution Approach 1:
The patent creates a universal digital twin model that can serve multiple functions: it preserves annotated regions of interest for focused analysis while simultaneously retaining all original image data and enabling additional analyses. This multi-functional model eliminates information loss by maintaining both the focused ROI data and the complete dataset for comprehensive outcome correlation.
Solution Approach 2:
The system transforms the data representation from limited annotated regions to a comprehensive digital model that preserves multiple parameters and features. By changing the data structure to a more complete digital twin representation, the system maintains analytical focus on ROIs while preventing information loss through comprehensive data retention and flexible parameter extraction.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining pre-treatment images; analyzing the pre-treatment images according to an imaging model that includes a machine learning model; predicting, according to the analyzing the pre-treatment images, one or more clinical variables; obtaining on-treatment images; analyzing the on-treatment images according to the imaging model; predicting, based on the analyzing the on-treatment images, the one or more clinical variables for the on-treatment images; and presenting event estimation information in a graphical user interface. Other embodiments are disclosed.