AI Image Segmentation for Kidney Size Estimation and Growth Prediction
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Solution Overview
Problem
Current computer vision techniques face challenges in isolating target objects from noisy images and accurately predicting future size changes, particularly in medical contexts like kidney disease diagnosis, due to issues with model complexity and the lack of effective baseline values for size estimation.
Innovation Solution
An AI-based image segmentation model is used to distinguish target objects from noise in images, generating binary images and size estimates, which are then input into a machine learning-based model to predict future sizes and growth rates, incorporating covariates for risk classification and treatment optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If computer vision techniques are used to identify target objects in noisy images, then object detection capability is improved, but model complexity and training requirements increase
Solution Approach 1:
The patent applies image segmentation to divide the complex task of target object detection into distinct phases: initial object identification, boundary detection, and size measurement. This segmentation allows the system to process noisy medical images systematically, reducing the overall complexity by breaking down the detection challenge into manageable steps that can be addressed by specialized sub-algorithms.
Solution Approach 2:
The patent introduces intermediate processing steps and auxiliary algorithms as mediators between the raw noisy images and the final detection results. These intermediaries include preprocessing filters, feature extraction modules, and validation layers that simplify the main detection task by preparing data in advance and filtering out noise before the primary object identification occurs.
2Measurement precision
If traditional size estimation methods are used, then baseline values can be obtained, but measurement precision and reliability are insufficient
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously refines size estimates by comparing predicted values against actual measurements from follow-up images. The machine learning model learns from these feedback loops, adjusting its predictions to improve both precision and reliability over time. This iterative refinement process allows the system to become more accurate without requiring increasingly complex measurement protocols.
Solution Approach 2:
The patent establishes baseline size measurements through preliminary image analysis and processing before actual disease progression monitoring begins. By pre-processing images to establish accurate baseline values using automated segmentation and measurement algorithms, the system creates a reliable reference point that improves subsequent measurement precision without requiring repeated complex measurements.
3Productivity
If manual image analysis is used, then interpretability is maintained, but productivity and time efficiency are reduced
Solution Approach 1:
The patent implements self-service automation where the system performs image segmentation, object identification, and size measurement automatically without requiring manual intervention at each step. The machine learning model autonomously processes images, generates measurements, and even identifies areas requiring review, significantly improving productivity while maintaining interpretability through automated documentation of the analysis process.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational algorithms. Instead of manual image browsing and measurement, the system uses automated image processing pipelines, machine learning-based object detection, and algorithmic size estimation that execute rapidly, reducing analysis time from minutes or hours to seconds while maintaining or improving measurement consistency.
4Measurement precision
If comprehensive covariate data is collected for prediction, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent creates a universal data processing framework that handles multiple types of covariate data (demographic, clinical, imaging) through a single integrated machine learning model. This multi-functional approach allows the system to process diverse data sources uniformly, improving prediction accuracy by incorporating comprehensive covariates while managing complexity through standardized processing pipelines that treat different data types consistently.
Data Source
AI summary
A system may access a source image of the subject. A system may execute an image segmentation model that uses the source image to distinguish the target object from among other objects in the source image. A system may generate a binary image of the target object based on execution of the image segmentation model. A system may generate, based on the binary image, a size estimate of the target object. A system may execute a machine learning-based model that uses the size estimate and one or more covariates to predict a future size of the target object. A system may determine a risk classification for the subject based on the predicted growth rate, the risk classification being based on a probability that the target object of the subject will result in a disease state, the risk classification to be used to determine a treatment regimen to treat or prevent the disease state.