AI Image Segmentation for Objective Brain Atrophy Diagnosis
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Solution Overview
Problem
Traditional methods for diagnosing brain atrophy in elderly individuals rely heavily on subjective doctor judgment and lack quantitative indicators, making it difficult to assess organ or tissue conditions accurately and track variations over time.
Innovation Solution
A system and method that segment specific regions from medical images using artificial intelligence models, such as deep learning, to compare morphological characteristic values with reference standards, enabling objective assessment and tracking of organ or tissue conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional subjective doctor judgment methods are used for diagnosis, then the diagnostic process is simple and quick, but the measurement precision and reliability of diagnosis are insufficient
Solution Approach 1:
The patent replaces the mechanical system of subjective doctor judgment with an automated image processing system that uses computer algorithms to objectively analyze medical images. The system automatically segments images, extracts features, and generates diagnostic assessments, eliminating reliance on doctor's subjective experience while improving measurement precision.
Solution Approach 2:
The patent introduces an intermediary automated analysis system between the medical image and the final diagnosis. This intermediary system processes images through standardized algorithms, extracting quantitative features and comparing them against reference data, thereby providing an objective bridge that enhances diagnostic reliability without requiring direct subjective interpretation.
2Reliability
If quantitative indicators are introduced to improve diagnosis accuracy, then the reliability of diagnosis is improved, but the ease of operation and simplicity of the diagnostic process deteriorate
Solution Approach 1:
The patent implements self-service functionality where the system automatically performs image segmentation, feature extraction, and diagnostic assessment without requiring manual intervention. The automated pipeline processes images through predefined algorithms and generates quantitative indicators independently, maintaining operational simplicity while enhancing reliability through consistent quantitative analysis.
Solution Approach 2:
The patent performs preliminary actions by pre-processing images and pre-extracting features before the actual diagnostic assessment. Reference standards and normal value ranges are pre-established, allowing the system to quickly compare patient data against these prepared references, thereby maintaining ease of operation while ensuring reliable quantitative comparison.
3Productivity
If follow-up data of images are to be used effectively for analyzing variation trends, then the productivity and efficiency of monitoring are improved, but the device complexity and difficulty of data processing increase
Solution Approach 1:
The patent creates a universal data processing framework that handles multiple types of medical images and various diagnostic parameters through the same automated pipeline. The system can process different image modalities and time points using consistent algorithms, enabling effective utilization of follow-up data for trend analysis without requiring separate complex processing systems for each data type.
Solution Approach 2:
The patent applies parameter changes by normalizing and standardizing image features across different time points and examinations. Through consistent feature extraction and comparison against reference ranges, the system transforms varied follow-up data into comparable quantitative parameters, enabling efficient trend analysis while managing data processing complexity through standardized transformations.
Data Source
AI summary
A method for assessing a condition of an organ or tissue of a target object is provided. The method may include: obtaining a target image of the target object; segmenting a target region from the target image, the target region of the target image corresponding to a sub-region of the organ or tissue; determining a morphological characteristic value of the target region in the target image; obtaining a reference standard associated with a sample organ or tissue of a plurality of sample objects, the sample organ or tissue being of a same type as the organ or tissue of the target object; and assessing the condition of the organ or tissue of the target object by comparing the morphological characteristic value of the target region in the target image with the reference standard.