Automatic Pathology Detection Using Dynamic Feature Classification
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Solution Overview
Problem
Current methods for detecting and classifying pathologies in imaging scans rely heavily on human expertise, leading to inefficiencies, misclassification, and limited accessibility, especially in cases where expert professionals are scarce or unavailable, and are prone to interpretation errors and inter-observer variation.
Innovation Solution
A computer-based system that automatically detects and quantifies pathology patterns in scanned images using a dictionary of features generated from image data, allowing for unsupervised classification and eliminating the need for expert input, capable of identifying patterns that may not be detectable by human eyes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts review imaging scans to identify and classify pathologies, then diagnostic accuracy can be maintained through expert judgment, but the process consumes excessive time and is not readily accessible to all patients
Solution Approach 1:
The patent creates a computational model that copies and replicates the decision-making process of human experts through machine learning algorithms. The system learns from annotated training data to reproduce expert-level pathology identification and classification, enabling automated review that maintains diagnostic accuracy while dramatically reducing time requirements
Solution Approach 2:
The patent replaces the mechanical system of human expert review with an automated computational system. Machine learning models process imaging scans algorithmically, substituting human cognitive processes with computational operations that can be performed rapidly and consistently without fatigue or subjective variation
2Reliability
If human experts are required to review imaging scans for pathology detection, then diagnostic quality can be ensured, but accessibility to healthcare is limited due to scarcity of experts
Solution Approach 1:
The patent enables the imaging scan review system to serve itself by automating the entire diagnostic workflow. The machine learning system independently performs scan analysis, pathology detection, and classification without requiring human expert intervention for each case, making the service broadly accessible while maintaining consistent quality through algorithmic reliability
3Measurement precision
If multiple expert opinions are obtained to ensure accurate pathology classification, then diagnostic accuracy improves, but time consumption and cost increase
Solution Approach 1:
The patent merges multiple expert perspectives into a single trained model by incorporating diverse training data and using ensemble learning techniques. The system combines the knowledge patterns of multiple experts during the training phase, then delivers unified, consistent classifications that reflect aggregated expert wisdom without requiring multiple sequential reviews
4Adaptability or versatility
If human experts manually review imaging scans to detect pathology changes over time, then patient care can be personalized, but the process is subjective and incapable of reviewing all necessary scans
Solution Approach 1:
The patent transforms the subjective assessment process into an objective parameter-based analysis. The system quantifies pathology features using standardized metrics and compares them across time points, eliminating inter-observer variation. This allows consistent, objective tracking of pathology changes while maintaining the ability to adapt to different patient conditions through configurable analysis parameters
Data Source
AI summary
Methods, devices, and systems are provided for quantifying an extent of various pathology patterns in scanned subject images. The detection and quantification of pathology is performed automatically and unsupervised via a trained system. The methods, devices, and systems described herein generate unique dictionaries of elements based on actual image data scans to automatically identify pathology of new image data scans of subjects. The automatic detection and quantification system can detect a number of pathologies including a usual interstitial pneumonia pattern on computed tomography images, which is subject to high inter-observer variation, in the diagnosis of idiopathic pulmonary fibrosis.