Automated Medical Image Anomaly Detection via Database Comparison

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Solution Overview

Problem

Current medical image analysis relies heavily on manual methods, which are inconsistent and prone to false negatives and false positives due to human error, and lack the necessary artificial intelligence to identify both recognizable and historically possible anomalies.

Innovation Solution

A method that mines statistical data from historical medical images to create a database of metrics, using automated software to compare new images with the database, identifying and highlighting regions of interest with anomalous characteristics, and presenting further details if anomalies are detected.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual methods are used for medical image analysis, then human expertise and judgment are applied, but accuracy and consistency deteriorate due to human error, fatigue, and subjectivity

Engineering Contradiction:

Improveaccuracy of anomaly detectionVSAvoidconsistency of analysis

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The patent replaces manual visual analysis with an automated image processing system that uses algorithms to detect anomalies. The system automatically processes medical images, compares them against learned patterns, and identifies abnormalities without human intervention, thereby eliminating human error and fatigue while maintaining high accuracy and consistency

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-learning by analyzing training data to automatically identify anomaly patterns. Once trained, the system independently analyzes new medical images without requiring continuous human guidance, achieving consistent and reliable results through automated decision-making based on learned characteristics

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If manual visual inspection is performed, then contextual understanding and clinical judgment are applied, but detection capability deteriorates for subtle or historically possible anomalies

Engineering Contradiction:

Improveability to detect various anomaly typesVSAvoiddetection precision of subtle anomalies

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system analyzes multiple parameters simultaneously including intensity, texture, shape, size, and spatial distribution of image features. By changing and combining these parameters in different ways, the system can detect subtle anomalies that may not be visually obvious, such as early-stage tumors or subtle tissue changes, achieving high detection precision across various anomaly types

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent segments the medical image into multiple regions and features, analyzing each separately and then integrating the results. This segmentation allows the system to focus on specific areas of interest and detect subtle anomalies within them, while maintaining the ability to understand overall context and identify various types of abnormalities

Inventive Principle:

Principle #1Segmentation

3Reliability

If automated algorithms are implemented, then consistency and accuracy improve, but device complexity increases

Engineering Contradiction:

Improveconsistency of analysis resultsVSAvoidcomplexity of image processing system

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary training using a large database of labeled medical images to learn anomaly patterns before actual analysis begins. This preliminary action consolidates complex detection logic into trained models, allowing the system to maintain high consistency and accuracy while reducing the apparent complexity during operation, as the algorithms are pre-optimized based on training data

Inventive Principle:

Principle #10Preliminary action

4Measurement precision

If comprehensive anomaly detection is performed, then detection capability improves, but processing time increases

Engineering Contradiction:

Improvecompleteness of anomaly detectionVSAvoidtime for image analysis

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system applies partial action by focusing computational resources on the most promising regions and features identified through preliminary analysis and pattern recognition. Rather than uniformly analyzing every pixel and feature with equal intensity, the system prioritizes areas with highest anomaly probability, achieving comprehensive detection of significant anomalies while reducing overall processing time through intelligent resource allocation

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS9177379B1Method and system for identifying anomalies in medical images

Publication Date: 2015.11.03 ATTI INT SERVICES

AI summary

Method and sequence for locating anomalous features in medical images in which medical images are supplied by an external source such as a CAT or XRAY scan machine or other similar device. A sequence of specific measurements is executed on the supplied data to obtain metrics relating to the images. The metrics are then compared to the corresponding values in an accompanying database resulting in an anomalous/not anomalous determination. Anomalous determinations are presented to the test operator for final analysis along with supplemental historical data. In application to all types of medical imagery, potential anomalies are quickly located resulting in an efficient and more accurate diagnosis.