Deep Learning Diagnostic System for IPF Pattern Classification

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Solution Overview

Problem

The diagnosis of idiopathic pulmonary fibrosis (IPF) and interstitial lung disease (ILD) is hindered by inter-observer variation in visual identification of UIP patterns on HRCT images, leading to potential misdiagnosis, especially when less experienced radiologists are involved.

Innovation Solution

A computer-based diagnostic system utilizing deep machine learning with trained convolutional neural networks to process tomographic images, classify organ regions, and provide quantitative and qualitative diagnostic results, reducing reliance on human expertise.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If visual identification of UIP patterns is performed by radiologists, then diagnostic capability is achieved, but inter-observer variation and misdiagnosis occur

Engineering Contradiction:

Improvediagnostic reliabilityVSAvoidpattern identification accuracy

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The patent replaces the mechanical visual inspection process performed by radiologists with an automated image processing system using convolutional neural networks. The system automatically analyzes HRCT images to identify UIP patterns, eliminating human subjectivity and inter-observer variation while maintaining diagnostic capability through machine-based pattern recognition

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The diagnostic system performs self-service by automatically processing and analyzing medical images without requiring manual radiologist intervention for pattern identification. The convolutional neural network independently executes the diagnostic analysis, generating results that reduce reliance on human expertise and minimize diagnostic errors

Inventive Principle:

Principle #25Self-service

2Measurement precision

If deep machine learning processing is implemented, then diagnostic accuracy is improved, but system complexity increases

Engineering Contradiction:

Improvepattern classification accuracyVSAvoidsystem structural complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent implements a multi-functional deep learning system where convolutional neural networks perform multiple diagnostic tasks including image processing, pattern recognition, and classification within a single integrated framework. This universal approach handles various lung disease patterns simultaneously, improving diagnostic accuracy while consolidating system functions rather than increasing complexity through multiple separate systems

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS10910094B2Computer-based diagnostic system

Publication Date: 2021.02.02 SIEMENS HEALTHINEERS AG

AI summary

A computer-based diagnostic system includes an image generation unit adapted to generate tomographic images of a patient's organ; a deep machine learning unit configured to process generated tomographic images of the patient's organ to classify organ regions of diseased functional tissue of the patient's organ as belonging to one of a set of abnormal image patterns using trained deep neural networks; and a clinical decision support unit adapted to process classification results of the deep machine learning unit to calculate a diagnostic result output via an interface of the clinical decision support unit.