Endoscope Image Optimization via Self-Learning Contamination Detection

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Solution Overview

Problem

Endoscopes face challenges in maintaining optimal image quality during medical procedures due to contamination, such as blood splashes, tissue particles, and steam, which can impair the operator's vision and require extracorporeal cleaning, disrupting the procedure.

Innovation Solution

A method for image analysis and optimization using a self-learning module that automatically detects contamination and issues control instructions for image optimization, including activation of a cleaning module to clear the distal window of the endoscope, thereby maintaining clear visibility.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If extracorporeal cleaning of the endoscope is performed manually, then the contamination can be removed, but the procedure is interrupted and time is lost

Engineering Contradiction:

Improveimage qualityVSAvoidprocedure duration

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The endoscope system performs its own cleaning automatically through an integrated cleaning module that can be activated via control unit, eliminating the need for manual extracorporeal cleaning and procedure interruptions. The system detects contamination and cleans itself intracorporeally.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously monitors image quality parameters and detects contamination before it severely impacts the procedure. The cleaning module is activated proactively to maintain optimal visibility throughout the endoscopic procedure without waiting for critical contamination to occur.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If the endoscope is cleaned extracorporeally, then the distal window can be cleared, but the operator's vision is obstructed during cleaning

Engineering Contradiction:

ImprovevisibilityVSAvoidoperator workflow

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The cleaning operation is performed automatically by the endoscope's own cleaning module without requiring operator intervention or distraction. The system manages the cleaning process autonomously, maintaining continuous visibility for the operator.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical cleaning process is replaced with an automated control system that manages the cleaning module activation, timing, and coordination with the endoscopic procedure, eliminating the need for manual cleaning operations.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Extent of automation

If automatic image optimization is implemented, then contamination can be detected and corrected automatically, but the device complexity increases

Engineering Contradiction:

Improvecleaning automationVSAvoidsystem structure

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The cleaning module serves multiple functions: it can be activated manually by the operator, automatically by the control unit based on image quality analysis, or in response to detected contamination events. This multi-functionality consolidates multiple control approaches into a single integrated system.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The control unit continuously receives feedback from the image acquisition device about image quality parameters and automatically adjusts cleaning module activation accordingly. This closed-loop feedback system enables automatic optimization based on real-time visual conditions.

Inventive Principle:

Principle #23Feedback

4Measurement precision

If continuous monitoring of image quality is performed, then contamination can be detected early, but the processing requirements and system complexity increase

Engineering Contradiction:

Improvecontamination detection accuracyVSAvoidimage processing system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system analyzes specific image quality parameters (brightness, contrast, sharpness) rather than processing every pixel of every image in detail. This selective analysis approach provides sufficient contamination detection capability while reducing computational complexity and processing requirements.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250049294A1Method, system and trained model for image optimization for endoscopes

Publication Date: 2025.02.13 KARL STORZ SE & CO KG

AI summary

A method and a control system for image analysis and optimization of at least one image acquired at the distal end of an endoscope includes the following steps: acquiring image data by means of an image acquisition device of an endoscope; receiving the image data by a control unit, pre-analyzing at least a subset of the image data of one or more consecutive images by a control unit to determine at least one image structure; determining a quality value by means of a training data-based, preferably self-learning, module by comparing the at least one determined image structure with image structures of a reference database stored in a memory unit, and on the basis of the determined quality value, manually or automatically outputting control instructions from the control unit to a unit for activating image optimization.