Medical Device Analysis System Residual Contamination Prediction

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Solution Overview

Problem

Existing medical device analysis systems fail to accurately predict residual contamination status after cleaning, especially for complex multidimensional structural information and varying usage and cleaning conditions, which complicates the design of easy-to-clean medical devices.

Innovation Solution

A medical device analysis system utilizing a convolutional neural network (CNN) that divides multidimensional structural information into unit regions and generates secondary structural information to estimate residual contamination status, considering both structural and usage conditions, thereby improving prediction accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If multidimensional structural information is used to predict residual contamination status, then prediction accuracy is improved, but system complexity increases

Engineering Contradiction:

Improveprediction accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments multidimensional structural information into multiple types (geometric shape, material composition, surface roughness, porosity, connectivity) and processes them separately through dedicated neural network layers. This segmentation allows the system to handle complex multidimensional data while maintaining manageable processing pathways for each dimension type.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces a learned model (neural network) as an intermediary between the multidimensional structural information input and the residual contamination status prediction output. This intermediary automatically learns and extracts relevant features from the complex multidimensional data, reducing the burden on the overall system architecture while maintaining high prediction accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If various usage and cleaning conditions are considered in prediction, then prediction accuracy is improved, but computational complexity increases

Engineering Contradiction:

Improveprediction accuracyVSAvoidcomputational complexity

Core Design Contradiction:

Measurement precisionVSPower

Solution Approach 1:

The patent performs preliminary action by pre-training the neural network model with comprehensive usage and cleaning condition data during the learning phase. This allows the model to internally encode knowledge about various conditions, enabling it to make accurate predictions during actual use without requiring real-time computational analysis of all possible condition combinations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent handles various usage and cleaning conditions by transforming them into standardized input parameters for the neural network. The model learns to process these parameter variations efficiently, converting diverse conditional inputs into a unified computational framework that reduces overall computational complexity during prediction.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If complex multidimensional structural information is processed, then prediction accuracy is improved, but processing time increases

Engineering Contradiction:

Improveprediction accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces traditional mechanical or manual analysis methods for processing multidimensional structural information with a learned model based on neural networks. This substitution enables parallel processing of multiple information dimensions simultaneously, dramatically reducing processing time while maintaining or improving prediction accuracy compared to sequential analytical methods.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20210357755A1Medical device analysis system, medical device analysis method, and learned model

Publication Date: 2021.11.18 OLYMPUS CORPORATION(JP)

AI summary

A medical device analysis system includes: a medical device; a learning medical device; and a processor comprising hardware. The processor is configured to input multidimensional structural information of the medical device, estimate a residual contamination status after cleaning of the medical device from the input multidimensional structural information of the medical device, based on a learned model that learns about a relationship between the multidimensional structural information of the learning medical device and the residual contamination status after cleaning of the learning medical device, and output the estimated residual contamination status after cleaning of the medical device.