AI Semantic Segmentation for Medical Scope Channel Anomaly Detection
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Solution Overview
Problem
Current methods for inspecting medical scopes, such as endoscopes, are prone to human error and inefficiency due to the need for manual inspection using borescopes that produce low-resolution images, making it difficult to detect fine surface anomalies and requiring extensive labor and time, especially in high-volume medical facilities.
Innovation Solution
An AI image recognition system utilizing a deep learning tool with a convolutional neural network for semantic segmentation is employed to rapidly analyze images from a digital borescope, enabling real-time detection of surface anomalies along the channels of medical scopes with high accuracy and speed, processing images at a rate of about 0.03 seconds per frame without sacrificing sensitivity or reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection using borescopes is used, then inspection can be performed, but detection precision is low due to low-resolution images and human error
Solution Approach 1:
The patent replaces the mechanical manual inspection system with an automated AI-based image recognition system. The convolutional neural network automatically analyzes borescope images to detect surface anomalies, eliminating human error and improving both detection precision and reliability through consistent automated classification of defects.
Solution Approach 2:
The patent creates a digital copy of the inspection process by training the AI system on a large dataset of annotated images containing various surface anomalies. This digital model learns to replicate expert inspection capabilities, enabling consistent and reliable detection without human involvement.
2Productivity
If manual inspection methods are used, then inspection can be performed, but productivity is low due to extensive labor and time requirements
Solution Approach 1:
The patent replaces time-consuming manual inspection with automated AI analysis that processes images in real-time. The system can analyze multiple images simultaneously, dramatically increasing inspection throughput and reducing the time required per scope while maintaining high detection accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-training the convolutional neural network on extensive datasets of annotated images before deployment. This pre-training enables the system to rapidly process new inspection images without requiring time-consuming manual analysis, achieving both high speed and accuracy.
3Measurement precision
If high-resolution imaging is used to detect fine surface anomalies, then detection precision improves, but device complexity increases
Solution Approach 1:
Instead of increasing the physical resolution of imaging hardware, the patent substitutes computational analysis power. The AI system enhances the effective detection capability by intelligently analyzing lower-resolution images, avoiding the complexity of high-resolution imaging systems while achieving superior anomaly detection.
Solution Approach 2:
The patent changes the parameter of analysis from physical image resolution to computational feature extraction. By transforming the inspection approach from hardware-dependent resolution to software-based pattern recognition, the system achieves high detection precision without increasing device complexity.
Data Source
AI summary
An artificial intelligence (AI) image recognition system can detect and recognize surface anomalies along a channel of a medical scope. The system includes image processing deep learning modules configured to process image data from an image sensor of a digital borescope. The image processing modules include a convolutional neural network trained on a data set of images of surface anomalies present along surfaces of channels of medical scopes, and the convolutional neural network is configured for semantic segmentation. The presence and instances of the surfaces of the anomalies can be predicted in real time as the digital borescope is pushed through the channel of the medical scope. Indicia of the type and/or instances of occurrence of the surface anomalies along the channel can be displayed in a time neutral manner with respect to the digital video output by a borescope.