Deep Learning Colonoscopy Screening for Accurate Polyp Classification
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Solution Overview
Problem
Current colorectal cancer screening methods, such as unaided video colonoscopy using White Light and Narrow Band Imaging, are limited by errors in polyp detection, including missed or misdiagnosed lesions, due to subjective interpretation and inter-observer variability.
Innovation Solution
A computer vision-aided neural network system that processes colonoscopy images to classify polyps using a deep learning algorithm, incorporating dimensionality reduction and t-SNE embedding, and a multilayer perceptron for robust classification of adenoma, hyperplastic, and serrated polyps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If unaided video colonoscopy is used for polyp detection, then the screening process is simple and quick, but the detection accuracy is low due to subjective interpretation and inter-observer variability
Solution Approach 1:
A deep learning-based image analysis system serves as an intermediary between the colonoscopy imaging system and the endoscopist's diagnosis. The system processes colonoscopy images to detect and classify polyps, providing objective assistance that reduces inter-observer variability and improves detection accuracy without replacing the endoscopist's role
Solution Approach 2:
The patent replaces the subjective human interpretation mechanism with an automated deep learning-based image analysis system. This substitution eliminates inter-observer variability and provides consistent, objective polyp detection and classification while maintaining workflow integration
2Measurement precision
If deep learning-based image analysis is implemented, then polyp classification accuracy is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The deep learning system is divided into specialized components: polyp detection modules, classification modules for different polyp types (adenoma, hyperplastic, serrated), and integration layers. This segmentation allows each component to be optimized for its specific function while working together to achieve high overall accuracy
Solution Approach 2:
The image analysis system performs multiple functions within a unified deep learning framework: detecting polyps, classifying them into different types, and providing diagnostic assistance. This multi-functionality reduces the need for separate systems while maintaining high accuracy across different tasks
3Measurement precision
If comprehensive feature extraction and analysis are performed on polyp images, then diagnostic accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary feature extraction and analysis during the colonoscopy procedure itself, processing images in real-time or near-real-time. This allows diagnostic information to be available immediately without requiring separate post-procedure analysis, reducing overall processing time
Solution Approach 2:
The deep learning system extracts and analyzes only the most relevant features from polyp images for classification and neoplasticity prediction, rather than processing all possible image characteristics. This selective feature extraction maintains high diagnostic accuracy while reducing computational burden and processing time
Data Source
AI summary
Disclosed are various embodiments of systems and methods of deep learning for colorectal polyp screening and providing a prediction of neoplasticity of a polyp. A video of a colonoscopy procedure can be captured. Frames from the video or images associated with the colonoscopy procedure can be extracted. A model for classifying objects that appear in the frames or the images can be obtained. A classification can be determined for a polyp that appears in at least one of the frames or images based on applying the frames or images to an input layer of the model.