AI Colonoscopy Optical Scanning for 3D Polyp Detection
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Solution Overview
Problem
Current colonoscopy systems struggle to accurately detect and treat polyps and adenomas, particularly those under 6 mm in size, due to limited visualization capabilities and operator errors, leading to missed detections and increased cancer risk.
Innovation Solution
An optical scanning system integrated within an endoscope uses miniature near-infrared cameras and VCSEL sources to create three-dimensional point clouds, combined with patterned and solid illumination, enabling precise polyp detection and navigation guidance, and augmented navigation systems for improved detection and treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard endoscope with visible light camera is used, then the system is simple and easy to operate, but polyp detection accuracy is insufficient due to limited visualization capabilities
Solution Approach 1:
The patent combines multiple imaging modalities (visible light camera, near-infrared cameras, VCSEL sources, optical scanning system) into an integrated endoscope system. This merging of different technologies enables comprehensive polyp detection with enhanced accuracy while maintaining a unified device structure that can be operated as a single system.
Solution Approach 2:
The patent transitions from two-dimensional visible light imaging to three-dimensional point cloud mapping by integrating near-infrared optical scanning. This dimensional enhancement provides depth information and spatial context that significantly improves polyp detection accuracy and characterization without overwhelming the operator.
2Measurement precision
If mechanical protrusions (Endocuff, G-EYE, EndoRings) are used to flatten colon folds, then polyp visibility improves, but procedure complexity increases and risk of colon injury rises
Solution Approach 1:
The patent replaces mechanical protrusion systems with an optical-based solution. The integrated optical scanning system and image processing algorithms actively scan and identify polyps within natural colon folds without requiring mechanical flattening, thereby eliminating the risk of colon perforation while maintaining high polyp detection capability.
Solution Approach 2:
The patent introduces an intermediary optical scanning and image processing system that acts as a mediator between the endoscope and the operator. This intermediary actively processes images in real-time, enhancing polyp visibility through multiple imaging modalities without requiring physical manipulation of the colon wall.
3Measurement precision
If multiple images per camera are displayed (Third Eye Panoramic System), then detection coverage increases, but operator workload and processing complexity increase significantly
Solution Approach 1:
The patent merges multiple imaging modalities and multiple camera views into a single integrated three-dimensional point cloud representation. This consolidation provides comprehensive detection coverage while presenting a unified, simplified view to the operator, reducing cognitive workload compared to displaying multiple separate images simultaneously.
Solution Approach 2:
The patent transforms multiple two-dimensional images into a single three-dimensional point cloud model. This dimensional transformation integrates information from multiple cameras and imaging modalities into a cohesive spatial representation that provides comprehensive coverage while simplifying operator interpretation through intuitive 3D visualization.
4Extent of automation
If AI techniques are used to examine 2D video images, then automated detection capability improves, but false positive rate increases and detection accuracy decreases in featureless colon environments
Solution Approach 1:
The patent enhances automated detection by transitioning from two-dimensional video analysis to three-dimensional point cloud analysis. The additional depth and spatial information in 3D data provide contextual cues that reduce false positives and improve detection accuracy in the featureless colon environment, while maintaining high automated detection capability.
Solution Approach 2:
The patent combines multiple imaging modalities (visible light, near-infrared, optical scanning) into an integrated detection system. This merging provides multiple data streams that can be cross-validated by AI algorithms, improving detection accuracy and reducing false positives through multi-modal confirmation while maintaining automated operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances polyp detection accuracy, reduces procedure time, and improves patient access to screenings by providing real-time, three-dimensional mapping and navigation assistance, thereby increasing the effectiveness of colonoscopy procedures.
Implementation Method 1
miniature near-infrared cameras and VCSEL sources
Implementation Method 2
uses external sensors to track a magnetic marker on the endoscope, to help the operator locate the endoscope within the patient's colon with millimeter accuracy
Implementation Method 3
An optical scanning system integrated within an endoscope uses miniature near-infrared cameras and VCSEL sources to create three-dimensional point clouds
Implementation Method 4
combined with patterned and solid illumination
Data Source
AI summary
Described are colonoscopy systems and methods of using such systems. The colonoscopy systems may include an optical scanning system having at least one illuminator configured to produce spatially patterned light and solid light in at least one frame to illuminate tissue within the colon, and at least one camera configured to capture the at least one image of the illuminated tissue within the colon. Additionally, the optical scanning system may include at least one control system configured to construct at least one three dimensional point cloud representations of the tissue within the colon and detect at least one feature of interest using the at least one three dimensional point cloud and a pre-trained artificial intelligence engine.