Multispectral Medical Imaging for ML-Guided Procedure Analysis
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Solution Overview
Problem
Existing medical imaging technologies struggle to effectively integrate visible-spectrum and non-visible-spectrum image data for enhanced medical procedure analysis and control, limiting the capabilities of computer-assisted medical systems.
Innovation Solution
A system and method that utilizes both visible-spectrum and non-visible-spectrum image sequences captured during a medical procedure to train a machine learning model, enabling the generation of predictions and operations that enhance medical procedure analysis and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only visible-spectrum image data is used for medical procedure analysis, then the system is simpler to operate, but the analysis precision and diagnostic capability are limited
Solution Approach 1:
The patent segments the imaging system into multiple independent spectrum channels (visible, infrared, ultraviolet) that can be processed separately. Each spectrum type is captured by dedicated sensors and processed through separate machine learning models, allowing the complex multi-spectrum analysis to be divided into manageable segments that maintain high precision without overwhelming operational complexity
Solution Approach 2:
The patent introduces machine learning models as intermediary components that automatically fuse and interpret multi-spectrum image data. These ML models serve as intermediaries between the raw multi-spectrum inputs and the final diagnostic outputs, handling the complexity of integrating multiple spectrum types while presenting simplified, high-precision results to users
2Loss of information
If multiple spectrum image sequences are processed together, then the information completeness improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary processing of each spectrum type independently before fusion, including preprocessing, feature extraction, and preliminary analysis. By preparing each spectrum channel in advance and separately, the system reduces the computational burden during final integration, maintaining information completeness while reducing overall processing time
Solution Approach 2:
The patent implements selective processing where not all spectrum types are processed with the same level of detail in all scenarios. The system can adjust the depth of processing for each spectrum type based on procedural needs, applying full analysis only to critical regions or time periods, thus reducing unnecessary processing time while maintaining essential information completeness
3Measurement precision
If advanced machine learning models are used to integrate multi-spectrum data, then the diagnostic accuracy improves, but the device complexity increases
Solution Approach 1:
The patent employs universal machine learning model architectures that can handle multiple spectrum types through a common processing framework. The same core model structure processes visible, infrared, and ultraviolet data, reducing the need for separate complex models for each spectrum type while maintaining high diagnostic accuracy across all modalities
Solution Approach 2:
The patent uses data augmentation and synthetic data generation to create training samples for rare or critical diagnostic cases. By copying and transforming existing multi-spectrum datasets, the system trains more accurate models without requiring additional physical imaging equipment or increasing hardware complexity
Data Source
AI summary
An illustrative system may access a first image sequence captured by an imaging device during a medical procedure, the first image sequence comprising first images. the first images based on illumination of a scene associated with the medical procedure using visible-spectrum light: access a second image sequence captured by the imaging device during the medical procedure. the second image sequence comprising second images. the second images based on illumination of the scene using non-visible spectrum light: and provide the first image sequence and the second image sequence to a machine learning module.