AI Surgical Microscope for Real-Time Tissue Differentiation
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Solution Overview
Problem
Current methods for differentiating between healthy and cancerous tissue during surgical operations are hindered by the need for contrast agents, high computational demands, and lack of real-time support, leading to suboptimal tissue removal and increased operation times.
Innovation Solution
A computer-implemented method using a combined machine learning system to predict digital images in the form of digital fluorescence representation and a further derived representation, allowing for real-time differentiation between healthy and diseased tissue without the need for contrast agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a biopsy is performed to obtain decision basis for treatment, then diagnostic accuracy is improved, but patient risk increases due to medical risks of intervention
Solution Approach 1:
The patent replaces the mechanical/biological biopsy intervention with an optical imaging and AI analysis system. The surgical microscope captures images that are processed by trained AI models to provide diagnostic information, substituting physical tissue sampling with non-invasive optical detection and computational analysis.
Solution Approach 2:
The patent introduces AI-based image analysis as an intermediary between tissue imaging and diagnostic decision-making. Instead of directly relying on biopsy results, the system uses trained machine learning models to analyze microscopic images and provide diagnostic support, acting as a mediator that reduces the need for invasive procedures.
2Measurement precision
If contrast agent is injected several hours before operation to differentiate healthy and diseased tissue, then tissue differentiation is improved, but preparation time and operation time increase
Solution Approach 1:
The patent performs preliminary training of AI models on extensive datasets before the actual surgical operation. The AI system is pre-trained to recognize patterns of diseased tissue, so during surgery it can immediately provide differentiation without requiring contrast agents or extended preparation time. The preliminary computational work is done beforehand, enabling real-time decision support during the procedure.
Solution Approach 2:
The patent replaces the chemical contrast agent injection method with an AI-based image analysis system. Instead of using chemical substances to enhance tissue visibility, the system uses trained machine learning models to analyze standard microscopic images and differentiate between healthy and diseased tissue, eliminating the need for contrast agents and associated preparation time.
3Measurement precision
If switching between lighting presettings is done frequently during operation to optimize tissue visualization, then tissue differentiation is improved, but operation time and complexity increase
Solution Approach 1:
The patent replaces the mechanical/optical approach of switching between multiple lighting presettings with an AI-based analysis system that works with standard illumination. Instead of manipulating physical lighting conditions to enhance tissue contrast, the system uses trained machine learning models to extract diagnostic information from images captured under常规 lighting conditions, simplifying the operational workflow.
Solution Approach 2:
The patent creates a universal AI analysis system that can differentiate tissue types under standard lighting conditions without requiring specialized lighting setups. The trained models are multi-functional, capable of identifying various tissue types and pathologies using a single, consistent imaging protocol, eliminating the need for multiple lighting configurations.
4Measurement precision
If high computational power is used for real-time image analysis during operation, then diagnostic support quality is improved, but computing resource requirements increase
Solution Approach 1:
The patent performs computationally intensive model training and optimization before the surgical procedure. The AI models are pre-trained on large datasets and optimized for efficient inference, so during the actual operation, the system can perform real-time analysis with reduced computational demands. The heavy computational work is done in advance, enabling lightweight processing during surgery.
Data Source
AI summary
A computer-implemented method for predicting digital images in the form of a digital fluorescence representation together with a further derived representation by means of a combined machine learning system is described. The method comprises providing a first digital image of a tissue sample that was recorded under white light by means of a microsurgical optical system with a digital image recording unit, and predicting a second digital image of the tissue sample in a fluorescence representation and a further representation, which has optical indications about diseased tissue elements. This is done by means of a previously trained combined machine learning system comprising a trained combined machine learning model for predicting the second digital image of the tissue sample in the fluorescence representation and the further representation.