Surgical Tissue Classification With Trustworthiness Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing surgery assistance systems, particularly those using machine learning for image interpretation during microsurgical interventions, lack reliability indicators, leading surgeons to rely on uncertain predictions without knowing the trustworthiness of the systems, which can result in suboptimal tissue removal during operations like tumor resection.
Innovation Solution
A method and system that trains a machine learning model to predict both the probability of diseased tissue presence and the trustworthiness of its predictions, generating a control signal for surgery assistance systems, using digital images with annotation data to enhance surgical precision and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning systems are used to classify tumor regions in surgical images, then the recognizability of tumorous tissue is improved, but the reliability and trustworthiness of the classification remains uncertain
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning system not only provides classification results but also generates reliability indicators that feed back to the surgeon. This allows the surgeon to assess the confidence level of each classification and make informed decisions, transforming the black-box ML output into a transparent, verifiable decision-support tool.
Solution Approach 2:
The system performs preliminary actions by pre-training the machine learning model with extensive annotated surgical images before actual surgery. This pre-training phase establishes the baseline classification capability and reliability metrics, enabling the system to provide trustworthy predictions during the actual surgical procedure without requiring real-time adjustments.
2Reliability
If more tissue is removed to ensure complete tumor removal, then the reliability of tumor removal is improved, but healthy tissue damage increases
Solution Approach 1:
The patent applies local quality by providing spatially varying reliability information across different regions of the surgical image. Each pixel or region is associated with a confidence score that indicates the reliability of tumor classification at that specific location, allowing the surgeon to make locally optimized decisions about tissue removal rather than applying uniform margins.
Solution Approach 2:
The system dynamically changes the classification threshold parameter based on the reliability indicators. When confidence is high, the system allows for more aggressive tumor removal; when confidence is low, it becomes more conservative. This adaptive parameter adjustment optimizes the balance between complete tumor removal and healthy tissue preservation.
3Object-affected harmful factors
If less tissue is removed to preserve healthy tissue, then healthy tissue preservation is improved, but the risk of incomplete tumor removal increases
Solution Approach 1:
The reliability indicator serves as continuous feedback to the surgeon, providing real-time confidence assessments that guide the extent of tissue removal. This feedback loop enables the surgeon to confidently remove less tissue when the system expresses high confidence in its classification, knowing that the reduced margin is compensated by the high reliability of the ML classification.
4Measurement precision
If traditional staining methods are used to improve tumor recognizability, then tumor visibility is improved, but surgical time and complexity increase
Solution Approach 1:
The patent substitutes the mechanical/chemical staining process with a computational image analysis system. Instead of physically staining tissue samples during surgery, the machine learning system processes real-time surgical images computationally to highlight tumor regions, eliminating the time-consuming staining steps while maintaining or improving tumor visibility.
Data Source
AI summary
A method for training and using a machine learning system for a differentiation between healthy and diseased tissue during a microsurgical intervention is described. In this case, the method comprises: receiving training data and associated annotation data for training a machine learning system, training the machine learning system, which after training is configured for a prediction of a probability value and a prediction of a trustworthiness value, from which a control signal for a surgery assistance system is derivable, which is usable during a later application during a microsurgical operation, and storing parameter values of the trained machine learning model.