Machine Learning Algorithm for Surgical Tissue Classification
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Solution Overview
Problem
Surgeons face challenges in determining which tissue sections to resect or not during surgeries, especially in neurosurgical oncology, where preserving vital brain tissue is crucial while removing harmful tissue.
Innovation Solution
A system and method for training a machine-learning algorithm using images from a surgical microscope, which adjusts the algorithm to generate instruction data indicating actions to be performed on tissue, based on training data including images, patient data, and annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a surgeon manually determines which tissue sections to resect based on visual inspection, then the surgeon has full control over the decision-making process, but the precision and accuracy of identifying harmful versus vital tissue is limited by human capability
Solution Approach 1:
The patent introduces a machine learning algorithm as an intermediary between the surgeon and the tissue. The algorithm processes microscope images and provides automated classification of tissue as harmful or vital, serving as a decision-support tool that enhances surgical precision without completely replacing surgeon control
Solution Approach 2:
The patent replaces the manual visual inspection process with an automated machine learning-based image analysis system. The mechanical/manual process of tissue identification is substituted with an automated computational system that analyzes microscope images and generates classification results
2Reliability
If the surgeon relies on manual visual inspection to identify harmful tissue, then the surgical process remains simple and quick, but the reliability of correctly identifying tissue that must be preserved versus removed is reduced
Solution Approach 1:
The patent performs preliminary training of the machine learning algorithm using annotated microscope images before actual surgical use. This preliminary action ensures the algorithm is properly calibrated and reliable when deployed in the surgical setting, allowing for accurate tissue classification during the actual procedure
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning algorithm's classifications are validated against ground truth annotations from pathological examinations. This feedback loop enables continuous improvement and verification of the algorithm's reliability in distinguishing harmful from vital tissue
3Productivity
If automated machine learning algorithms are used to generate instruction data for tissue resection, then surgical precision and decision-making support are improved, but the complexity of the surgical system increases
Solution Approach 1:
The patent creates a multi-functional system where the machine learning algorithm serves multiple purposes: classifying tissue as harmful or vital, generating instruction data for resection boundaries, and providing decision-support to surgeons. This universal approach consolidates multiple functions into a single integrated system
Solution Approach 2:
The system creates a digital copy or representation of the tissue structure through image processing and machine learning analysis. This digital model serves as a virtual representation that can be analyzed and classified without physically manipulating the actual tissue, enabling automated decision-support
Data Source
AI summary
A system for training of a machine-learning algorithm includes one or more processors and one or more storage devices. The system is configured to receive training data. The training data includes images showing a tissue. The system is further configured to adjust the machine-learning algorithm to obtain a trained machine-learning algorithm based on the training data, such that the trained machine-learning algorithm generates instruction data for at least a part of the tissue shown in the images. The instruction data is indicative of an action to be performed on the tissue. The system is further configured to provide the trained machine-learning algorithm.