Two-Tier Tissue Specimen Analysis for Intraoperative Margin Detection
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Solution Overview
Problem
Current surgical oncology practices face challenges in determining whether cancerous lesions have been entirely removed during a surgical procedure due to labor-intensive and time-consuming microscopic pathologic evaluation of excised tissue specimens, which delays the availability of margin results, potentially requiring additional surgeries and treatments.
Innovation Solution
A two-tiered prediction model system is employed for intraoperative tissue specimen analysis, utilizing a first computationally efficient model to reduce the image dataset and minimize false negatives, followed by a second, more computationally intensive model to enhance accuracy in identifying false positives, thereby optimizing the analysis of tissue specimen margins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single high-accuracy detection model is used to analyze all images, then false positive identification is minimized, but computational time and resource requirements become prohibitively high
Solution Approach 1:
The image dataset is segmented into two groups: a first group of images likely to contain artifacts and a second group of images unlikely to contain artifacts. This segmentation allows different detection models with appropriate computational complexity to be applied to each group, reducing overall computational time while maintaining high accuracy for critical cases.
Solution Approach 2:
Different detection models are applied to different image groups based on their local characteristics. A first detection model with higher computational power is applied to the first group of images where artifacts are suspected, while a second, less computationally intensive model is applied to the second group. This local differentiation optimizes the balance between accuracy and computational efficiency.
2Productivity
If a computationally efficient detection model is used to reduce the image dataset, then processing time is reduced, but false negative identification increases
Solution Approach 1:
A first detection model is applied preliminarily to the entire image dataset to identify and separate images likely to contain artifacts. This preliminary action creates a focused subset of images that require more intensive analysis, ensuring that computationally efficient filtering does not miss potential artifacts while reducing the workload for subsequent high-accuracy analysis.
Solution Approach 2:
The first detection model acts as an intermediary that bridges the gap between computationally efficient processing and high-accuracy detection. It processes all images quickly to create a curated subset, which then serves as input for the second detection model. This intermediary step ensures that no potential artifacts are missed in the initial filtering while enabling efficient processing of the reduced dataset.
3Measurement precision
If all images are analyzed with high computational power, then false positives are minimized, but the complexity of the system increases
Solution Approach 1:
The detection system is segmented into two distinct detection models with different computational characteristics. This segmentation allows the system to manage complexity by assigning appropriate computational resources to different image groups, avoiding the need for a single overly complex model to process all images uniformly.
Solution Approach 2:
High computational power is applied partially only to the first group of images where artifacts are suspected, rather than excessively applying it to all images. This partial application of computational resources reduces overall system complexity while maintaining high accuracy where it is most needed.
Data Source
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AI summary
Systems and methods for tissue specimen analysis. Methods for tissue specimen analysis may include: retrieving a primary image data set including a plurality of images representing a tissue specimen margin; generating a reduced data set representing images having suspected artifacts based on a first detection model and the primary image set, the first detection model trained based on pathology-confirmed images and for prioritizing reducing false negative identification of artifacts while minimizing training penalization for false positive identification of artifacts; generating a prediction data set representing a subset of the reduced data set based on a second detection model and the reduced data set; and generating a signal representing the prediction data set for displaying one or more images predicting a true positive identification of a suspected artifact.