Interpretable Confidence Rankings for Medical Procedure Analysis
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Solution Overview
Problem
Machine learning-based annotation systems in medical procedures lack transparency and confidence metrics, making it difficult for users to trust the accuracy of predictions and identify potential errors in surgical video analysis.
Innovation Solution
A system that uses machine learning to predict model performance and provide interpretable confidence rankings by attributing the confidence level to specific input features, displaying this information through a user interface, including text, colors, symbols, timestamps, and video highlights, allowing users to understand the reliability of predictions without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to annotate medical procedure videos, then automation and productivity are improved, but reliability and measurement precision deteriorate due to lack of confidence metrics and interpretability
Solution Approach 1:
The system implements feedback by providing confidence scores and feature attributions that return information about the model's own predictions to the user. This allows users to understand the reliability of each annotation and make informed decisions about whether to trust or override the automated analysis.
Solution Approach 2:
The patent introduces confidence scores and feature attribution visualizations as intermediary elements between the machine learning model and the user. These intermediaries translate the model's internal decision-making process into interpretable information that enhances user trust without requiring changes to the core automated analysis functionality.
2Ease of operation
If machine learning models provide automated predictions, then ease of operation is improved, but loss of information occurs due to lack of explanation about prediction confidence and reasoning
Solution Approach 1:
The system uses color coding in the feature attribution visualizations to indicate the strength and direction of feature contributions to predictions. Different colors represent different levels of confidence or types of influence, providing an intuitive visual language that conveys complex information about prediction reasoning without requiring text-heavy explanations.
Solution Approach 2:
The patent adds a new dimension of information by overlaying feature attributions and confidence scores on the video timeline. This transforms one-dimensional video data into multi-dimensional information space, where users can simultaneously view the procedure video, confidence levels, and contributing features across the temporal dimension.
3Measurement precision
If detailed confidence analysis is provided for each prediction, then reliability and measurement precision are improved, but device complexity increases due to additional processing and visualization requirements
Solution Approach 1:
The system implements partial action by providing confidence analysis and feature attributions selectively rather than for every single prediction. This allows the system to focus computational resources on predictions where users are most uncertain or where the stakes are highest, rather than uniformly processing all annotations with the same level of detail.
Solution Approach 2:
The patent extracts only the most relevant feature attributions and confidence metrics for display, rather than presenting all available model internals. This selective extraction reduces the complexity of the visualization system while maintaining the essential information needed for user decision-making.
Data Source
AI summary
The solution for an ML-based medical procedure analysis with interpretable model confidence rankings is disclosed. The solution can include a system having one or more processors, coupled with memory. The system can receive a plurality of input features associated with a prediction for a video stream that captures a procedure performed with a robotic medical system. The prediction can be made via a first model trained with machine learning. The system can determine, via a second model trained with machine learning, a level of confidence in the prediction made via the first model. The system can attribute the level of confidence among at least two input features of the plurality of input features. The system can provide, for display via a display device, an indication overlaid on the video stream of the attribution of the level of confidence among the at least two input features.