Surgical Task Recognition Using Multi-Stream Ensemble Models
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Solution Overview
Problem
Existing surgical data processing systems face challenges in accurately recognizing surgical tasks due to disparities in sensor availability, limited computational resources, and the need for high recognition accuracy, which can introduce human error and subjectivity, particularly in real-time applications.
Innovation Solution
Implementing a computer-implemented system that utilizes ensemble machine learning models, including convolutional neural networks and recurrent neural networks, to analyze surgical data from various sensors and tools, incorporating unsupervised, supervised, and reinforcement learning methods to enhance task recognition accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated machine learning systems are used for surgical task recognition, then scalability is improved, but recognition accuracy and reliability deteriorate due to disparate sensor availability and limited computational resources
Solution Approach 1:
The system segments surgical task recognition into multiple specialized machine learning models, each trained on specific sensor data types. This allows the system to handle disparate sensor availability by activating only the models relevant to available sensors, maintaining accuracy while enabling scalability across different surgical theaters with varying sensor configurations.
Solution Approach 2:
The system dynamically adjusts processing parameters including computational resource allocation, model complexity, and data sampling rates based on available computational resources. This enables the system to maintain high recognition accuracy on resource-constrained devices while preserving scalability to more powerful systems when available.
2Reliability
If manual recognition by surgical experts is used, then recognition accuracy is improved, but scalability deteriorates due to human error, subjectivity, and impracticality in real-time applications
Solution Approach 1:
The system replaces manual expert recognition with automated machine learning models that objectively analyze surgical data. This substitution eliminates human error and subjectivity while enabling real-time processing and scalable deployment across multiple surgical theaters, preserving accuracy through rigorous model training and validation.
Solution Approach 2:
The system creates standardized digital models of surgical task recognition that can be replicated and deployed across multiple theaters. These models capture expert knowledge in a scalable format that can be consistently applied without human intervention, enabling widespread deployment while maintaining recognition quality.
3Measurement precision
If high computational resources are allocated for real-time processing, then recognition accuracy is improved, but device complexity and resource requirements worsen
Solution Approach 1:
The system implements partial processing by activating only the subset of machine learning models necessary for the available sensors and computational resources. This approach maintains recognition precision for the tasks at hand without requiring the full computational overhead of a complete model suite, thereby reducing device complexity while preserving necessary accuracy.
Data Source
AI summary
Various of the disclosed embodiments are directed to computer-implemented systems and methods for recognizing surgical tasks from surgical data, In some embodiments an ensemble model configured to receive video data, kinematics data, and system event data from the surgical theater may be implemented. The ensemble model may implement modular streams for processing the data, facilitating predictions even when less than all the data types are available. In some embodiments, smoothing operations may help facilitate more accurate prediction results, Various of the embodiments may be employed in real-time during surgery, providing predictions at per-second intervals.