Ensemble Surgical Task Recognition With Missing Data Streams
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Solution Overview
Problem
Existing surgical data processing systems face challenges in accurately recognizing surgical tasks due to human error, scalability issues, and inconsistent data availability, which can lead to biased machine learning models and potential negative patient outcomes.
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 for accurate task recognition, incorporating uncertainty calculations and task transition probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts manually recognize surgical tasks, then recognition accuracy may be high, but human error and subjectivity are introduced and scalability is limited
Solution Approach 1:
The patent replaces manual human expert analysis with automated machine learning models that process surgical data. Multiple ML models (including computer vision models and natural language processing models) automatically recognize surgical tasks from video feeds, sensor data, and electronic health records, eliminating human error and subjectivity while enabling scalable deployment across multiple surgical theaters.
Solution Approach 2:
The system creates digital copies of surgical data from various sources (video feeds, sensor readings, EHR entries) and processes these copies through machine learning models. This allows automated analysis without requiring human experts to directly examine original surgical data, improving both accuracy and scalability.
2Productivity
If automated solutions are implemented, then scalability improves, but data consistency and availability challenges arise
Solution Approach 1:
The patent implements a universal data processing framework that handles multiple data types (video, sensor data, text) from various surgical systems through standardized pipelines. The system is designed to work across different surgical theaters and robotic systems, maintaining data consistency through uniform processing protocols while enabling scalable deployment.
Solution Approach 2:
The system introduces intermediary processing layers that standardize and validate data from diverse surgical sources before analysis. Data normalization layers and validation mechanisms ensure consistent data quality across different theaters and systems, mediating between heterogeneous data sources and the machine learning models.
3Measurement precision
If multiple data sources are integrated, then recognition comprehensiveness improves, but system complexity increases
Solution Approach 1:
The patent divides the complex recognition system into specialized modules: computer vision models for video analysis, natural language processing models for text data, sensor data processing modules, and a coordination layer. Each module handles specific data types independently, reducing overall system complexity while maintaining comprehensive recognition capabilities.
Solution Approach 2:
The system merges results from multiple specialized models through an ensemble approach, combining predictions from computer vision, NLP, and sensor analysis. This integration provides comprehensive task recognition while managing complexity through modular architecture and coordinated processing.
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.