Surgical Data Harmonization for Neural Network Training and Recommendations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Surgical data encompasses a wide range of data types from various sources, making it difficult to process for determining surgical performance, trends, and recommendations, and traditional analysis often lacks accuracy in identifying complex trends.
Innovation Solution
Utilizing a neural network to derive a common data set from related surgical procedures, comparing surgical data between sub-tasks, and providing surgical recommendations through a trained neural network while maintaining healthcare professional privacy and balancing data reduction with system capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analysis methods are used to process surgical data, then the processing is simple and straightforward, but the accuracy in identifying complex trends is insufficient
Solution Approach 1:
The patent replaces traditional mechanical analysis methods with neural network-based machine learning algorithms. The neural network automatically processes surgical data to identify complex trends, substituting manual or rule-based analysis with an intelligent system that can detect patterns without explicit programming, thereby improving accuracy while managing complexity through automated learning.
Solution Approach 2:
The patent transforms surgical data from its raw heterogeneous form into standardized features and parameters that neural networks can process effectively. This involves converting diverse surgical data types into uniform numerical representations, changing the parameter structure to enable accurate trend identification while maintaining processing efficiency.
2Reliability
If diverse surgical data from multiple sources is processed to improve recommendation accuracy, then the quality of surgical recommendations is enhanced, but the data processing complexity increases
Solution Approach 1:
The patent implements a universal neural network architecture that can process multiple types of surgical data from various sources through a single standardized interface. The system handles heterogeneous data including surgical videos, electronic health records, and device data uniformly, enabling high-quality recommendations without requiring separate processing pipelines for each data type, thus managing complexity through consolidation.
Solution Approach 2:
The neural network acts as an intermediary layer between diverse surgical data sources and the recommendation system. It standardizes and integrates data from multiple heterogeneous sources, transforming them into a unified representation that improves recommendation quality while shielding the downstream system from the complexity of handling diverse raw data formats.
3Productivity
If data volume is reduced to optimize system resources, then system efficiency is improved, but information loss may occur
Solution Approach 1:
The neural network extracts only the most relevant and informative features from the complete surgical data set, separating essential information from redundant data. This extraction process reduces data volume for processing while preserving the critical information needed for accurate surgical recommendations, optimizing system efficiency without significant information loss.
Solution Approach 2:
The system transforms raw surgical data into compressed feature representations that capture essential information in a more compact form. By changing the parameter representation from raw data to extracted features, the system reduces data volume for efficient processing while maintaining the informational content necessary for high-quality surgical recommendations.
Data Source
AI summary
A surgical computer-implement surgical system may include a surgical computing system (e.g., a surgical hub), one or more surgical data sources in communication with the surgical computing system, a surgical device in communication with the surgical computing system, and a processor. Data generated by the one or more surgical data sources may be received by the processor. Such data may be used, by the processor, to train a machine learning (ML) model (e.g., a neural network). The ML model may be deployed to affect an operation of the surgical device. For example, the ML model may be deployed to the surgical hub to affect an operation of the surgical device.