Neural Network Surgical Data Harmonization
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Solution Overview
Problem
The challenge in surgical data processing lies in the complexity and variability of surgical data from diverse sources, making it difficult to accurately determine surgical performance, trends, and recommendations using traditional analysis methods.
Innovation Solution
A neural network-based approach is employed to derive a common data set from different surgical procedures, comparing related sub-tasks to provide surgical recommendations while maintaining healthcare professional privacy and balancing data reduction with physical system capacities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analysis methods are used to process surgical data, then the processing approach is simple and familiar, but the accuracy of determining surgical performance, trends, and recommendations deteriorates due to data complexity and variability
Solution Approach 1:
The patent replaces traditional mechanical analysis methods with machine learning algorithms and neural networks. Specifically, machine learning models are trained on historical surgical data to automatically identify complex patterns, trends, and performance metrics that traditional analysis cannot detect, thereby improving measurement precision while the system learns to manage data complexity
Solution Approach 2:
The patent transforms surgical data by changing its representation parameters through feature extraction and selection. Machine learning algorithms convert raw surgical data into meaningful features and metrics, changing the parameter space from raw data points to interpretable surgical performance indicators, which improves accuracy without proportionally increasing system complexity
2Loss of information
If all surgical data is processed to maintain completeness, then data comprehensiveness is improved, but processing time and computational resources deteriorate
Solution Approach 1:
The patent extracts only the most relevant features and data elements needed for surgical analysis using machine learning-based feature selection. Instead of processing all surgical data, the system identifies and extracts key features that contribute most to surgical performance determination, thereby maintaining information completeness while significantly reducing processing time
Solution Approach 2:
The patent applies partial action by processing a subset of surgical data that is sufficient for accurate analysis. Machine learning models are trained to determine the optimal amount of data needed for each surgical task, processing only that necessary portion rather than all available data, thus balancing completeness with efficiency
3Quantity of substance
If data from multiple surgical procedures is aggregated to improve analysis, then the amount of available data is improved, but data variability and difficulty in identifying trends worsen
Solution Approach 1:
The patent creates a universal data framework that can handle multiple surgical procedure types through machine learning. The system learns common patterns across different surgical specialties while accommodating procedure-specific variations, enabling the aggregation of diverse surgical data without proportionally increasing harmonization complexity
Solution Approach 2:
The patent standardizes surgical data by changing its parameters through normalization and feature engineering. Machine learning algorithms transform diverse surgical data from different procedures into a common parameter space, making the data comparable and analyzable while reducing the complexity associated with data variability
4Measurement precision
If neural networks are used to derive common data sets from different surgical procedures, then the accuracy of surgical recommendations is improved, but the complexity of the processing system increases
Solution Approach 1:
The patent performs preliminary actions by pre-training neural networks on large volumes of historical surgical data before actual surgical analysis. The machine learning models are trained in advance to learn surgical patterns, relationships, and best practices, so that during actual use, the system can provide accurate recommendations without requiring complex real-time processing
Solution Approach 2:
The patent introduces machine learning algorithms as intermediaries between raw surgical data and surgical recommendations. These algorithms act as mediators that automatically process, interpret, and translate complex surgical data into actionable insights, reducing the apparent system complexity while maintaining high accuracy
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.