Neural Network Surgical Data Throttle
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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 also filtering data to maintain healthcare professional privacy and balancing data reduction with system resources.
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 straightforward, but the accuracy of determining surgical performance, trends, and recommendations is insufficient
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 managing system complexity through automated learning processes.
Solution Approach 2:
The patent transforms surgical data into multiple derived parameters including surgical performance scores, trend indicators, and recommendation metrics through machine learning processing. By changing the parameters from raw data to processed insights, the system achieves higher accuracy in determining surgical performance while the complexity is managed through systematic parameter transformation rather than manual analysis.
2Measurement precision
If comprehensive surgical data from diverse sources is collected to improve analysis accuracy, then the accuracy of surgical recommendations improves, but the difficulty of processing and managing the data increases
Solution Approach 1:
The patent extracts only the most relevant features and parameters from comprehensive surgical data using machine learning feature selection techniques. By taking out and focusing on critical data elements rather than processing all raw data, the system maintains high recommendation accuracy while reducing processing difficulty through selective extraction of meaningful information.
Solution Approach 2:
The patent introduces machine learning models as intermediary layers between raw surgical data and final recommendations. These intermediaries automatically process, normalize, and transform diverse data sources into unified analytical outputs, thereby improving recommendation accuracy while shielding the system from the complexity of direct data management through automated intermediary processing.
3Object-affected harmful factors
If data is reduced to maintain healthcare professional privacy, then privacy protection is improved, but the amount of available data for analysis decreases
Solution Approach 1:
The patent changes the parameters of data representation by transforming identifiable information into anonymized or aggregated forms while preserving statistical properties useful for analysis. Machine learning models process data in ways that maintain analytical value while altering parameters such as personal identifiers, thereby protecting privacy without substantially reducing the quantity of analyzable data through intelligent parameter transformation.
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.