Surgical ML Model Orchestration for Accurate Device Recommendations
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Solution Overview
Problem
Existing surgical data processing systems face challenges in accurately identifying trends and providing reliable surgical recommendations due to the wide variety of data types and sources, leading to inefficiencies and inaccuracies in traditional analysis methods.
Innovation Solution
Implementing a surgical computing system that utilizes interrelated machine learning models across facility, edge, and cloud networks to aggregate and process surgical data, determining data exchange behaviors, and employing adaptive learning algorithms to improve artificial intelligence algorithms, allowing for more accurate and efficient surgical device control and recommendation generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing methods are used to analyze surgical data from multiple sources, then the system complexity remains low, but the accuracy and reliability of surgical recommendations deteriorate due to inability to handle diverse data types effectively
Solution Approach 1:
The patent segments the machine learning system into multiple specialized models (e.g., NLP model for text data, computer vision model for image data, structured data model for tabular data). Each model is optimized for specific data types, improving overall accuracy while managing complexity through modular architecture. The surgical computing system divides data processing tasks across these specialized models rather than using a single monolithic system.
Solution Approach 2:
The patent introduces intermediary components including a data preprocessing layer that standardizes diverse surgical data formats, and an orchestration layer that coordinates between multiple ML models. These intermediaries enable accurate processing of heterogeneous data types without requiring direct complex interactions between all system components, thus improving recommendation accuracy while controlling system complexity.
2Reliability
If more comprehensive surgical data is collected from multiple sources to improve analysis accuracy, then the reliability of surgical recommendations improves, but the data processing time and resource consumption increase
Solution Approach 1:
The patent implements preliminary data preprocessing and feature extraction steps that prepare surgical data in advance for ML model processing. Data from multiple sources is standardized, cleaned, and transformed into appropriate formats before being fed into the recommendation system. This preliminary action reduces the actual processing time during surgical procedures while maintaining comprehensive data collection for reliable recommendations.
Solution Approach 2:
The patent employs selective data processing where not all collected surgical data is processed with equal depth. The system identifies and prioritizes the most relevant data features for each specific surgical context, processing only the necessary subset of data at full detail while summarizing or skipping less critical data. This approach maintains recommendation reliability by focusing on key predictive features while reducing overall processing time and resource consumption.
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). 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.