Interrelated Machine Learning Models for Surgical Data Processing
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Solution Overview
Problem
The integration of non-traditional algorithms, such as machine learning, into medical technologies poses challenges due to the high stakes and conservative nature of surgical processes, requiring innovative solutions for tailored patient care.
Innovation Solution
The implementation of interrelated machine learning models across various networks, including facility, edge, and cloud networks, to process and combine data for comprehensive surgical analysis, enabling advanced data processing and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple interrelated machine learning models are implemented across facility, edge, and cloud networks to perform comprehensive surgical analysis, then the productivity and measurement precision of surgical data processing are improved, but the device complexity increases
Solution Approach 1:
The patent segments the machine learning system into multiple independent models distributed across three network layers: facility network models handle local surgical data processing, edge network models provide intermediate processing capabilities, and cloud network models perform comprehensive analysis. This segmentation allows each model to operate independently on specific data subsets while contributing to the overall surgical analysis workflow, thereby improving productivity without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces a spatial dimension to the ML system architecture by distributing models across multiple network locations (facility, edge, cloud) rather than concentrating all processing in a single location. This dimensional distribution enables parallel processing of different data aspects simultaneously, enhancing surgical data processing efficiency while managing complexity through hierarchical organization.
2Measurement precision
If machine learning models process sensitive surgical data across multiple networks, then the measurement precision and analytical capability are improved, but the reliability and security of data processing become compromised
Solution Approach 1:
The patent applies local quality by assigning different data processing responsibilities to different network locations based on their security characteristics. Facility network models process highly sensitive surgical data locally with strict HIPAA compliance, edge network models handle semi-sensitive data with moderate security requirements, and cloud network models process anonymized or aggregated data with relaxed security constraints. This localized approach allows each component to optimize for its specific security context while collectively achieving high measurement precision.
Solution Approach 2:
The patent introduces intermediary mechanisms between different network layers to maintain security while enabling data flow. Edge network models serve as intermediaries that aggregate and anonymize data before transmitting to cloud models, and facility models act as intermediaries that selectively share only necessary processed results with external systems. These intermediaries preserve data security and HIPAA compliance while enabling comprehensive surgical analysis across the distributed system.
Data Source
AI summary
Systems, methods, and instrumentalities are disclosed for using interrelated machine learning (ML) models (e.g., algorithms). The interrelated ML models may act collectively to perform complimentary portions of a surgical analysis. The ML models may be used at various locations. For example, ML models may be implemented in a facility network, a cloud network, an edge network, and/or the like. The location of the ML models may influence the type of data the ML models process. For example, ML models used outside a HIPAA boundary (e.g., cloud network) may process non-private and/or non-confidential information. The ML models may be used to feed their respective results into other ML models to provide a more complete result.