Surgical Device Authentication via Performance Signature Analysis
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Solution Overview
Problem
Incorporating non-traditional algorithms, such as machine learning, into medical technologies for authenticating original equipment manufacturer (OEM) devices is challenging due to the time-consuming process of training these models and the inconvenience it poses in surgical settings.
Innovation Solution
A computing system utilizing machine learning algorithms to quickly determine whether a surgical device is authentic or counterfeit by analyzing performance data and comparing it to preconfigured thresholds, reducing the iterations needed for training and improving data processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained to detect counterfeit surgical devices, then detection accuracy is improved, but training time increases
Solution Approach 1:
The system performs preliminary actions by collecting and storing performance data from surgical devices during normal operation. This data is pre-processed and organized into structured formats that can be quickly loaded into the machine learning model during surgery, eliminating the need for time-consuming training during critical procedures.
Solution Approach 2:
The patent creates a virtual copy of the surgical device's performance characteristics through digital twins and simulated data representations. These copies allow the system to train and validate detection algorithms using replicated device behavior patterns without requiring physical training time during actual surgical operations.
2Measurement precision
If comprehensive data collection is performed to train machine learning models, then model accuracy is improved, but processing complexity increases
Solution Approach 1:
The system segments the complex data collection and processing task into distinct modules: device performance monitoring, data validation, feature extraction, and model inference. Each module handles specific aspects of data processing independently, reducing overall system complexity while maintaining comprehensive data collection for accurate modeling.
Solution Approach 2:
The patent introduces an intermediary data processing layer that acts as a mediator between raw device data and the machine learning model. This intermediary layer standardizes, cleans, and transforms complex raw data into simplified feature representations that the model can process efficiently, reducing processing complexity without sacrificing accuracy.
3Reliability
If real-time device authentication is implemented, then patient safety is improved, but computational resources required increase
Solution Approach 1:
The system applies partial action by focusing computational resources on the most critical authentication tasks rather than analyzing all possible device parameters. The machine learning model is designed to perform only the necessary inference operations for counterfeit detection, using a subset of key performance indicators that provide sufficient accuracy without exhausting computational resources.
Solution Approach 2:
The surgical device performs self-service authentication by incorporating onboard sensors and processing capabilities that continuously monitor its own performance characteristics. The device autonomously compares its operational parameters against expected ranges and can trigger alerts or notifications without requiring constant external computational intervention, thereby reducing overall computational resource demands while maintaining patient safety.
Data Source
AI summary
A computing device, such as a surgical hub, may obtain performance data associated with a device, such as a surgical device in an operation room. Based on the performance data, a computing device may identify a performance signature associated with the surgical device. A computing device may determine whether the surgical device is an original equipment manufacturer (OEM) device or a counterfeit device. A computing device may compare the operation data and/or the performance signature to data (e.g., data from a ML trained model) associated with OEM devices. Based on the comparison, a computing device may determine whether the operation data associated with the surgical device is within a normal operation parameter. If a computing device determines that the operation data outside of the normal operation parameter, the computing device may send a message to a health care professional.