Surgical Data Timing Schemas for Cross-Device Time Alignment
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Solution Overview
Problem
Existing surgical data collection methods lack an optimal observation schema, leading to inefficient data collection and misalignment of time domains, which hinders the effectiveness of machine learning models in analyzing surgical data.
Innovation Solution
A surgical computing system employs a machine learning model to adjust the temporal characteristics of data collection across diverse surgical devices, aligning data collection times and providing a common time domain for improved data utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If default observation schema is used for data collection, then data collection is simple, but data collection efficiency is low and time domains are misaligned
Solution Approach 1:
The system dynamically changes the observation schema parameters (sampling frequency, data collection timing) based on the surgical procedure phase and machine learning model requirements. The surgical computing system receives information about the surgical procedure and generates an observation schema that adjusts data collection parameters to align time domains across multiple devices, improving data collection efficiency without requiring complex manual configuration.
2Productivity
If manual observation schema configuration is implemented, then data collection timing can be optimized, but time consumption for configuration increases
Solution Approach 1:
The system automatically generates and configures the observation schema without requiring manual intervention. The surgical computing system receives information about the surgical procedure and autonomously determines the optimal observation schema, including timing and frequency parameters, based on the specific surgical context and machine learning model requirements, eliminating configuration time while maintaining optimization.
3Reliability
If diverse surgical devices collect data independently, then device autonomy is maintained, but time domain alignment is lost
Solution Approach 1:
The surgical computing system acts as an intermediary that coordinates data collection timing across multiple diverse surgical devices. It receives information about the surgical procedure and generates a unified observation schema that synchronizes the time domains of all connected devices, ensuring proper alignment without requiring complex peer-to-peer coordination between devices themselves.
Data Source
AI summary
A surgical computing system may provide first information about a surgical procedure to a recommendation model. The first information may include the identity of an observation point of a surgical device to be used during the surgical procedure. The surgical device may collect data descriptive of an object of the observation point. The surgical computing system may receive second information from the recommendation model. The second information may be indicative of a recommended schema for the observation point. The recommended schema may define a timing via which the surgical device is to collect data. The surgical computing device may send, to the surgical device, an instruction to collect, during the surgical procedure, data descriptive of an object of the observation point according to the timing defined by the recommended schema.