Surgical Data Anchor Points for Multi-Modal Context Verification
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Solution Overview
Problem
Intraoperative data streams during surgical procedures are difficult to automatically verify and contextualize, particularly when anatomical structures like vertebrae are not clearly identifiable in video streams due to structural similarity and partial occlusion.
Innovation Solution
A system utilizing a sensor array with multiple cameras and depth sensors to capture multi-modal data, combined with AI applications, automatically recognizes and verifies context across different data streams, enabling accurate surgical characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-modal data streams are integrated to improve surgical documentation accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces anchor points as intermediary elements that link multiple data streams (video, audio, sensor data) to surgical templates. These anchor points serve as mediators that automatically associate context from different modalities without requiring complex manual integration, thereby improving measurement precision while managing device complexity through automated processing.
Solution Approach 2:
The surgical documentation is segmented into discrete templates with specific anchor points. Each template captures a particular surgical event or context, allowing the system to process and verify information in manageable units rather than attempting to integrate all data streams simultaneously, thus reducing the effective complexity of the integration process.
2Reliability
If automated verification systems are implemented to improve documentation reliability, then reliability is improved, but device complexity increases
Solution Approach 1:
The system implements automated verification through feedback mechanisms where anchor points in surgical templates are verified against actual multi-modal data streams. The system continuously checks whether recorded surgical events match the documented templates, providing automated feedback that improves documentation reliability without requiring complex manual verification processes.
Solution Approach 2:
The verification system operates autonomously by automatically comparing surgical template anchor points with incoming data streams. The system self-verifies the accuracy of surgical documentation without requiring external intervention, thereby improving reliability while keeping the verification mechanism relatively simple through automated self-checking.
3Measurement precision
If detailed contextual information is captured to improve surgical precision, then measurement precision is improved, but loss of time increases
Solution Approach 1:
Surgical templates with predefined anchor points are prepared in advance before the surgical procedure. This preliminary structuring allows the system to quickly match incoming multi-modal data to appropriate templates during surgery, capturing detailed contextual information without requiring time-consuming post-processing analysis, thus improving measurement precision while minimizing time loss.
Data Source
AI summary
Methods of automatically generating a characterization of a surgical procedure, and associated systems and devices are disclosed herein. A representative method can include acquiring surgical procedure data of the surgical procedure including at least a first intraoperative data stream and a second intraoperative data stream different than and captured simultaneously with the first intraoperative data stream. The method can further include determining a first context in the first intraoperative data stream at a time in the first intraoperative data stream and, based on the determined first context, determining a corresponding second context in the second intraoperative data stream at and/or proximate the same time in the second intraoperative data stream. The method can further include utilizing an artificial intelligence application to convert at least a portion of the first and second intraoperative data streams and the first and second contexts into a natural language description characterizing the surgical procedure.