Multimodal Surgical Data Analytics Using Text Feature Conversion
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Solution Overview
Problem
Existing data analytics tools are limited to analyzing alphanumeric or text data and cannot effectively combine and analyze non-text data such as images and audio generated during surgical procedures, which are crucial for determining surgical performance and outcomes.
Innovation Solution
A system that converts multiple modalities of non-text data, including video, image, and audio data, into a set of text features using machine-learning-based segmentation engines, allowing these data to be combined with text data for comprehensive data analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing data analytics tools are used to analyze surgical data, then text data can be processed effectively, but non-text data such as images and audio cannot be analyzed
Solution Approach 1:
The patent introduces an intermediary conversion process that transforms non-text surgical data (images, audio, video) into text format using machine learning models. This intermediary step enables existing text-based analytics tools to process previously incompatible data types, resolving the contradiction between maintaining tool simplicity and expanding data type compatibility.
Solution Approach 2:
The patent changes the parameter format of non-text data by converting images, audio, and video into text representations through machine learning segmentation engines. This parameter transformation allows the data to be processed by text-based analytics tools while preserving the underlying information content, thereby improving adaptability without losing non-text data utility.
2Measurement precision
If multiple modalities of surgical data are combined for comprehensive analysis, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex task of multi-modality data analysis into distinct processing stages: data collection from multiple sources, modality-specific segmentation engines, conversion to text features, and final analytics processing. This segmentation allows each component to handle specific data types independently, improving prediction accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The patent creates a universal processing framework where machine learning-based segmentation engines can handle multiple data modalities (images, audio, video) through a common text conversion interface. This multi-functional approach enables comprehensive data analysis without proportionally increasing system complexity, as the same architectural pattern applies to different data types.
Data Source
AI summary
This patent disclosure provides various embodiments of combining multiple modalities of non-text surgical data in forms of videos, images, and audios in a meaningful manner so that the combined data can be used to perform comprehensive data analytics for a surgical procedure. In some embodiments, the disclosed system can begin by receiving two or more modalities of surgical data during the surgical procedure. The system then time-synchronizes the two or more modalities of surgical data to generate two or more modalities of time-synchronized surgical data. Next, the system converts each modality of the time-synchronized surgical data into a corresponding array of values of a common format. The system then combines the two or more arrays of values to generate a combined set of values. The system subsequently performs comprehensive data analytics on the combined set of values to generate a surgical decision for the surgical procedure.