Multimodal Clinical Event Recognition on a Common Timeline
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Solution Overview
Problem
Existing methods for identifying events during clinical procedures, such as surgical procedures, are time-consuming and prone to inaccuracies, whether through manual video analysis or limited automated processing, and fail to capture a comprehensive range of events.
Innovation Solution
A data processing apparatus that integrates multiple data modalities (e.g., video, audio, imaging) with a common timeline, using machine learning models to generate sub-event labels and derive higher-level event classifications, enhancing accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual video analysis is used to identify events during clinical procedures, then event identification can be performed, but it consumes a significant amount of clinician time
Solution Approach 1:
The patent replaces manual mechanical video analysis with automated machine learning models that process video data, audio data, and imaging data to identify events. This substitution eliminates the need for clinicians to manually review videos while maintaining accurate event detection through multi-modal data fusion and sophisticated algorithms.
Solution Approach 2:
The system enables self-service event identification by automatically processing clinical procedure data without requiring clinician intervention. The machine learning models independently analyze video, audio, and imaging data to detect and label events, freeing clinicians to focus on patient care rather than administrative tasks.
2Productivity
If automated video processing is used to identify events, then clinician time is saved, but the type of events that may be identified is quite limited and there is potential for inaccuracy
Solution Approach 1:
The patent merges multiple data modalities including video data, audio data, and imaging data into a unified analysis framework. By combining these diverse data sources, the system expands the range of detectable events beyond what is possible with video alone and improves accuracy through cross-modal verification and complementary information.
Solution Approach 2:
The machine learning system is designed with multi-functionality to handle various types of events across different clinical procedures. The unified model can identify diverse event types including procedural milestones, anomalies, and contextual events by processing multiple data modalities, making it adaptable to different clinical scenarios.
3Extent of automation
If automated video processing is used to identify events, then event identification can be performed automatically, but the type of events that may be identified is quite limited
Solution Approach 1:
The patent combines video data, audio data, and imaging data to create a comprehensive event detection system. This merging of modalities enables the automated system to identify a broader range of event types including those that may not be visible in video alone, such as audio-based events or imaging-detected anomalies, thereby improving versatility.
Solution Approach 2:
The system adds new dimensions to event detection by incorporating audio and imaging data alongside video. This multi-dimensional approach allows the automated system to detect events that exist in different data dimensions, expanding the scope of identifiable events beyond the limitations of single-modal video analysis.
Data Source
AI summary
A data processing apparatus comprising processing circuitry configured to: receive data collected during a clinical procedure, the data belonging to a plurality of data modalities, at least one of the data modalities being an imaging data type and a further of the data modalities being an additional data type other than imaging data, wherein the data is provided with reference to a common timeline over which the data is collected; for each of the plurality of data modalities, process data of the respective data modality to generate one or more labels, each identifying an event occurring at a time on the common timeline and indicated by the processed data; and process the labels for each of the identified events based on the times of occurrence of the events to obtain an output indicative of a further medical event.