Surgery Support System for Real-Time Bleeding Event Detection
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Solution Overview
Problem
During laparoscopic surgery, identifying and managing bleeding events can be challenging due to the bleeding hiding the field of view and the difficulty in determining the exact location of the bleed from endoscopic images, which can lead to delays and increased risk for the patient.
Innovation Solution
A surgery support system that acquires and analyzes medical information in real-time, generating association information to easily confirm abnormal events like bleeding by correlating the information with the point of occurrence, allowing for timely intervention and reducing the need for conversion to laparotomy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If real-time analysis of medical information is performed to detect bleeding events, then identification speed of abnormalities is improved, but system complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary processing by pre-segmenting medical information into multiple types (endoscopic images, vital signs, operation logs, etc.) and establishing detection rules for each type in advance. During surgery, the system only needs to match incoming data against these pre-defined rules, significantly reducing real-time processing complexity while maintaining fast detection capability
Solution Approach 2:
The medical information is divided into multiple segments or types (video images, vital signs, operation logs, etc.), each processed by dedicated detection rules. This segmentation allows parallel processing of different data streams independently, reducing the complexity burden on any single processing component while enabling comprehensive monitoring
2Measurement precision
If multiple types of medical information are acquired and correlated, then measurement precision of abnormal events is improved, but information processing complexity increases
Solution Approach 1:
The system introduces an intermediary processing layer that correlates multiple types of medical information through time synchronization and spatial registration. This intermediary layer integrates endoscopic images, vital signs, and operation logs by matching timestamps and operational contexts, enabling precise abnormal event detection without requiring complex direct integration of all data sources
Solution Approach 2:
The detection rules are designed with universal applicability across different types of medical information. A single detection rule framework can process various data types (images, vitals, logs) by applying appropriate analysis methods specific to each type, reducing the need for separate complex processing systems for each information source
Data Source
AI summary
A surgery support system according to an embodiment includes processing circuitry. The processing circuitry acquires medical information of a subject under surgery. The processing circuitry detects an event relating to an abnormality, based on the acquired medical information of the subject. The processing circuitry associates a point of time of detecting the event relating to the abnormality with the medical information acquired at the point of time to generate association information.