Real-Time Surgical Tool Presence/Absence Detection for Unsafe Use
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Solution Overview
Problem
Existing surgical tools, particularly energy tools, lack real-time monitoring for unsafe usage, posing a risk of patient injury during surgeries.
Innovation Solution
A machine-learning-based energy tool presence/absence detection model is developed to process surgical videos in real-time, integrating with control signals to identify unsafe events and prevent injuries by disabling the tool or providing alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring is implemented for energy tool usage, then patient safety is improved, but device complexity increases
Solution Approach 1:
A machine learning-based detection system acts as an intermediary between the energy tool and the surgical environment. The system processes video feeds from surgical cameras to detect tool presence and identify unsafe usage patterns, providing real-time monitoring without requiring modifications to the energy tool itself. This mediator approach enables safety monitoring while maintaining tool functionality.
Solution Approach 2:
The patent replaces traditional mechanical safety mechanisms with a computational approach. Instead of using physical interlocks or mechanical sensors embedded in the tool, the system uses machine learning algorithms processing visual data to detect unsafe usage. This substitution reduces mechanical complexity while achieving real-time safety monitoring.
2Adaptability or versatility
If machine learning models are trained to detect multiple tool models and versions, then adaptability is improved, but training data requirements increase
Solution Approach 1:
The machine learning model is designed with universal detection capabilities that can identify multiple energy tool models and versions using a single unified architecture. The system processes diverse tool appearances, configurations, and variations through one comprehensive model, eliminating the need for separate specialized models for each tool type while maintaining high adaptability.
Data Source
AI summary
In one aspect, a process for improving patient safety during a laparoscopic or robotic surgery involving an energy tool is disclosed. A real-time control signal indicating an operating state of an energy tool during the surgery is being received, along with real-time endoscope video images of the surgery. The process simultaneously applies a machine-learning surgical tool presence/absence detection model to the real-time endoscope video images to generate real-time decisions on a location of the energy tool in the real-time endoscope video images. The process then checks the real-time control signal against the real-time decisions to identify an unsafe event and takes a proper action when an unsafe event is identified. Other aspects are also described and claimed.