Surgical Smoke Evacuation via Machine Learning Image Analysis
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Solution Overview
Problem
Conventional surgical smoke evacuation systems require manual adjustment by clinicians to optimize smoke evacuation, leading to inefficiencies in smoke removal during surgical procedures.
Innovation Solution
A surgical smoke evacuation system that includes an imaging device to capture images of the surgical site, a machine learning network to classify the amount of smoke, and a processor to dynamically control the vacuum pressure of the smoke evacuator based on the classified amount, using features such as tissue type, smoke spread rate, and smoke generation locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual control of smoke evacuator speed is used, then clinicians can adjust the system, but time is lost in identifying appropriate speed and when to enable/disable the system
Solution Approach 1:
The smoke evacuator system performs self-adjustment by automatically detecting smoke levels through imaging and machine learning analysis, then autonomously modulating vacuum pressure without requiring clinician intervention for speed selection or system activation
Solution Approach 2:
The system continuously captures images of the surgical site, analyzes smoke presence and amount through machine learning, and uses this feedback to dynamically adjust evacuator speed in real-time, creating a closed-loop control system that eliminates manual adjustment delays
2Productivity
If automatic smoke detection and control is implemented, then smoke evacuation efficiency is improved, but device complexity increases due to imaging device and machine learning network
Solution Approach 1:
The imaging device serves multiple functions: capturing surgical site images for smoke detection, providing visual documentation of the procedure, and enabling machine learning analysis for automatic control decisions, thereby justifying its inclusion despite increased complexity
Solution Approach 2:
The system replaces manual mechanical adjustment of evacuator speed with an automated intelligent control system that uses imaging and machine learning algorithms to dynamically modulate vacuum pressure, improving efficiency despite the added computational complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides automatic and efficient smoke evacuation by dynamically adjusting vacuum pressure, reducing smoke accumulation and improving surgical workflow by minimizing manual intervention.
Implementation Method 1
a suction generator configured to create a vacuum pressure
Data Source
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Figure 2
Figure 3~4
AI summary
A surgical smoke evacuation system includes an imaging device configured to capture an image of a surgical site, a smoke evacuator in communication with the imaging device and including a suction generator configured to create a vacuum pressure, an electrosurgical pencil including a nozzle, a suction conduit coupling the nozzle to the smoke evacuator, a processor, and a memory. The memory includes instructions stored thereon which, when executed by the processor, cause the surgical smoke evacuation system to: identify a feature in the captured image of the surgical site; classify an amount of smoke in the image using a machine learning network based on the identified feature; and dynamically adjust the vacuum pressure generated by the smoke evacuator based on the classified amount of smoke in the image.