Neural Smoke Estimation for Surgical Evacuator Control
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Solution Overview
Problem
Conventional methods for detecting surgical smoke require additional hardware, leading to increased installation and maintenance costs, and yield inaccurate results due to high-intensity light, with manual smoke removal being inconvenient and imprecise.
Innovation Solution
An electronic device utilizing a trained neural network model to detect and estimate smoke levels, generating a heatmap, and automatically controlling a smoke evacuator for precise and efficient smoke removal without additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional specialized sensors are used for smoke detection, then detection capability is provided, but installation cost and maintenance cost increase
Solution Approach 1:
The patent uses a camera to capture images of the surgical site, creating a visual copy of the environment. This image copy is then processed by a neural network model to detect smoke, replacing the need for specialized physical sensors. The camera captures light reflected from smoke particles, converting optical information into detectable data through computational processing.
Solution Approach 2:
The patent replaces mechanical/specialized sensor-based smoke detection with an optical-imaging-and-computational system. Instead of using dedicated smoke sensors that detect particulate matter directly, the system uses a standard camera combined with neural network processing to identify smoke based on visual characteristics in captured images.
2Measurement precision
If conventional sensors are used for smoke detection, then smoke detection is possible, but accuracy decreases due to high-intensity light in the operation room
Solution Approach 1:
The patent transforms the detection parameter from direct smoke particle detection to visual pattern recognition in images. The neural network model analyzes multiple parameters including color, texture, shape, and spatial distribution of smoke particles in captured images, enabling accurate detection even under high-intensity lighting conditions by identifying characteristic smoke patterns rather than relying on simple optical sensors.
Solution Approach 2:
The patent introduces image processing and neural network analysis as intermediary steps between light interaction with smoke and detection. The camera captures light-scattered images of smoke, the neural network processes these images to extract smoke characteristics, and this multi-stage intermediary process enables accurate smoke detection despite the challenging high-intensity lighting environment in operating rooms.
3Object-generated harmful factors
If manual activation of smoke removal equipment is used, then smoke removal is achieved, but convenience decreases and precision is reduced
Solution Approach 1:
The patent implements a feedback loop where the neural network continuously analyzes images to monitor smoke levels in real-time, and based on this feedback, automatically controls the smoke evacuation system. When smoke is detected above threshold levels, the system activates or increases evacuation; when smoke levels are low, the system reduces or stops evacuation, creating an automated responsive system that improves both convenience and precision.
Solution Approach 2:
The patent enables the smoke removal system to serve itself through automated detection and control. The neural network model autonomously monitors the surgical environment, identifies smoke conditions, and triggers appropriate evacuation responses without requiring manual intervention from surgical staff, making the system self-regulating and improving operational convenience.
4Object-generated harmful factors
If manual activation of smoke removal equipment is used, then smoke removal is possible, but effectiveness and precision decrease
Solution Approach 1:
The patent replaces manual assessment of smoke levels with automated neural network-based image analysis. The system objectively measures smoke characteristics including particle density, distribution patterns, and visual intensity from captured images, providing precise quantitative assessment that eliminates the subjectivity and inaccuracy of manual smoke level evaluation by surgical staff.
Data Source
AI summary
An electronic device for smoke estimation is provided. The electronic device receives a first image of a plurality of images of a physical space. The electronic device detects smoke in the physical space based on an application of a trained neural network model on the received first image. The electronic device generates a heatmap of the physical space based on the detected smoke in the physical space, and further based on an output of the trained neural network model corresponding to the detection of the smoke. The electronic device estimates a level of the smoke in the physical space based on a normalization of the generated heatmap.