Medical Camera System for Real-Time Thermal Spread Detection
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Solution Overview
Problem
Current medical procedures face challenges in accurately and efficiently identifying and treating delicate anatomical structures like the posterior nasal nerve during ENT procedures, leading to potential over-treatment and unnecessary lesions due to lack of precision and real-time support.
Innovation Solution
A medical camera system equipped with a camera and controller that captures images using multiple light modalities, employs AI for structure detection, estimates thermal spread, and provides real-time feedback through alerts or automatic device deactivation to prevent tissue damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional surgical methods are used to treat the posterior nasal nerve, then treatment can be performed, but precision is insufficient leading to over-treatment and unnecessary lesions
Solution Approach 1:
The system performs preliminary detection and identification of the posterior nasal nerve and surrounding risk structures before treatment begins. The AI-based image analysis pre-maps the treatment area, identifies the target nerve, and detects vulnerable structures in advance, enabling the medical professional to plan the treatment path and avoid over-treatment before actually performing the procedure.
Solution Approach 2:
The system provides real-time feedback during the treatment procedure through continuous image capture and analysis. The AI algorithm continuously monitors the treatment area, tracks the treatment device position, and alerts the medical professional when approaching risk structures or when treatment parameters need adjustment, preventing unnecessary lesions through immediate feedback loops.
2Measurement precision
If multiple imaging tests such as CT scans or MRI are performed to detect structural abnormalities, then diagnostic accuracy is improved, but procedure complexity and time increase
Solution Approach 1:
The medical camera system integrates multiple functions into a single device: it captures images during endoscopy, performs AI-based analysis to detect structural abnormalities, identifies the posterior nasal nerve, tracks treatment devices, and provides real-time guidance. This multi-functional integration replaces the need for separate CT scans and MRI examinations, reducing procedure complexity while maintaining diagnostic accuracy.
Solution Approach 2:
The system replaces complex mechanical imaging systems (CT scanners, MRI machines) with an AI-based image analysis approach that processes standard endoscopic images. Instead of requiring additional heavy imaging equipment, the AI algorithm extracts detailed anatomical information from routine endoscopic views, substituting mechanical imaging complexity with computational intelligence.
3Measurement precision
If real-time image processing and AI analysis are implemented, then treatment precision is improved, but computational energy consumption increases
Solution Approach 1:
The AI system performs partial analysis on each captured image frame, focusing only on relevant features such as the posterior nasal nerve identification, risk structure detection, and treatment device tracking. Rather than analyzing every pixel in full detail for each frame, the system applies selective AI processing to maintain treatment precision while reducing overall computational energy consumption through targeted, partial analysis of critical areas.
Data Source
AI summary
A medical camera system for capturing images and processing the captured images to determine specific structures or conditions within a subject is provided. The medical camera system including: a camera configured to acquire images; and a controller operatively coupled to the camera to receive the acquired images and process output images. Wherein the controller: detects a risk structure within the acquired images; detects a presence of a treatment device and/or guiding laser beam within the acquired images; estimates a lateral thermal spread generated during activation of the treatment device in real-time based on settings and/or operational status of the treatment device; evaluates a distance or position of a predefined isothermal line relative to the detected risk structure; and causes a predetermined response when the distance between the detected risk structure and/or the isothermal line falls below a predetermined threshold.