AI-Guided Cervicography Excision Tool Selection for Precise Lesion Margins
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cervical cancer treatment methods, such as conization, face challenges in accurately determining the excision area for removing precancerous lesions due to subjective interpretation by medical professionals, leading to unnecessary excision of healthy tissue.
Innovation Solution
An analysis apparatus uses a cervicography image and a learning model to detect lesion areas, set a margin area, and select or design an excision tool based on the shape and size of the excision area, utilizing techniques like deep learning and 3D printing to create customized tools for precise tissue removal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a medical professional visually interprets a cervix image to determine the excision area, then the examination can be performed with standard equipment and procedures, but the determination of the excision area is subjective and may lead to unnecessary excision of healthy tissue
Solution Approach 1:
An analysis apparatus is introduced as an intermediary between the cervix image and the medical professional's decision-making. This apparatus includes a learning model that processes the image data and provides objective excision area determination, serving as a mediator that combines AI analysis with medical expertise to resolve the contradiction between accuracy and system complexity
Solution Approach 2:
The subjective visual interpretation process is partially replaced with an automated learning model that uses machine learning algorithms to analyze cervix images and determine excision areas. This substitution of mechanical/AI processing for human visual interpretation improves measurement precision while managing the complexity through automated decision-support rather than complete automation
2Reliability
If a larger excision area is removed to ensure complete removal of abnormal cells, then the treatment effectiveness is maximized, but more healthy tissue is unnecessarily excised
Solution Approach 1:
The learning model enables different parts of the cervix to be treated differently based on local characteristics. By analyzing the image data and identifying specific lesion boundaries, the system determines precise excision margins that apply local treatment quality to each region - ensuring complete removal of abnormal cells in lesion areas while preserving healthy tissue in normal areas
Solution Approach 2:
The analysis apparatus performs preliminary analysis of the cervix image before the actual excision procedure. By pre-determining the optimal excision area through AI-based image analysis, the system prepares a customized excision plan that balances treatment effectiveness with tissue conservation, allowing the medical professional to proceed with confidence in the predetermined margins
3Adaptability or versatility
If a standardized excision tool is used for all patients, then the device is simple and easy to manufacture, but it cannot accommodate variations in lesion shape and size across different patients
Solution Approach 1:
The system transitions from static, standardized excision tools to dynamic, customizable tools. The analysis apparatus generates patient-specific excision area data that can inform the design and manufacturing of customized excision tools, allowing the system to adapt to varying lesion characteristics while maintaining a streamlined process through automated image analysis and data generation
Solution Approach 2:
The invention changes the parameters of the excision tool based on patient-specific data. By using the learning model to analyze cervix images and determine precise excision areas, the system generates customized parameters (shape, size, margins) for each patient's excision tool, transforming a one-size-fits-all approach into a personalized treatment approach through parameter optimization
Data Source
AI summary
A method of generating excision tool information using a cervicography image includes receiving, by an analysis apparatus, a cervicography image of a patient, inputting, by the analysis apparatus, the cervicography image to a learning model to detect a lesion area, additionally setting, by the analysis apparatus, a margin area based on the lesion area to determine an excision area, and selecting, by the analysis apparatus, one excision tool according to a shape and size of the excision area, or generating a source model for manufacturing an excision tool based on the shape and size of the excision area.