AI Lymphedema Fluorescence Classification System
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Solution Overview
Problem
Current methods for diagnosing and managing lymphedema lack precision and objectivity, relying on manual inspections and imaging techniques that can be subjective and difficult to interpret, especially in distinguishing between different stages and types of lymphedema-induced fluorescence patterns.
Innovation Solution
A computer-based clinical decision support system (CDSS) that uses artificial intelligence to classify lymphedema-induced fluorescence patterns from images captured after administering a fluorescent agent, incorporating both fluorescence and visible light images, and employing advanced image processing and stitching algorithms to provide accurate staging and typing of lymphedema severity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection and traditional imaging techniques are used for lymphedema diagnosis, then the diagnostic process is simple to perform, but the measurement precision and objectivity are insufficient
Solution Approach 1:
The patent replaces manual inspection and traditional imaging interpretation with an automated AI-based classification system. The system uses machine learning models to analyze fluorescence images and automatically determine lymphedema patterns, substituting human subjective judgment with objective algorithmic analysis, thereby improving diagnostic precision while managing system complexity through software automation.
Solution Approach 2:
The patent introduces an intermediary AI classification system between the fluorescence imaging and final diagnosis. This intermediary layer processes the complex fluorescence patterns and translates them into standardized lymphedema classifications, bridging the gap between raw imaging data and clinical decision-making, thus improving objectivity without requiring clinicians to directly interpret complex images.
2Measurement precision
If fluorescence imaging with AI classification is implemented, then diagnostic objectivity and precision are improved, but the ease of operation decreases due to complex image processing requirements
Solution Approach 1:
The patent implements a self-service automated classification system where the AI model independently performs image analysis and pattern recognition without requiring manual intervention. The system automatically processes fluorescence images, applies classification algorithms, and generates diagnostic results, freeing clinicians from complex image processing tasks while maintaining high classification accuracy.
Solution Approach 2:
The patent performs preliminary image processing and classification computations automatically before presenting results to clinicians. The AI system pre-processes fluorescence images, extracts relevant features, and generates classification outcomes in advance, so that when clinicians review the results, the complex analytical work has already been completed, simplifying their operational task.
3Reliability
If multiple imaging modalities and AI processing are used, then the reliability of lymphedema staging is improved, but the loss of time in processing increases
Solution Approach 1:
The patent implements continuous automated processing where fluorescence image acquisition and AI-based classification occur in an integrated, uninterrupted workflow. The system continuously captures images and immediately processes them through the classification algorithm without manual intervention steps, maintaining continuous useful action from data acquisition to diagnostic output, thereby reducing total processing time while preserving diagnostic reliability.
Solution Approach 2:
The patent performs preliminary feature extraction and model training in advance, so that during actual diagnosis, the AI system can quickly classify new images using pre-computed knowledge. The heavy computational work of learning patterns from training data is done beforehand, allowing rapid inference on new patient images, thus reducing real-time processing time while maintaining high reliability through the use of pre-trained robust models.
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 CDSS enhances diagnostic precision and objectivity by automatically classifying lymphedema severity and types, assisting medical professionals in making accurate diagnoses and tailoring therapies, thereby improving patient outcomes and treatment efficacy.
Implementation Method 1
The dye emits fluorescent light when exited with near infrared light having a wavelength between 600 nm and 800 nm. Due to this excitation, ICG emits fluorescence light between 750 nm and 950 nm.
Data Source
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Figure 3a~4
AI summary
Computer based clinical decision support system (CDSS) 62 and method for determining a classification of a lymphedema induced fluorescence pattern. The fluorescence image 7 is determined from a measurement of a fluorescence signal in a tissue of a body part 4, to which a fluorescent agent 8 has been added. The CDSS 62 comprising: an input interface 64 through which the fluorescence image 7, which is specific to a patient 6, is provided as an input feature to an artificial intelligence (Al) model 68, a processor 66, which performs an inference operation in which the fluorescence image 7 is applied to the Al model 68 to generate the classification of a lymphedema induced fluorescence pattern, and a user interface (UI) 14 through which the classification of the lymphedema induced fluorescence pattern is communicated to a user.