AI Image Data Evaluation for Tumor Ablation Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current minimally invasive interventions, such as tumor ablation, lack a reliable method to assess treatment success in real-time, leading to subjective evaluations and potential over- or under-treatment due to the reliance on visual assessment of image data, which can result in unnecessary risks or disease progression.
Innovation Solution
An AI-driven method that automatically derives property data from image datasets to determine changes in the lesion or intervention area, using algorithms like neural networks or deep learning to provide objective change information for immediate assessment and optimal patient management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visual assessment of image data is used to evaluate treatment success, then the evaluation process is simple and quick, but the assessment is subjective and unreliable
Solution Approach 1:
The patent replaces the manual visual assessment mechanism with an automated AI-based evaluation system. The AI algorithm objectively analyzes image data to determine treatment success, eliminating subjective human judgment while providing reliable, consistent assessments without requiring complex additional hardware
Solution Approach 2:
The patent introduces an AI-based evaluation module as an intermediary between the image data acquisition and the treatment success determination. This intermediary automatically processes the image data and provides objective assessment results, bridging the gap between raw imaging data and clinical decision-making
2Reliability
If follow-up imaging is performed to reliably assess treatment success, then the assessment is accurate, but the time delay prevents timely corrective measures
Solution Approach 1:
The patent performs the treatment success assessment immediately during or directly after the minimally invasive procedure, rather than waiting for follow-up imaging. The AI algorithm evaluates the image data acquired during the procedure to provide timely feedback, enabling immediate corrective actions if needed
Solution Approach 2:
The patent implements real-time feedback by automatically evaluating treatment success during or immediately after the procedure. The AI-based assessment provides immediate feedback to the physician about treatment efficacy, allowing for prompt corrective measures rather than delayed feedback from follow-up imaging
3Productivity
If image data is evaluated by a single physician, then the process is simple and quick, but the result is subjective and variable
Solution Approach 1:
The patent replaces the human physician's visual assessment with an AI-based automated evaluation system. This substitution maintains the speed of evaluation while eliminating subjectivity, as the AI algorithm consistently applies the same evaluation criteria to all cases without human variability
4Productivity
If treatment is performed without reliable real-time assessment, then the procedure can be completed quickly, but over- or under-treatment risks increase
Solution Approach 1:
The patent implements immediate feedback during or after the procedure by using AI to automatically evaluate treatment success based on acquired image data. This feedback loop allows physicians to assess treatment efficacy in real-time and make informed decisions about additional treatment needs, preventing both over- and under-treatment while maintaining procedural efficiency
Solution Approach 2:
The patent performs the treatment success assessment as part of the procedure itself rather than as a separate follow-up step. The AI evaluation is executed immediately after treatment, providing preliminary assessment results that guide subsequent clinical decisions without delaying the overall treatment workflow
Data Source
Figure 1~2
AI summary
Method for evaluating image data of a patient after a minimally invasive procedure on a lesion, in particular a tumor ablation of a tumor, wherein a pre-interventional image data set (1) showing the lesion and an intra- and/or post-interventional second image data set (2) showing the intervention area are evaluated, characterized in that at least partially automatically property data describing properties of the image data are derived from the image data sets (1, 2), which are provided as input data to an evaluation algorithm of artificial intelligence trained with training data of other patients to which a basic truth is assigned, which determines from the property data a change information (12) describing the change of the lesion and/or in the intervention area by the intervention.