AI Diagnostic System Confidence-Based Segmentation
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Solution Overview
Problem
Current AI-based methods for cancer diagnosis using image-based approaches are hindered by the difficulty in identifying molecular biomarkers based on morphological features, leading to insufficient accuracy for clinical diagnostic standards, and are costly due to reliance on next-generation sequencing and immunohistochemistry tests.
Innovation Solution
A computer-implemented diagnostic-assistance system utilizing an artificial intelligence entity with an artificial neural network that classifies images from digitized histopathology slides, generates confidence measures, and provides a diagnostic signal, distinguishing between conclusive and inconclusive results to trigger additional analysis when necessary, thereby optimizing the prediction of molecular biomarkers using standard H&E images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI-based methods use morphological features from digitized histopathology slides to predict molecular biomarkers, then cost is reduced and accessibility is improved, but accuracy is insufficient for clinical diagnostic standards
Solution Approach 1:
The system segments the diagnostic workflow into two distinct pathways: a fast AI-based screening path for conclusive predictions, and a traditional testing path for inconclusive cases. This segmentation allows the system to achieve high accuracy by selectively applying complex traditional methods only when necessary, while maintaining overall system efficiency and accessibility through AI preprocessing.
Solution Approach 2:
The confidence measure module acts as an intermediary between the AI prediction system and the traditional testing methods. It evaluates the reliability of AI predictions and mediates the decision process, determining whether to release AI results directly or trigger additional traditional diagnostic analysis, thereby bridging the accuracy gap between AI and clinical standards.
2Productivity
If the system releases AI classification results directly, then efficiency is improved, but reliability may be compromised for inconclusive cases
Solution Approach 1:
The system dynamically adjusts its operational mode based on prediction confidence levels. For high-confidence predictions, it operates in fast-release mode to maximize efficiency. For low-confidence predictions, it automatically transitions to a more rigorous verification mode, triggering traditional diagnostic methods. This dynamic adaptation allows the system to optimize the balance between efficiency and reliability for each individual case.
Solution Approach 2:
The system changes the decision parameter from a fixed threshold approach to a variable confidence-based approach. By continuously evaluating confidence measures and adjusting the release decision based on these dynamic parameters, the system can maintain high efficiency for clear cases while ensuring reliability for ambiguous cases through selective triggering of additional analysis.
3Reliability
If expensive tests like NGS, IHC, or PCR are used for all cases, then accuracy is ensured, but cost and resource consumption increase significantly
Solution Approach 1:
The system applies traditional expensive testing methods partially, only to the subset of cases where AI predictions are inconclusive. Rather than applying these resource-intensive methods to all cases, the system performs just enough traditional testing to resolve uncertain cases, thereby maintaining diagnostic accuracy while significantly reducing overall resource consumption compared to universal testing approaches.
Data Source
AI summary
A computer-implemented diagnostic-assistance system for medical applications, comprises: an artificial intelligence neural network configured to classify images of an obtained image dataset according to a set of classes; a confidence module configured to generate a confidence measure associated with each of the classified images; a tagging module configured to generate, for the patient, a diagnostic signal based on the generated confidence measures associated with the classified images, wherein the diagnostic signal for the patient is tagged as conclusive if a processed combination of the confidence measures fulfills a condition and tagged as inconclusive if the processed combination of the confidence measures does not fulfill the condition; and an output interface configured to output the diagnostic signal, wherein if the diagnostic signal is conclusive the classification result is released, and if the diagnostic signal is inconclusive an additional diagnostic analysis is triggered.