Dose-Response Curve Image Classification for HTS Artifact Review
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Solution Overview
Problem
The large volume of dose-response data generated in High Throughput Screening (HTS) campaigns requires manual expert review to detect artifacts and correct erroneous data points, which is time-consuming and prone to human errors or inconsistencies.
Innovation Solution
A computer-implemented method using a neural network model, specifically a convolutional neural network, to classify images of dose-response graphs into predefined categories based on curve shape, reducing the impact of data inhomogeneity and enabling automated classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual expert review is used to analyze dose-response data, then detection precision of artifacts and erroneous data points is improved, but productivity is significantly reduced and loss of time increases
Solution Approach 1:
The patent replaces the mechanical manual review process with an automated image processing system using convolutional neural networks. The system converts dose-response graphs into images and applies deep learning models to automatically detect artifacts, outliers, and erroneous data points, eliminating the need for manual expert review while maintaining detection capability.
Solution Approach 2:
The patent creates visual copies of dose-response graphs as images for analysis. By converting data representations into image formats, the system enables automated visual inspection techniques to be applied, allowing the neural network to learn patterns of artifacts and errors from training images without requiring actual manual intervention on the original data.
2Measurement precision
If manual expert review is used to analyze dose-response data, then detection precision of artifacts is improved, but loss of time increases
Solution Approach 1:
The patent replaces the time-consuming manual review process with automated image processing using convolutional neural networks. The system processes dose-response graph images through trained models that rapidly identify artifacts and erroneous data points, reducing analysis time from hours or days of manual review to seconds or minutes of automated processing.
Solution Approach 2:
The patent implements preliminary training of the neural network model on labeled dose-response graph images containing various artifacts and errors. This pre-training phase enables the model to learn detection patterns beforehand, so that during actual analysis, the system can immediately apply learned knowledge without requiring real-time expert judgment, significantly reducing loss of time.
3Productivity
If automated classification is implemented, then productivity is improved and loss of time is reduced, but measurement precision may deteriorate compared to manual review
Solution Approach 1:
The patent performs preliminary training of the convolutional neural network on a large dataset of labeled dose-response graph images, where artifacts and erroneous data points have been manually identified and annotated. This pre-training establishes a foundation of expert knowledge within the model, enabling it to achieve measurement precision comparable to manual review while maintaining high productivity during actual analysis.
Solution Approach 2:
The system implements feedback mechanisms where the automated classification results can be reviewed and corrected, and these corrections are fed back into the training dataset to continuously improve model performance. This iterative feedback loop allows the system to maintain and enhance measurement precision over time while preserving the productivity benefits of automation.
4Reliability
If manual review is used, then consistency of analysis is improved through expert judgment, but productivity is reduced
Solution Approach 1:
The patent replaces manual expert review with an automated neural network system that applies consistent detection criteria uniformly across all dose-response graphs. The model's learned parameters and fixed architecture ensure that the same analysis rules are applied consistently regardless of which graphs are being analyzed, eliminating variability inherent in human judgment while maintaining high productivity through automation.
Solution Approach 2:
The system ensures homogeneity in analysis by processing all dose-response graphs through the same neural network model with identical parameters and thresholds. This uniform processing approach guarantees consistent application of detection criteria across the entire dataset, eliminating the inconsistencies that can arise from different experts or varying human judgment standards.
Data Source
AI summary
A computer-implemented method of classifying images comprising dose-response graphs obtained from dose-response experiments. The method comprises receiving, at a curve shape classifier model, an input comprising image data including a plurality of pixels, wherein the image data represents an image of a dose-response graph indicating a relationship between the concentration of a compound and its activity. The curve shape classifier model comprises a neural network model configured for classifying images of dose-response graphs into a plurality of dose-response graph categories relating to curve shape. The method further comprises generating, using the neural network model, a classification output for the image represented by the received image data, said generating comprising processing the image data using one or more layers of the neural network model in accordance with parameters associated with the one or more layers.