Deep Learning Skin Toxicity Assessment From Tissue Layer Thickness
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Solution Overview
Problem
Traditional machine learning algorithms require time-intensive training on well-defined data features and are not suitable for evaluating subtle changes in tissue samples, leading to subjective and non-scalable assessments of skin toxicity in reconstituted human epidermis samples.
Innovation Solution
Utilizing deep learning and image processing algorithms to measure the thickness of tissue layers, enabling automated, reproducible, and scalable evaluation of skin toxicity by analyzing digital pathology images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning algorithms are used to evaluate tissue samples, then the model requires time-intensive training on well-defined data features, but this leads to subjective and non-scalable assessments of skin toxicity
Solution Approach 1:
The patent replaces traditional machine learning algorithms with deep learning neural networks that can process raw digital pathology images directly. This substitution eliminates the need for time-intensive training on pre-defined features and manual annotation, enabling automated, objective, and scalable skin toxicity assessment while maintaining high reliability through the model's ability to learn complex patterns from the images themselves
2Ease of operation
If manual evaluation methods are used to assess skin toxicity, then the process is simple to implement, but it is subjective and not easily reproducible or scalable
Solution Approach 1:
The deep learning model performs self-service by automatically learning to identify and quantify epidermal thickness changes from digital pathology images without requiring manual intervention. The system trains on annotated images and then autonomously evaluates new samples, providing objective, reproducible, and scalable skin toxicity assessment that maintains ease of operation through automated processing
Solution Approach 2:
The patent creates a digital copy of the manual evaluation process through the deep learning model. The model learns from annotated images (copies of ground truth) and reproduces the assessment process automatically, enabling scalability while maintaining consistency and objectivity that manual methods lack
3Measurement precision
If deep learning models are trained on large datasets with annotated data, then the model achieves high accuracy in predicting skin toxicity, but the data preparation process becomes complex and time-consuming
Solution Approach 1:
The patent performs preliminary action by pre-training the deep learning model on large datasets of annotated digital pathology images before applying it to skin toxicity assessment. This pre-training phase captures general features and patterns from diverse tissue samples, enabling the model to achieve high accuracy in toxicity prediction while the actual application requires only simple image input without complex data preparation
Data Source
AI summary
In one embodiment, a method includes receiving a querying image associated with a tissue sample after a treatment by a drug compound, identifying a target layer of the tissue sample based on a machine-learning model trained to identify layers of tissue samples, calculating a normalized thickness of the identified target layer, and determining a toxicity indication of the treatment by the drug compound based on the normalized thickness of the identified target layer.