Deep Learning Ovarian Toxicity Assessment via Corpora Lutea Counting
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Solution Overview
Problem
Current methods for assessing ovarian toxicity in preclinical studies are laborious, time-consuming, and prone to errors due to the need for manual annotation of serial sections by pathologists, and they fail to accurately quantify corpora lutea (CL) counts, which are crucial for evaluating the impact of compounds on ovarian function.
Innovation Solution
A deep learning neural network using a one-stage detector with focal loss as a loss function is employed to identify and count CL in ovarian tissue slices, allowing for automated analysis of H&E stained sections and inferring ovarian toxicity based on the CL count.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of serial sections by pathologists is used, then accurate qualitative and quantitative evaluation of follicles can be achieved, but the process becomes laborious and time-consuming
Solution Approach 1:
The patent creates digital copies of histological images and uses deep learning models to analyze these digital representations, replacing manual pathologist review while maintaining evaluation accuracy. The system processes digital images of ovarian sections through trained neural networks that replicate and extend human expert capabilities.
Solution Approach 2:
The patent replaces the mechanical process of manual annotation with an automated computational system. Deep learning models, specifically convolutional neural networks, automatically detect and count corpora lutea in histological images, substituting the manual mechanical process of pathologist review with an automated digital system that reduces time loss while maintaining precision.
2Measurement precision
If manual counting of corpora lutea is performed, then accurate quantification can be achieved, but the workload and potential for errors increase
Solution Approach 1:
The patent implements self-service through automated deep learning models that independently perform the counting of corpora lutea without human intervention. The trained neural networks automatically process histological images, detect CL structures, and generate counts, making the system self-sufficient and eliminating manual workload while maintaining accuracy and reducing errors.
Solution Approach 2:
The system creates digital replicas of the counting process through trained deep learning models that have learned from annotated training data. These models replicate the expert counting capability and apply it consistently across all images, eliminating variability and error associated with manual counting while maintaining high accuracy.
3Reliability
If complete understanding of normal ovarian morphology and estrous cycle variation is required, then accurate detection of ovarian impairment can be achieved, but the difficulty of detection and measurement increases
Solution Approach 1:
The patent applies preliminary action by pre-training deep learning models on extensive datasets of normal ovarian morphology across different stages of the estrous cycle. The models are trained beforehand to recognize normal variations, enabling them to reliably detect deviations and impairments without requiring real-time expert knowledge of ovarian biology during the actual analysis.
Solution Approach 2:
The system makes the complexity of ovarian morphology understanding self-service to the algorithm rather than requiring human experts to continuously reference and apply complex biological knowledge. The deep learning model internally encodes knowledge of normal ovarian morphology and automatically applies it to detect impairments, reducing the operational difficulty while maintaining reliable detection.
Data Source
AI summary
The present disclosure relates to a deep learning neural network that can identify corpora lutea in the ovaries and a rules-based technique that can count the corpora lutea identified in the ovaries and infer an ovarian toxicity of a compound based on the count of the corpora lutea (CL). Particularly, aspects of the present disclosure are directed to obtaining a set of images of tissue slices from ovaries treated with an amount of a compound; generating, using a neural network model, the set of images with a bounding box around objects that are identified as the CL within the set of images based on coordinates predicted for the bounding box; counting the bounding boxes within the set of images to obtain a CL count for the ovaries; and determining an ovarian toxicity of the compound at the amount based on the CL count.