Deep Learning Pain Assessment via Brain Activity Analysis
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Solution Overview
Problem
Conventional methods for assessing spontaneous pain in animal models are limited in their ability to accurately detect human-like pain, relying mainly on facial expressions and behavioral compensation, which are not sufficient for understanding pain mechanisms or developing effective therapeutic drugs.
Innovation Solution
A deep learning model-based pain assessment method that analyzes brain activity images from specific brain areas using a bidirectional recurrent neural network, inputting images of regions of interest into multiple layers to assess pain, allowing for more accurate pain evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional spontaneous pain assessment methods based on facial expression changes or behavioral compensation are used, then the assessment process is simple, but the measurement precision of pain detection is insufficient
Solution Approach 1:
The patent replaces conventional behavioral observation methods with a neural network-based image analysis system. The system uses deep learning models to automatically analyze facial expression images and detect pain indicators, substituting manual behavioral assessment with automated computational analysis to achieve higher measurement precision
Solution Approach 2:
The patent introduces an intermediary neural network system between the subject animal and the assessment outcome. This intermediary component processes facial expression images through multiple neural network layers to extract pain-related features, acting as a mediator that transforms raw image data into quantified pain assessment results
2Measurement precision
If deep learning model with multiple input layers is used to analyze brain activity images, then the measurement precision of pain assessment is improved, but the device complexity increases
Solution Approach 1:
The patent segments the pain assessment task into multiple specialized neural network layers, each processing specific features from brain activity images. The bidirectional recurrent neural network divides analysis into forward and backward processing streams, with each layer focusing on extracting particular pain-related patterns from different temporal and spatial dimensions of the input images
Solution Approach 2:
The patent adds temporal dimension to the analysis by using bidirectional recurrent neural networks that process brain activity images across multiple time points. The model analyzes both past and future temporal contexts surrounding each measurement point, transforming static image analysis into dynamic temporal pattern recognition to improve pain assessment precision
Data Source
AI summary
Disclosed is a pain assessment method using a deep learning model, the pain assessment method including operations of receiving, by an analysis device, an image indicating activity in a specific brain area of a subject animal and allowing the analysis device to input images of regions of interest in the image into a neural network model and assess the pain of the subject animal according to a result output by the neural network model.