Random Forest Cerebral Perfusion Classification

Solution Overview

Problem

Current cerebral perfusion imaging technologies rely on large-scale equipment like CT and MRI, making it difficult to assess cerebral perfusion states in special scenarios such as aerospace and outdoor emergency settings due to equipment size and complexity.

Innovation Solution

A cerebral perfusion state classification apparatus and method using a transceiver module to receive physiological feature data and a processor to input data into a random forest model for predicting cerebral perfusion states, allowing classification without large-scale equipment, and enabling integration of more physiological features for accurate distinction.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If large-scale equipment such as CT and MRI is used for cerebral perfusion examination, then measurement precision is improved, but device complexity and ease of operation deteriorate

Engineering Contradiction:

Improvecerebral perfusion examination accuracyVSAvoidequipment complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent creates a virtual copy of the cerebral perfusion examination system through a random forest model trained on CT/MRI data. Instead of physically transporting large imaging equipment, the system copies the essential diagnostic capability into a software model that can run on portable devices, maintaining examination accuracy while eliminating the complexity of physical equipment transport and operation in field scenarios

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent replaces the mechanical/imaging system (CT/MRI hardware) with an information-processing system (random forest model). The physical examination process is substituted by a computational model that processes physiological data through algorithmic decision trees, eliminating the need for complex imaging machinery while maintaining diagnostic precision

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If large-scale equipment such as CT and MRI is used for cerebral perfusion examination, then measurement precision is improved, but ease of operation deteriorates

Engineering Contradiction:

Improvecerebral perfusion examination accuracyVSAvoidoperation simplicity

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The patent creates a virtual copy of the cerebral perfusion examination system through a random forest model trained on CT/MRI data. Instead of physically transporting large imaging equipment, the system copies the essential diagnostic capability into a software model that can run on portable devices, maintaining examination accuracy while eliminating the complexity of physical equipment transport and operation in field scenarios

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent replaces the mechanical/imaging system (CT/MRI hardware) with an information-processing system (random forest model). The physical examination process is substituted by a computational model that processes physiological data through algorithmic decision trees, eliminating the need for complex imaging machinery while maintaining diagnostic precision

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If traditional cerebral perfusion imaging is used, then measurement precision is improved, but adaptability to special scenarios deteriorates

Engineering Contradiction:

Improvecerebral perfusion state assessment accuracyVSAvoidadaptability to aerospace and emergency scenarios

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent creates a virtual copy of the cerebral perfusion examination system through a random forest model trained on CT/MRI data. Instead of physically transporting large imaging equipment, the system copies the essential diagnostic capability into a software model that can run on portable devices, maintaining examination accuracy while eliminating the complexity of physical equipment transport and operation in field scenarios

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent changes the fundamental parameters of the examination system from physical imaging hardware to computational algorithms. By transforming the system from a hardware-dependent imaging platform to a software-based predictive model that processes physiological data, the system achieves adaptability to diverse environments including aerospace and emergency scenarios while maintaining diagnostic precision

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11621079B2Cerebral perfusion state classification apparatus, method and device, and model training apparatus

Publication Date: 2023.04.04 BEIJING FRIENDSHIP HOSPITAL CAPITAL MEDICAL UNIV

AI summary

The present disclosure discloses a cerebral perfusion state classification apparatus, method and device, and a model training apparatus. In the apparatus, a transceiver module is used for receiving physiological feature data from different data collection devices; and a processor is used for extracting physiological features from the physiological feature data; inputting the physiological features into a random forest model to cause a plurality of decision-making trees in the random forest model to predict a cerebral perfusion state type corresponding to the physiological features; and classifying a cerebral perfusion state based on the cerebral perfusion state type corresponding to the physiological features.