ML Image Recognition for Ventilatory Data Condition Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical ventilator systems rely on manual clinical assessments and imaging procedures to determine patient conditions, which are time-consuming and inefficient, especially in identifying conditions like asthma, ARDS, and COPD, requiring more accurate and automated methods for predicting clinical conditions based on ventilatory data.
Innovation Solution
The use of machine-learning image recognition models that convert ventilatory data into human-indecipherable images, which are then processed to predict clinical conditions such as asthma, ARDS, or COPD, allowing for automated adjustment of ventilator settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual clinical assessments and imaging procedures are used to determine patient conditions, then diagnostic accuracy can be achieved, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual clinical assessments and traditional imaging procedures with an automated machine learning image recognition system. The system converts ventilatory data into images and uses trained ML models to automatically predict clinical conditions, eliminating the need for time-consuming manual evaluations while maintaining diagnostic accuracy through algorithmic analysis of ventilatory patterns.
Solution Approach 2:
The patent creates visual representations (images) of ventilatory data that can be processed by machine learning models. By converting numerical ventilatory parameters into image formats, the system enables automated pattern recognition and condition prediction, significantly reducing the time required for clinical assessment compared to traditional manual methods.
2Extent of automation
If traditional ventilator systems are used with manual condition identification, then system simplicity is maintained, but automation and efficiency are limited
Solution Approach 1:
The patent integrates multiple functions into a unified automated system: ventilatory data acquisition, image conversion, machine learning inference, and condition prediction. The ventilator system now performs both traditional ventilation delivery and automated clinical condition identification, reducing the need for separate manual assessment processes while managing complexity through integrated software architecture.
Solution Approach 2:
The system enables the ventilator to automatically assess patient conditions without requiring external manual intervention. The machine learning model processes ventilatory data and generates condition predictions autonomously, allowing the device to self-diagnose clinical states and potentially self-adjust ventilation parameters based on predicted conditions.
3Productivity
If manual assessment methods are used for condition identification, then implementation simplicity is maintained, but productivity and care efficiency decrease
Solution Approach 1:
The system continuously converts ventilatory data into images and maintains ready-trained machine learning models for immediate condition prediction. This preliminary preparation of data representations and predictive models enables rapid condition identification when needed, significantly improving care efficiency compared to reactive manual assessments without requiring complex real-time processing during critical decision moments.
Data Source
AI summary
The technology relates to methods and systems for recognition of conditions from ventilation data. The methods may include acquiring ventilation data for ventilation of a patient during a time period; generating an image based on the acquired ventilation data; providing, as input into a trained machine learning model, the generated image, wherein the trained machine learning model was trained based on images having a same type as the generated image; and based on output from the trained machine learning model, generating a predicted condition of the patient. The image may be generated by storing ventilatory data as pixel channel values to generate a human-indecipherable image.