Ventilation Image Monitoring for Detecting Spirometry Deviations
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Solution Overview
Problem
Clinicians struggle to interpret complex ventilation system parameters and detect subtle deviations in spirometry images, leading to potential issues like a leaking laryngeal mask or kinked endotracheal tube, especially when overseeing multiple patients, and automated monitoring is lacking.
Innovation Solution
Implement automated comparison models to analyze ventilation parameter images, including waveforms and spirometry, against annotated baseline images to identify deviations and alert clinicians, with a weaning protocol for spontaneous breathing detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If clinicians manually monitor and interpret ventilation parameters, then they can exercise clinical judgment and adapt to individual patient needs, but monitoring efficiency decreases and subtle deviations may be missed
Solution Approach 1:
The patent replaces manual clinical interpretation of ventilation parameters with an automated image processing system. The system converts ventilation parameters into visual images (waveform images, spirometry images) and uses computer vision algorithms to automatically detect deviations, substituting the mechanical process of manual chart review with automated digital image analysis that operates continuously without human intervention.
Solution Approach 2:
The patent introduces visual images as an intermediary between raw ventilation data and clinician decision-making. Instead of clinicians directly interpreting complex numerical and graphical ventilation parameters, the system transforms these parameters into standardized visual images that can be rapidly processed and compared, serving as an intermediate representation that bridges data and clinical judgment.
2Adaptability or versatility
If clinicians oversee multiple patients, then workload distribution increases, but the ability to detect subtle deviations in each patient decreases
Solution Approach 1:
The system performs self-monitoring by automatically comparing current patient ventilation images against stored baseline images and generating alerts when deviations are detected. This self-service capability allows the ventilation system to continuously monitor itself without requiring clinician attention, freeing clinicians to manage multiple patients while maintaining reliable detection through automated comparison.
Solution Approach 2:
The system performs preliminary action by pre-storing baseline ventilation images for each patient and continuously comparing current images against these baselines. This preliminary preparation of reference data enables rapid detection of deviations without requiring clinicians to review each parameter in detail, allowing them to oversee multiple patients effectively.
3Reliability
If automated monitoring systems are implemented, then continuous monitoring is possible, but system complexity increases
Solution Approach 1:
The patent applies universality by using a single image processing framework to handle multiple types of ventilation parameters (waveform, spirometry, pressure, flow, volume) and multiple detection tasks (baseline comparison, deviation detection, alert generation). This multi-functional approach consolidates what would otherwise be separate complex systems into one unified platform, reducing overall system complexity while maintaining continuous monitoring capability.
Solution Approach 2:
The system uses copying by creating visual representations (images) of ventilation parameters that can be stored and compared. Instead of working directly with complex raw data streams, the system copies the essential information into standardized visual formats, simplifying the comparison process and reducing computational complexity while enabling continuous automated monitoring.
4Measurement precision
If detailed analysis of ventilation parameters is performed, then detection precision improves, but time required for analysis increases
Solution Approach 1:
The patent applies segmentation by dividing the ventilation monitoring task into distinct visual components (waveform images, spirometry images, pressure graphs, flow graphs, volume graphs). Each component can be independently processed and compared against its baseline, allowing parallel analysis that maintains detection precision while reducing total analysis time through efficient task decomposition.
Data Source
AI summary
Methods and systems are provided for a ventilation system. In one example, a method includes obtaining one or more patient ventilation parameter images of a patient with the ventilation system while the patient is undergoing mechanical ventilation; obtaining one or more reference ventilation parameter images; processing, with at least one comparison model, each patient ventilation parameter image and each reference ventilation parameter image to characterize at least one feature in each patient ventilation parameter image and each reference ventilation parameter image, including converting each patient ventilation parameter image and each reference ventilation parameter image to a binary mask; identifying, based on of the at least one feature, a deviation between a patient ventilation parameter image and a corresponding reference ventilation parameter image; and in response to the identifying, outputting a notification that indicates the deviation.