INTELLI-PVA: Informative sample annotation-based contrastive active learning for cross-domain patient-ventilator...

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Medicine
• Critical Care Medicine

Background:

• Patient-ventilator asynchrony (PVA) is common in mechanically ventilated patients and negatively affects outcomes.
• Real-time PVA detection is difficult due to variations in patient-ventilator interactions and overlapping PVA types.
• Existing AI systems struggle with cross-domain generalization in PVA detection.

Purpose of the Study:

• To develop an efficient artificial intelligence (AI) framework, INTELLI-PVA, for cross-domain patient-ventilator asynchrony (PVA) detection.
• To address limitations in current AI systems, including clinical variability and morphological overlap of PVA types.
• To enable practical deployment of AI-assisted ventilation monitoring across diverse clinical settings.

Main Methods:

• Developed a hybrid two-stage PVA classifier combining a deep learning model and a rule-based algorithm.
• Utilized contrastive learning for pre-training and active learning for iterative domain adaptation with minimal expert annotation.
• The deep learning model identified compound PVA types, while the rule-based algorithm differentiated subtypes based on triggering signatures.

Main Results:

• INTELLI-PVA achieved a superior average F1-score of 0.849 in classifying eight PVA types across two centers.
• The framework demonstrated respiratory therapist-level recognition ability (average Cohen's κ=0.850) on unseen data.
• Effective cross-domain detection was achieved with only 1000 annotated samples per target domain.

Conclusions:

• INTELLI-PVA provides a practical and efficient solution for high-accuracy, cross-domain PVA detection.
• The framework minimizes annotation burden, facilitating AI-assisted ventilation monitoring in various clinical environments.
• This AI approach enhances the ability to manage mechanical ventilation effectively by addressing PVA challenges.