Computer methods and programs in biomedicine · 2025
INTELLI-PVA: Informative sample annotation-based contrastive active learning for cross-domain patient-ventilator asynchrony detection.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhang Lingwei, Feng Xue, Lu Fei, Ding Zepeng, Yang Jiayi, Fang Luping, Ning Gangmin, Yuan Shuohui, Ge Huiqing, Pan Qing
Аннотация
BACKGROUND AND OBJECTIVE: Patient-ventilator asynchrony (PVA) is prevalent in mechanically ventilated patients and adversely impacts clinical outcomes, but its real-time detection remains challenging. While artificial intelligence (AI) systems show promise for PVA detection, their cross-domain generalization faces two major limitations: variability in patient-ventilator interactions across different clinical settings, and morphological overlap between PVA types. These challenges necessitate specialized AI solutions rather than conventional re-annotation approaches.
METHODS: We present the INTELLI-PVA framework for efficient cross-domain PVA detection on eight types. First, a hybrid two-stage PVA classifier was developed. A deep learning model, pre-trained on unannotated data using contrastive learning and fine-tuned using annotated data, identified four morphologically defined compound PVA types, each encompassing a reverse triggering (RT) and a non-RT type. A subsequent rule-based algorithm differentiated the subtypes within each compound type according to their triggering signatures. Then, the model was adapted to the target domain through an iterative active learning cycle, which selected the most informative samples for expert annotation and used them to fine-tune the model.
RESULTS: Established and validated on data from two centers encompassing 1190 patients and 124.975 million respiratory cycles, INTELLI-PVA demonstrates superior detection performance (average F1-score: 0.849) in classifying the eight PVA classes using only 1000 annotated samples per target domain, and achieves respiratory therapist-level recognition ability (average Cohen's κ=0.850) across unseen ventilator configurations and patient demographics.
CONCLUSIONS: INTELLI-PVA achieves high-accuracy, cross-domain PVA detection with minimal annotation burden, establishing a practical and efficient pathway for deploying AI-assisted ventilation monitoring in diverse clinical settings.
METHODS: We present the INTELLI-PVA framework for efficient cross-domain PVA detection on eight types. First, a hybrid two-stage PVA classifier was developed. A deep learning model, pre-trained on unannotated data using contrastive learning and fine-tuned using annotated data, identified four morphologically defined compound PVA types, each encompassing a reverse triggering (RT) and a non-RT type. A subsequent rule-based algorithm differentiated the subtypes within each compound type according to their triggering signatures. Then, the model was adapted to the target domain through an iterative active learning cycle, which selected the most informative samples for expert annotation and used them to fine-tune the model.
RESULTS: Established and validated on data from two centers encompassing 1190 patients and 124.975 million respiratory cycles, INTELLI-PVA demonstrates superior detection performance (average F1-score: 0.849) in classifying the eight PVA classes using only 1000 annotated samples per target domain, and achieves respiratory therapist-level recognition ability (average Cohen's κ=0.850) across unseen ventilator configurations and patient demographics.
CONCLUSIONS: INTELLI-PVA achieves high-accuracy, cross-domain PVA detection with minimal annotation burden, establishing a practical and efficient pathway for deploying AI-assisted ventilation monitoring in diverse clinical settings.