Let’s get in sync: current standing and future of AI-based detection of patient-ventilator asynchrony

Rietveld, T.P., van der Ster, B.J.P., Schoe, A. et al. Let’s get in sync: current standing and future of AI-based detection of patient-ventilator asynchrony. ICMx 13, 39 (2025). https://doi.org/10.1186/s40635-025-00746-8

Summary of "Let’s get in sync: current standing and future of AI-based detection of patient-ventilator asynchrony"

Abstract

Patient-ventilator asynchrony (PVA), a frequent mismatch between the patient’s breathing effort and ventilator support, can lead to adverse outcomes such as prolonged ventilation, lung injury, and diaphragm dysfunction. While visual detection is difficult and time-consuming, artificial intelligence (AI) offers promise for real-time, accurate PVA identification. This review evaluates 19 studies using rule-based, machine learning (ML), and deep learning (DL) approaches, highlighting challenges and future pathways for clinical integration.

Key Points:

1. Prevalence and Consequences of PVA: PVA is highly prevalent (up to 90% of mechanically ventilated patients) and linked to worsened outcomes such as VILI, diaphragm atrophy, longer ventilation duration, and higher mortality.

2. Limitations of Visual Inspection: Clinician-based waveform interpretation is inconsistent and labor-intensive, often underestimating both frequency and type of PVA due to the complexity and continuous nature of ventilator data.

3. Types of AI Used: Reviewed approaches include rule-based systems (with fixed thresholds), ML algorithms (e.g., random forests), and DL neural networks, each with unique strengths and limitations in PVA detection accuracy and generalizability.

4. Rule-Based Performance: Although transparent and clinically intuitive, rule-based algorithms detect limited PVA types and may lack robustness across patient populations.

5. ML Algorithms Expand Capabilities: ML models have shown high sensitivity and specificity (e.g., >0.90 for premature and delayed cycling) and can detect multiple PVA types simultaneously, especially with adequate feature engineering.

6. Advantages of Deep Learning: DL models, particularly recurrent and convolutional neural networks, outperform rule-based and ML systems in robustness and generalizability, especially for detecting ineffective efforts and double triggers.

7. Licensed Software Examples: Commercial solutions such as Better Care®, Syncron-E™, and Remote-VentilateView are emerging, but often detect only a subset of PVAs (mostly ineffective efforts) and require further clinical validation.

8. Lack of Gold Standards: Most studies lack esophageal pressure (Pes) or diaphragm electrical activity (EAdi) data for validation, undermining their ability to truly gauge patient effort and creating a barrier to widespread acceptance.

9. Real-Time and Bedside Integration Challenges: For clinical deployment, algorithms must shift from offline validation to real-time performance in diverse ICU environments with standardized inputs, outputs, and definitions of PVA.

10. Future Directions: The authors emphasize the need for multicenter datasets, integration of ground truth markers like Pes/EAdi, standardization of PVA types, and transparent AI models that can adapt across ventilator modes and patient phenotypes.

Conclusion

AI-based detection of patient-ventilator asynchrony is a promising tool to improve personalized mechanical ventilation by reducing clinician workload and improving detection accuracy. However, to achieve real-time bedside implementation, future efforts must focus on algorithm generalizability, comprehensive PVA detection across types, and external validation using gold-standard physiological markers like Pes and EAdi. Standardized frameworks and collaborative validation studies will be crucial to realize the full clinical impact of AI in managing PVA.

ACCESS FULL ARTICLE HERE

Watch the following video on "Webinar: Patient-Ventilator Asynchrony" by RT: For Decision Makers in Respiratory Care

Discussion Questions

1. How can future AI algorithms incorporate Pes and EAdi measurements to better reflect true patient effort and minimize misclassification?

2. What are the practical barriers to implementing real-time AI monitoring of PVA in ICUs with varying ventilator platforms?

3. Could AI-driven PVA detection play a role in tailoring sedation strategies and ventilator settings to optimize synchrony in real-time?

Javier Amador-Castañeda, BHS, RRT, FCCM, PNAP

Interprofessional Critical Care Network (ICCN)

Take Advantage of This Resource

I encourage you to explore this growing library of articles and leverage it to stay informed on the latest in critical care. Visit the collection today at: https://perfusfind.com/ic/

This is another step in making high-quality, evidence-based information easily accessible to the critical care community. As always, thank you for your continued support!

As always, don’t forget to like, share, and subscribe. See you on the other side!

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions