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Artificial Intelligence Algorithm to Monitor Inspiratory Muscle Effort and Patient-Ventilator Dyssynchrony During
Glauco M Plens1,2, Caio César Araújo Morais1,3, Thaís Gregol1
1Divisao de Pneumologia, Faculdade de Medicina, Instituto do Coracao, Hospital das Clinicas Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil.
Critical Care Medicine
|May 21, 2026
Summary
A new artificial intelligence (AI) algorithm noninvasively estimates inspiratory muscle pressure (Pmus) during mechanical ventilation. This AI tool offers continuous monitoring comparable to invasive methods, improving patient care.
Area of Science:
- Critical Care Medicine
- Biomedical Engineering
- Respiratory Physiology
Background:
- Estimating inspiratory muscle pressure (Pmus) during mechanical ventilation is crucial for patient management.
- Current methods are often invasive or rely on intermittent occlusion maneuvers, limiting continuous monitoring.
- There is a need for noninvasive, real-time methods to assess Pmus and ventilator synchrony.
Purpose of the Study:
- To develop and evaluate a noninvasive artificial intelligence (AI) algorithm for real-time estimation of Pmus during mechanical ventilation.
- To compare the AI-based Pmus estimation (Pmus,AI) against the gold standard of esophageal manometry (Pmus,es).
- To assess the AI algorithm's ability to detect patient effort, driving pressure, and ventilator synchrony.
Main Methods:
- A prospective diagnostic accuracy study was conducted in two ICUs.
- Adult patients under pressure support ventilation were included.
- The AI algorithm's Pmus estimations were compared with esophageal manometry and occlusion-based techniques (Pocc).
Main Results:
- The AI algorithm (Pmus,AI) demonstrated good performance, with a bias of 0.9 cm H2O and limits of agreement of -5.1, 6.9 cm H2O compared to Pmus,es.
- Pmus,AI accurately detected extreme values of Pmus and dynamic driving pressure (AUC > 0.8).
- The AI showed high sensitivity (86.5%) and specificity (77.4%) in detecting ineffective effort and autotriggering.
Conclusions:
- The developed AI algorithm provides a noninvasive and accurate method for estimating Pmus during mechanical ventilation.
- This AI tool enables continuous monitoring of patient effort and ventilator synchrony, comparable to intermittent occlusion techniques.
- The algorithm facilitates automatic detection of specific dyssynchronies, offering potential benefits for clinical decision-making.
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