Related Experiment Video
Updated: May 22, 2026

Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit
Published on: September 6, 2024
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.
Objective:
Current methods for estimating inspiratory muscle pressure ( Pmus ) during mechanical ventilation are either invasive or dependent on occlusion maneuvers. A noninvasive artificial intelligence (AI) algorithm estimating in real-time the amplitude and timing of Pmus , enabling continuous monitoring of patient effort, driving pressure, and synchrony with the ventilator was designed, and its performance was evaluated against the gold standard obtained with esophageal manometry ( Pmus,es ).
Design:
A prospective diagnostic accuracy study.
Setting:
Two ICUs from the University of São Paulo, Brazil.
Patients:
Adult patients under pressure support ventilation.
Interventions:
None.
Measurements And Main Results:
Pmus estimated using AI ( Pmus,AI ) was compared with Pmus,es and to values derived from occlusion maneuvers, the pressure muscle index and the occlusion pressure ( Pocc ). Automatic detection of dyssynchronies based on Pmus,AI was compared with experts' classification. A total of 48 participants with 4918 cycles were analyzed. Pmus,es varied from 1.0 to 28.4 cm H 2 O. Pmus,AI showed a bias of 0.9 cm H 2 O, 95% limits of agreement -5.1, 6.9 cm H 2 O and detected extreme values of both Pmus,es and dynamic driving pressure with area under the receiver operating characteristic curve greater than 0.8. Pmus,AI accuracy was comparable to occlusion-based techniques. Sensitivity and specificity to detect ineffective effort, autotriggering or reverse triggering were 86.5% and 77.4%, respectively.
Conclusions:
AI presented good performance in detecting high and low Pmus , and allowed the automatic detection of specific types of dyssynchronies. This novel noninvasive method was comparable to intermittent techniques requiring occlusion maneuvers.
Related Concept Videos
Ventilatory Modes
There are three ventilatory modes: full support, partial support, and spontaneous. These are described below.
Full Support Modes
Full support modes include controlled mechanical ventilation, continuous mandatory...
Mechanical Ventilation I: Indication and Settings
Mechanical Ventilation II: Invasive Ventilation
Negative-Pressure Ventilators
Negative-pressure ventilators create a vacuum around the chest or body to draw air into the lungs, simulating breathing. This method does not require an...
Mechanical Ventilation III: Noninvasive Ventilation
Noninvasive Positive-Pressure Ventilation (NIPPV)
Assessment of Ventilation II: Respiratory Depth and Rhythm
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
Assessment of Ventilation I: Respiratory Rate
A Ventilation assessment is critical for monitoring a patient's health status. Respiration, one of the most accessible vital signs, provides insights into the function of numerous body systems and can indicate serious health issues, such as brainstem injuries from head trauma.
Critical Guidelines for Assessing Ventilation:

