This is a diagnostic study aiming to compare accuracy to detect and classify patient-ventilator dyssynchronies by a machine learning algorithm, compared to the gold-standard defined as dyssynchronies diagnosed and classified by mechanical ventilator and esophageal pressure waveforms analyzed by experts.

The main question of this study is:

• Are patient-ventilator dyssynchronies accurately detected and classified by an artificial intelligence algorithm when compared to experts analyzing esophageal pressure and mechanical ventilator waveforms?

This is a diagnostic, observational study, aiming to assess patient-ventilator dyssynchrony automated detection and classification by a machine learning algorithm. Accuracy of the machine learning algorithm will be compared with the gold-standard, defined as dyssynchronies detected and classified by mechanical ventilation experts.

Experts will analyzed airway pressure, flow, volume and esophageal pressure waveforms to detect and classify dyssynchronies.

Inclusion Criteria:

• Subjects under assisted or assist-controlled mechanical ventilation and monitored with esophageal pressure balloon.

Exclusion Criteria:

• Refusal from patient's family or attending physician

Adult subjects under mechanical ventilation, with an assisted or assist-controlled mode, who are monitored with an esophageal pressure balloon due to clinical indication are eligible.