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Patient-Ventilator Dyssynchrony Detection With a Machine Learning Algorithm

NCT06506123 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 80

Last updated 2024-07-17

No results posted yet for this study

Summary

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?

Conditions

Interventions

DEVICE

Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies

Machine learning algorithm to detect and classify patient-ventilator dyssynchronies, which is integrated in the mechanical ventilator (Fleximag Max, Magnamed, Brazil).

Sponsors & Collaborators

  • Magnamed Tecnologia Medica S/A

    collaborator UNKNOWN

  • University of Sao Paulo General Hospital

    lead OTHER

Principal Investigators

  • Eduardo LV Costa, MD, PhD · University of Sao Paulo

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-05-25
Primary Completion
2025-05-24
Completion
2025-12-24

Countries

  • Brazil

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06506123 on ClinicalTrials.gov