Effective lung protective ventilation strategy using deep learning with graphic monitors : Development of an artificial intelligence prediction model.

Effective lung protective ventilation strategy using deep learning with graphic monitors : Development of an artificial intelligence prediction model. - ELPIS-grad STUDY

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000051938

Enrollment

200

Registered

2023-08-20

Start date

2023-08-20

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Ventilated patients admitted to the Yamagata University Hospital Advanced Intensive Care Center

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Ventilated patients admitted to the Advanced Intensive Care Center, Yamagata University Hospital

Exclusion criteria

Exclusion criteria: None

Design outcomes

Primary

Measure	Time frame
Compare the AI system's prediction of the need for setting changes and reasons for setting changes with the actual presence or absence of setting changes and reasons for setting changes by the intensivist. From that comparison, ROC curves are drawn and accuracy, sensitivity, specificity, and AUC are calculated.	—

Countries

Japan

Contacts

Public ContactTatsuya Hayasaka

Yamagata University Hospital Department of Anesthesiology

hayasakatatsuya1101@gmail.com0236285400

Outcome results