Development of an Artificial Intelligence Model for Mask Ventilation Difficulty/Intubation Difficulty Classification Using Deep Learning with Patient Facial Images

Development of an Artificial Intelligence Model for Mask Ventilation Difficulty/Intubation Difficulty Classification Using Deep Learning with Patient Facial Images - Development of an Artificial Intelligence Model for Mask Ventilation Difficulty/Intubation Difficulty Classification Using Deep Learning with Patient Facial Images

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000052233

Enrollment

800

Registered

2023-10-01

Start date

2023-10-01

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

Scheduled surgical cases

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Patients scheduled for surgery at Yamagata University Hospital

Exclusion criteria

Exclusion criteria: Patients who cannot give consent Patients considered inappropriate by the anesthesiologist in the case Cardiac surgery cases Patients who cannot follow instructions Patients with limited mobility of the neck Patients whose facial appearance, mask ventilation, or intubation is affected by artifacts

Design outcomes

Primary

Measure	Time frame
Prediction accuracy of classifiers (systems) that can discriminate between mask ventilation difficulties and intubation difficulties	—

Countries

Japan

Contacts

Public ContactTatsuya Hayasaka

Yamagata University Medical School Hospital Department of Anesthesiology

hayasakatatsuya1101@gmail.com0236285400

Outcome results