Predicting Difficult Airway Management Through Machine Learning of Patient Photograph

Development and validation of deep learning based algorithm for airway evaluation and prediction of difficult airway management using patient's airway evaluation photograph

Status

Active, not recruiting

Phases

Unknown

Study type

Interventional

Source

CRIS

Registry ID

KCT0004193

Enrollment

2000

Registered

2019-08-13

Start date

2019-09-02

Completion date

Unknown

Last updated

2019-08-26

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

Conditions

None listed

Interventions

Procedure/Surgery : All patients undergoing general anesthesia are photographed before surgery (front, side, neck extension, mouth). Mask ventilation and tracheal intubation during general anesthesia.

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Adults 19 years of age or older who are scheduled for general anesthesia.

Exclusion criteria

Exclusion criteria: Patients scheduled for airway related surgery. Patients with endotracheal intubation before surgery. Patients who do not have tracheal intubation during general anesthesia. Patients tracheal intubation using glide scope.

Design outcomes

Primary

Measure	Time frame
cormack -lehane score	—

Secondary

Measure	Time frame
mask ventilation grade	—

Countries

Korea, Republic of

Contacts

Public ContactYoungsuk Kwon

Hallym University Medical Center- Chuncheon

gettys@naver.com+82-33-240-5000

Outcome results