Establishment of Airway Database for Surgical Patients

• Conditions: Difficult Airway; Artificial Intelligence

• Registration Number: NCT03125837
• Lead Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University

Brief Summary

Difficult airway is a major reason of anesthesia related injuries with latent life threatening complications. Foresee difficult airway in the preoperative period is vital for the patient's safety. The aim of this study is to develop a computer algorithm that can detect whether the patient is a difficult airway based on photographs form six aspects. This method will be decreased potential complication related to difficult airway and increased patient safety.

Detailed Description

Introduction:

The primary purpose of the study is to develop a computer algorithm that can detect whether the patient is a difficult airway based on photographs from six different aspects.

Methods:

This study is divided into two parts. In the first part, we collected the patients' airway assessment score who underwent general anesthesia with endotracheal intubation assessed by an experienced attending anesthesiologists before and after intubation. Evaluation of airway score after tracheal intubation as the gold standard for airway assessment. Digital photographs of the face of each patient in frontal neutral view and in profile neutrals were obtained. Details of the photographs, each corresponding to a facial motion: (1) Frontal, neutral. (2) Frontal, mouth open. (3)Frontal, extreme mouth open and tongue out. (4)Frontal, extreme upper lip bite (5)Profile, neutral. (6) Profile, neutral, maximum head back. The patient's photographs and the airway evaluation score after intubation were input to the computer to train the computer. In the second part, the trained computer was used to evaluate the airway score of the new patient compared with that of the patient after intubation, and calculated the sensitivity.

Study Timeline

• Study First Submitted: 2017-03-13
• Status Verified: 2017-04
• Study First Submitted QC: 2017-04-19
• Last Update Submitted: 2017-04-19
• Study First Posted: 2017-04-24
• Last Update Posted: 2017-04-24
• Study Start: 2017-05
• Primary Completion: 2022-05
• Study Completion: 2022-05

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 50000

Inclusion Criteria

• General anesthesia-induced tracheal intubation in patients who undergoing elective surgical patients

Exclusion Criteria

• Patients with multiple facial injuries Patients who had undergone head or neck surgery Patients who need emergency operation

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
the sensitivity of artificial Intelligence to predict difficulty of facemask ventilation and endotracheal intubation	5 years	The outcome will be a computer algorithm that can detect whether the patient is a difficult airway based on photographs from six different aspects.Details of the photographs, each corresponding to a facial motion: (1) Frontal, neutral. (2) Frontal, mouth open. (3)Frontal, extreme mouth open and tongue out. (4)Frontal, extreme upper lip bite (5)Profile, neutral. (6) Profile, neutral, maximum head back.