Ignite Creation Date:
2024-05-06 @ 8:09 PM
Last Modification Date:
2024-10-26 @ 3:21 PM
Study NCT ID:
NCT06270797
Status:
RECRUITING
Last Update Posted:
2024-02-21
First Post:
2024-02-14
Brief Title:
Pre-anesthesia Imaging-based Respiratory Assessment and Analysis
Sponsor:
Kaohsiung Medical University Chung-Ho Memorial Hospital
Organization:
Kaohsiung Medical University Chung-Ho Memorial Hospital
Study Overview
Official Title:
Pre-anesthesia Imaging-based Respiratory Assessment and Analysis
Status:
RECRUITING
Status Verified Date:
2023-12
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
This study is to establish a preoperative respiratory imaging assessment database and develop a difficult intubation risk prediction model and further risk analysis We attempt to construct it into a pre-anesthesia intubation risk assessment software as the clinical decision support system
Detailed Description:
Anesthesia respiratory assessment is an important issue for anesthesiologists to evaluate the respiratory status and airway management of patients before surgery The American Society of Anesthesiologists ASA updated its guidelines in 2022 emphasizing the importance of comprehensive respiratory assessment in the guidelines
Various risk factors have been proposed in past literature for discussion and corresponding to these risk factors there is currently no single factor that can predict difficult intubation completely Existing investigations into difficult intubation factors mostly focus on high-risk populations including patients with morbid obesity where significant differences have been identified but not developed into predictive models
With the rapid development of AI-related technologies in recent years numerous image-related AI frameworks have been proposed In recent years attempts have been made to combine various clinical risk factors using machine learning methods to create automated prediction models for difficult intubation However their effectiveness has not met expectations reflecting the significant clinical problem of difficulty in prediction that remains unresolved
This study is an observational study aimed at analyzing and establishing patient image data refining various data engineering techniques and optimizing existing prediction model frameworks to enhance their medical value Additionally the focus of this project will be on establishing more prediction models to improve existing clinical decision support systems
Various risk factors have been proposed in past literature for discussion and corresponding to these risk factors there is currently no single factor that can predict difficult intubation completely Existing investigations into difficult intubation factors mostly focus on high-risk populations including patients with morbid obesity where significant differences have been identified but not developed into predictive models
With the rapid development of AI-related technologies in recent years numerous image-related AI frameworks have been proposed In recent years attempts have been made to combine various clinical risk factors using machine learning methods to create automated prediction models for difficult intubation However their effectiveness has not met expectations reflecting the significant clinical problem of difficulty in prediction that remains unresolved
This study is an observational study aimed at analyzing and establishing patient image data refining various data engineering techniques and optimizing existing prediction model frameworks to enhance their medical value Additionally the focus of this project will be on establishing more prediction models to improve existing clinical decision support systems
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None