Unique ID issued by UMIN | UMIN000040123 |
---|---|
Receipt number | R000045753 |
Scientific Title | Examination of difficult intubation evaluation using face recognition system |
Date of disclosure of the study information | 2020/05/01 |
Last modified on | 2020/09/03 10:54:50 |
Examination of difficult intubation evaluation using face recognition system
Examination of difficult intubation evaluation using face recognition system
Examination of difficult intubation evaluation using face recognition system
Examination of difficult intubation evaluation using face recognition system
Japan |
Elective surgery patients
Anesthesiology |
Others
NO
The aim is to create a classifier (system) that can determine intubation difficulty using a face authentication system
Efficacy
Create a classifier (system) that can determine intubation difficulties, make a Receiver operating characteristic curve, and determine sensitivity and specificity
Observational
20 | years-old | <= |
Not applicable |
Male and Female
Patients undergoing elective surgery at Yamagata University Hospital
Brain surgery
Nasal surgery
Dental surgery
Thyroid surgery
Cervical spine surgery
200
1st name | Tatsuya |
Middle name | |
Last name | Hayasaka |
Yamagata University Faculty of Medicine
Department of Anesthesia
9909585
2-2-2 Iida-nishi, Yamagata city, Yamagata 990-9585, Japan
023-628-5400
hayasakatatsuya1101@gmail.com
1st name | Tatsuya |
Middle name | |
Last name | Hayasaka |
Yamagata University Faculty of Medicine
Department of Anesthesia
9909585
2-2-2 Iida-nishi, Yamagata city, Yamagata 990-9585, Japan
023-628-5400
hayasakatatsuya1101@gmail.com
Yamagata University Faculty of Medicine
Department of Anesthesiology, Yamagata University Faculty of Medicine
Self funding
The Ethical Review Committee of Yamagata University Faculty of Medicine
2-2-2, Iida-nishi, yamagata-shi, Yamagata JAPAN
0236285015
ikekenkyu@jm.kj.yamagata-u.ac.jp
NO
2020 | Year | 05 | Month | 01 | Day |
Unpublished
Completed
2020 | Year | 04 | Month | 01 | Day |
2020 | Year | 01 | Month | 31 | Day |
2020 | Year | 04 | Month | 10 | Day |
2030 | Year | 03 | Month | 31 | Day |
An anesthesiologist performs a tracheal intubation with a Macintosh laryngoscope as usual, and records the Cormack classification (degree of intubation difficulty) in the medical record as usual. After the operation, collect patient information on airway management while grasping the Cormack classification. In addition, a plurality of photographs such as a frontal photograph and a profile photograph of the patient are taken. Machine learning is performed, and a classifier is created as supervised learning. Then create an Receiver operating characteristic curve to determine sensitivity and specificity.
2020 | Year | 04 | Month | 10 | Day |
2020 | Year | 09 | Month | 03 | Day |
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000045753