UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000052233 |
|---|---|
| Receipt number | R000059622 |
| Scientific Title | Development of an Artificial Intelligence Model for Mask Ventilation Difficulty/Intubation Difficulty Classification Using Deep Learning with Patient Facial Images |
| Date of disclosure of the study information | 2023/10/01 |
| Last modified on | 2023/09/18 15:48:47 |
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Basic information
Public title
Development of an Artificial Intelligence Model for Mask Ventilation Difficulty/Intubation Difficulty Classification Using Deep Learning with Patient Facial Images
Acronym
Development of an Artificial Intelligence Model for Mask Ventilation Difficulty/Intubation Difficulty Classification Using Deep Learning with Patient Facial Images
Scientific Title
Development of an Artificial Intelligence Model for Mask Ventilation Difficulty/Intubation Difficulty Classification Using Deep Learning with Patient Facial Images
Scientific Title:Acronym
Development of an Artificial Intelligence Model for Mask Ventilation Difficulty/Intubation Difficulty Classification Using Deep Learning with Patient Facial Images
Region
| Japan |
Condition
Condition
Scheduled surgical cases
Classification by specialty
| Anesthesiology | Emergency medicine | Intensive care medicine |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
During cardiopulmonary resuscitation, mask ventilation and intubation are important techniques. When the patient's face is difficult to mask ventilate, intubation should be performed immediately, and the clinical skill to judge whether the patient is difficult to mask ventilate is necessary. In addition, cardiac massage must be interrupted during intubation, so it is necessary to quickly identify difficult intubation, but even physicians skilled in intubation may have difficulty diagnosing difficult intubation in advance. If it can be determined in advance that the patient is difficult to ventilate or intubate, cardiac massage can be prioritized instead of mask ventilation or intubation, and a physician skilled in airway management can be selected to assist the patient.
With the recent development of AI (Artificial Intelligence) technology, deep learning has become an integral part of our daily lives. By using deep learning, we hypothesize that we can determine the presence or absence of mask ventilation or intubation difficulties. The purpose of this study is to create and validate a classifier (system) that can discriminate difficult mask ventilation/difficult intubation using deep learning.
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Prediction accuracy of classifiers (systems) that can discriminate between mask ventilation difficulties and intubation difficulties
Key secondary outcomes
Base
Study type
Observational
Study design
Basic design
Randomization
Randomization unit
Blinding
Control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
Purpose of intervention
Type of intervention
Interventions/Control_1
Interventions/Control_2
Interventions/Control_3
Interventions/Control_4
Interventions/Control_5
Interventions/Control_6
Interventions/Control_7
Interventions/Control_8
Interventions/Control_9
Interventions/Control_10
Eligibility
Age-lower limit
| 20 | years-old | <= |
Age-upper limit
| 99 | years-old | > |
Gender
Male and Female
Key inclusion criteria
Patients scheduled for surgery at Yamagata University Hospital
Key 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
Target sample size
800
Research contact person
Name of lead principal investigator
| 1st name | Tatsuya |
| Middle name | |
| Last name | Hayasaka |
Organization
Yamagata University Medical School Hospital
Division name
Department of Anesthesiology
Zip code
990-2331
Address
2-2-2, Iida-Nishi, Yamagata City, Yamagata Prefecture
TEL
0236285400
hayasakatatsuya1101@gmail.com
Public contact
Name of contact person
| 1st name | Tatsuya |
| Middle name | |
| Last name | Hayasaka |
Organization
Yamagata University Medical School Hospital
Division name
Department of Anesthesiology
Zip code
990-2331
Address
2-2-2, Iida-Nishi, Yamagata City, Yamagata Prefecture
TEL
0236285400
Homepage URL
hayasakatatsuya1101@gmail.com
Sponsor or person
Institute
Yamagata Universal Faculty of Medcine
Institute
Department
Personal name
Funding Source
Organization
Ministry of Education, Culture, Sports, Science and Technology
Organization
Division
Category of Funding Organization
Japanese Governmental office
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
The Ethical Review Committee of Yamagata University Faculty of Medicine
Address
2-2-2, Iida-Nishi, Yamagata City, Yamagata Prefecture
Tel
0236285015
ikekenkyu@jm.kj.yamagata-u.ac.jp
Secondary IDs
Secondary IDs
NO
Study ID_1
Org. issuing International ID_1
Study ID_2
Org. issuing International ID_2
IND to MHLW
Institutions
Institutions
Other administrative information
Date of disclosure of the study information
| 2023 | Year | 10 | Month | 01 | Day |
Related information
URL releasing protocol
Publication of results
Unpublished
Result
URL related to results and publications
Number of participants that the trial has enrolled
Results
Results date posted
Results Delayed
Results Delay Reason
Date of the first journal publication of results
Baseline Characteristics
Participant flow
Adverse events
Outcome measures
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
Preinitiation
Date of protocol fixation
| 2023 | Year | 09 | Month | 18 | Day |
Date of IRB
Anticipated trial start date
| 2023 | Year | 10 | Month | 01 | Day |
Last follow-up date
| 2027 | Year | 12 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
Patient information regarding airway security will be collected with consent during the preoperative rounds of standby surgical patients (e.g., Mallampati score, age, gender, BMI, intermolar distance, presence of protruding dentition, cervical perimeter, restriction of forward mandibular movement, and restriction of forward and backward flexion movement). In addition, multiple photographs should be taken of the patient, including frontal and profile views. A classifier (system) for mask ventilation difficulty or intubation difficulty is created from these facial photographs.
The prediction accuracy of the created image system will be verified, and the sensitivity, specificity, and Area Under the Curve for difficult mask ventilation or difficult intubation will be determined. In addition, based on patient information regarding airway security (Mallampati score, age, gender, BMI, intermolar distance, presence of protruding teeth, cervical circumference, limitation of forward mandibular movement, limitation of forward and backward flexion movement), the predictive accuracy of the numerical values will be validated to obtain sensitivity, specificity, and area under the curve for mask ventilation difficulty or intubation difficulty. the Curve.
I would compare the prediction accuracy of the system generated from the patient's mug shot to the prediction accuracy generated from the numerical values.
Management information
Registered date
| 2023 | Year | 09 | Month | 18 | Day |
Last modified on
| 2023 | Year | 09 | Month | 18 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000059622
