UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000032012 |
|---|---|
| Receipt number | R000035399 |
| Scientific Title | Construction of monitoring camera system for medical use with the function of recogniting the facial imformation. |
| Date of disclosure of the study information | 2022/04/01 |
| Last modified on | 2019/04/21 15:40:30 |
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Basic information
Public title
Construction of monitoring camera system for medical use with the function of recogniting the facial imformation.
Acronym
Construction of the medical monitoring camera system.
Scientific Title
Construction of monitoring camera system for medical use with the function of recogniting the facial imformation.
Scientific Title:Acronym
Construction of the medical monitoring camera system.
Region
| Japan |
Condition
Condition
The patients aged 20 years or older who are entered at the intensive care unit.
Classification by specialty
| Intensive care medicine |
Classification by malignancy
Malignancy
Genomic information
NO
Objectives
Narrative objectives1
The purpose of this study is to construct a camera system with the function to automatically evaluate the scale of analgesia or sedation using face information.
Basic objectives2
Others
Basic objectives -Others
Development of camera system with AI
Evaluation of the effectiveness
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
The primary outcome is the correct answers rate of the prediction model which is constructed the machine learning.
The correct answer of the prediction model is as follow,
RASS, BPS: perfect maching or difference within 1 point.
VAS: perfect maching or difference within 10mm.
GCS: The maching of the 2 or full items
After the second year of research, we will incorporate the data obtained in the previous year into the model, and update the evaluation of the accuracy rate every year.
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
| Not applicable |
Gender
Male and Female
Key inclusion criteria
All patients entering the intensive care unit
Key exclusion criteria
Patients who are unable to agree to participate in this study
Patients deemed inappropriate as subjects by physicians
Target sample size
1400
Research contact person
Name of lead principal investigator
| 1st name | shunsuke |
| Middle name | |
| Last name | Takaki |
Organization
Yokohama city university school of medecine
Division name
Department of Anesthesiology and Critical Care Medicine
Zip code
2360004
Address
3-9, Fukuura, Kanazawa-ku, Yokohama, Japan
TEL
+81457872800
shunty5323@gmail.com
Public contact
Name of contact person
| 1st name | Shunsuke |
| Middle name | |
| Last name | Takaki |
Organization
Yokohama city university school of medecine
Division name
Department of Anesthesiology and Critical Care Medicine
Zip code
2360004
Address
3-9, Fukuura, Kanazawa-ku, Yokohama, Japan
TEL
+81457872800
Homepage URL
shunty5323@gmail.com
Sponsor or person
Institute
Yokohama city university school of medicine
Institute
Department
Personal name
Funding Source
Organization
Ministry of Education, Culture, Sports, Science and Technology
Strategic Information and Communications R&D Promotion Programme (SCOPE)
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 institutional ethics committee of the Yokohama City University Hospital
Address
3-9, Fukuura, Kanazawa-ku, Yokohama, Japan
Tel
+81453707627
rinri@yokohama-cu.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
| 2022 | Year | 04 | 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
Enrolling by invitation
Date of protocol fixation
| 2017 | Year | 12 | Month | 28 | Day |
Date of IRB
| 2017 | Year | 11 | Month | 16 | Day |
Anticipated trial start date
| 2018 | Year | 04 | Month | 01 | Day |
Last follow-up date
| 2023 | Year | 03 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
The collection of the data from medical record; patient characterisitics (i.e. height, weight, sex, American society of Anesthesiologists Physical Status, complications [i.e. hyper tension, dibetes, cerebral infarction, dementia, and other neurlogical disease], ope information, and physical findings of head and neck [i.e. intubation, denture, tape, gauze, medical equipment, or tumor].
The facial imformation data is aquired from the bd side medical camera. The sedative or pain scale are evaluted by the nurse who are not related the data analysis. These data are collected at fixed intervals. The data of facial information, sedative or pain scale data, and vitai signs data at the same time are matched. Then these data are used for the machine learning.
The the data pairs are collected at 5 to 10 times per day.
The cohort of the first year is devoted to data collection for constructing an initial prediction model. From the next year, we will randomly divide into cohorts to construct prediction models and cohorts to confirm the validity of prediction models built up to the previous year. We will improve the prediction model accuracy of artificial intelligence using the data obtained every fiscal year and examine data collection and validity in the following year.
Management information
Registered date
| 2018 | Year | 03 | Month | 30 | Day |
Last modified on
| 2019 | Year | 04 | Month | 21 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000035399
