UMIN-CTR Clinical Trial

Recruitment status Enrolling by invitation
Unique ID issued by UMIN UMIN000032012
Receipt No. 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 (Ver. 4)

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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
Last 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
Email shunty5323@gmail.com

Public contact
1st 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
Email shunty5323@gmail.com

Sponsor
Institute Yokohama city university school of medicine
Institute
Department

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
Email 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
URL(English) https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000035399