| 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) |
| 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. | |
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| Condition | ||
| Condition | The patients aged 20 years or older who are entered at the intensive care unit. | |
| Classification by specialty |
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| 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 | |
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| Eligibility | ||||
| Age-lower limit |
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| Age-upper limit |
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| 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 |
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| Target sample size | 1400 | |||
| Research contact person | |||||||
| Last name of lead principal investigator |
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| 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 | |||||||
| 1st name of contact person |
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| 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 | |
| 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 |
| 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 | |
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| Date of disclosure of the study information |
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| 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 | |
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| Date of the first journal publication of results | |
| Baseline Characteristics | |
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| Plan to share IPD | |
| IPD sharing Plan description | |
| Progress | |||||||
| Recruitment status | Enrolling by invitation | ||||||
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| 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 | |||||||
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| Link to view the page | |
| URL(English) | https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000035399 |