UMIN-ICDS Clinical Trial
| Unique ID issued by UMIN | UMIN000051938 |
|---|---|
| Receipt number | R000059275 |
| Scientific Title | Effective lung protective ventilation strategy using deep learning with graphic monitors : Development of an artificial intelligence prediction model. |
| Date of disclosure of the study information | 2023/08/20 |
| Last modified on | 2023/08/18 14:09:30 |
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Basic information
Public title
Effective lung protective ventilation strategy using deep learning with graphic monitors : Development of an artificial intelligence prediction model.
Acronym
ELPIS-grad STUDY
Scientific Title
Effective lung protective ventilation strategy using deep learning with graphic monitors : Development of an artificial intelligence prediction model.
Scientific Title:Acronym
ELPIS-grad STUDY
Region
| Japan |
Condition
Condition
Ventilated patients admitted to the Yamagata University Hospital Advanced Intensive Care Center
Classification by specialty
| Anesthesiology | Emergency medicine | Intensive care medicine |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
Reading graphic monitors requires considerable clinical experience and a high level of expertise in respiratory physiology.
The main objective of this study is to develop an AI system that predicts the need for interventions such as changing ventilatory settings by creating AI models from graphic monitor images acquired from ventilators of patients undergoing ventilatory therapy in intensive care units.
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Compare the AI system's prediction of the need for setting changes and reasons for setting changes with the actual presence or absence of setting changes and reasons for setting changes by the intensivist. From that comparison, ROC curves are drawn and accuracy, sensitivity, specificity, and AUC are calculated.
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
Ventilated patients admitted to the Advanced Intensive Care Center, Yamagata University Hospital
Key exclusion criteria
None
Target sample size
200
Research contact person
Name of lead principal investigator
| 1st name | Masaki |
| Middle name | |
| Last name | Nakane |
Organization
Yamagata University Hospital
Division name
Department of emergency medicine
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 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
Japan Society for the Promotion of Science
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 | 08 | Month | 20 | 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 | 08 | Month | 18 | Day |
Date of IRB
Anticipated trial start date
| 2023 | Year | 08 | Month | 20 | Day |
Last follow-up date
| 2026 | Year | 03 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
1) To collect clinical data from eligible patients as a prospective study.
Methods: Ventilator graphic image data were obtained. When ventilator mode or settings are changed by an intensivist skilled in respiratory therapy, the setting changes and the reasons for the changes are recorded.
2) Analysis of the captured clinical data and the need for modification.
Method: Using captured graphic images as input values, machine learning is performed to link the presence or absence of ventilatory setting changes and the reasons for the setting changes. The presence or absence of the setting change and the reason for the setting change are used as output values to predict the necessity of the change.
3) System construction
1) Construct an AI prediction model using the collected graphic monitor images.
2) Construct an AI system for predicting changes in ventilatory settings using train data (80% of the total data), and deep learning, transfer learning, and fine tuning using train data.
(3) Verify the accuracy of the constructed system using 20% of the data as test data.
4) Verify the prediction by the AI system and the setting change by the intensivists, and calculate AUC based on accuracy, sensitivity, specificity, and ROC curve.
(5) Visualize the evaluation area of the model with a class activation heat map (Grad-CAM) of the Test data after the AI model is created in order to clarify which area the AI system focuses on for evaluation.
Management information
Registered date
| 2023 | Year | 08 | Month | 18 | Day |
Last modified on
| 2023 | Year | 08 | Month | 18 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/icdr_e/ctr_view.cgi?recptno=R000059275
| Research Plan | |
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| Registered date | File name |
| Research case data | |
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