UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000043855 |
|---|---|
| Receipt number | R000050035 |
| Scientific Title | Accuracy of diagnostic-support artificial intelligence interpretation to detect interstitial pneumonia in the medical examination |
| Date of disclosure of the study information | 2021/04/09 |
| Last modified on | 2024/08/28 17:56:01 |
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Basic information
Public title
Accuracy of diagnostic-support artificial intelligence interpretation to detect interstitial pneumonia in the medical examination
Acronym
Accuracy of diagnostic-support artificial intelligence interpretation to detect interstitial pneumonia in the medical examination
Scientific Title
Accuracy of diagnostic-support artificial intelligence interpretation to detect interstitial pneumonia in the medical examination
Scientific Title:Acronym
Accuracy of diagnostic-support artificial intelligence interpretation to detect interstitial pneumonia in the medical examination
Region
| Japan |
Condition
Condition
Interstitial lung disease
Classification by specialty
| Medicine in general | Pneumology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
We previously developed the artificial intelligence engine to detect interstitial lung diseases on the chest X-ray image. Although we validated the detection ability of the AI engine with dataset of 200 chest X-ray images in the previous study, the number of images was not enough. Moreover, more than half of the images were derived from patients with interstitial lung diseases and this prevalence was completely different from the real world. By having the AI engine interpret the chest X-rays of the examinees who visit the medical examination center, we can evaluate the accuracy of the AI engine to detect interstitial lung diseases and estimate prevalence of interstitial lung diseases in the real world.
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
The sensitivity, specificity, positive predictive value and negative predictive value of the AI engine to detect interstitial lung diseases in the medical health check up examination.
Key secondary outcomes
The estimated prevalence of interstitial lung diseases in this cohort.
The comparison of the detection ability between the AI engine and human doctors.
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
| 50 | years-old | <= |
Age-upper limit
| 100 | years-old | >= |
Gender
Male and Female
Key inclusion criteria
Subjects aged >=50 years who visit Sapporo Fukujyuji Medical Health Center or Hokkaido Cancer Society Medical Health Center or Shin-yurigaoka General Hospital for the medical health check up from the approval day by the president to Dec 31, 2022.
Key exclusion criteria
Subjects who refuse written consent and do not undergo chest X-ray and/or blood examination. To maintain anonymization, elderly subjects aged >100 years, and those with very rares disease (i.e., <= 10 subjects in the database) will also be excluded.
Target sample size
3770
Research contact person
Name of lead principal investigator
| 1st name | Hirofumi |
| Middle name | |
| Last name | Chiba |
Organization
Sapporo Medical University, School of Medicine
Division name
Department of Respiratory Medicine and Allergology
Zip code
060-8543
Address
S1-W16, Chuo-ku, Sapporo, Hokkaido
TEL
011-611-2111
hchiba@sapmed.ac.jp
Public contact
Name of contact person
| 1st name | Hirotaka |
| Middle name | |
| Last name | Nishikiori |
Organization
Sapporo Medical University, School of Medicine
Division name
Department of Respiratory Medicine and Allergology
Zip code
060-8543
Address
S1-W16, Chuo-ku, Sapporo, Hokkaido
TEL
011-611-2111
Homepage URL
hnishiki@sapmed.ac.jp
Sponsor or person
Institute
Sapporo Medical University
Institute
Department
Personal name
Funding Source
Organization
Nippon Boehringer Ingelheim Co ., Ltd.
Organization
Division
Category of Funding Organization
Profit organization
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Sapporo Medical University
Address
S1-W16, Chuo-ku, Sapporo, Hokkaido
Tel
011-611-2111
rinri@sapmed.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
北海道結核予防会 札幌複十字総合健診センター（北海道）
北海道対がん協会札幌がん検診センター（北海道）
医療法人社団三成会新百合ヶ丘総合病院（神奈川県）
Other administrative information
Date of disclosure of the study information
| 2021 | Year | 04 | Month | 09 | 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
2805
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
No longer recruiting
Date of protocol fixation
| 2020 | Year | 10 | Month | 28 | Day |
Date of IRB
| 2020 | Year | 10 | Month | 28 | Day |
Anticipated trial start date
| 2021 | Year | 06 | Month | 29 | Day |
Last follow-up date
| 2024 | Year | 01 | Month | 31 | Day |
Date of closure to data entry
| 2024 | Year | 01 | Month | 31 | Day |
Date trial data considered complete
| 2024 | Year | 04 | Month | 30 | Day |
Date analysis concluded
| 2024 | Year | 06 | Month | 30 | Day |
Other
Other related information
We gather the data of the chest X-ray images and serum SP-D/KL-6 levels, gender, age, pre-existing illness, smoking history, etc. of examinees in the medical health examination. If examinees are indicated abnormalities on the chest X-ray images and/or high SP-D/KL-6 levels, they are recommended to go to the referral hospital for more detailed examination. We interpret the chest CT images taken at these hospitals. With the result of the CT interpretation as the correct answer, we make AI engine interpret the chest X-ray images at the first medical examination and evaluate the detectability of interstitial shadow of the AI engine. We estimate the actual prevalence of ILD from the number of images that AI engine judged to have interstitial lung shadows.
Management information
Registered date
| 2021 | Year | 04 | Month | 06 | Day |
Last modified on
| 2024 | Year | 08 | Month | 28 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000050035
