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 2023/10/08 19:46:00

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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

Email

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


Email

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

Email

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


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


Date analysis concluded



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

2023 Year 10 Month 08 Day



Link to view the page

Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000050035


Research Plan
Registered date File name

Research case data specifications
Registered date File name

Research case data
Registered date File name