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A Study on the development of image diagnosis technology using artificial intelligence (AI) for Chest X-ray

A Study on the development of image diagnosis technology using artificial intelligence (AI) for Chest X-ray - A Study on the development of image diagnosis technology using artificial intelligence (AI) for Chest X-ray

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
JPRN
Registry ID
JPRN-UMIN000053885
Enrollment
40
Registered
2024-03-18
Start date
2024-03-19
Completion date
Unknown
Last updated
2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

subjects with thoracic diseases

Interventions

None listed

Sponsors

Random Square Co., Ltd
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1) Japanese male and female aged 18 years and older at the time of consent acquisition. 2) An individual with any of the following thoracic diseases. pneumonia, interstitial pneumonia, bronchiolitis obliterans organizing pneumonia (BOOP), pulmonary abscess, pulmonary tuberculosis, atypical mycobacterial infection, pulmonary fungal infection, bronchiectasis, bronchiolitis, lung cancer, metastatic lung tumor, pleural tumor, mediastinal tumor, pulmonary fibrosis, pneumoconiosis, pulmonary alveolar proteinosis, pulmonary embolism, pulmonary arteriovenous fistula, pleurisy, empyema*, pneumothorax, pulmonary emphysema, mediastinal emphysema *Settings to confirm that there are no abnormalities in the chest X-ray image even though it is not a chest disease. 3)Those who have received sufficient explanation about the purpose and content of the study and have signed the consent form before the start of the examination.

Exclusion criteria

Exclusion criteria: Individuals deemed inappropriate by the principal investigator for the examination and the sub investigator.

Design outcomes

Primary

MeasureTime frame
Compare the interpretation results by physicians with the diagnostic outcomes by AI, and validate the proportion of agreement.

Secondary

MeasureTime frame
Compare thoracic diseases diagnosed through patient interviews with those diagnosed by AI, and validate the proportion of agreement.

Countries

Japan

Contacts

Public ContactYoshihiro Otake

feileB Co., Ltd Clinical Research Support dev.

otake@feileb.jp03-4332-1770

Outcome results

None listed

Source: JPRN (via WHO ICTRP) · Data processed: Jul 3, 2026