Stability and effectiveness of chest CT analysing software using artificial intelligence

Not Applicable

Conditions: Diseases of th respiratory system

Registration Number: KCT0005459

Lead Sponsor: Asan Medical Center

Brief Summary: Not available

Detailed Description: Not available

Recruitment & Eligibility

Status: ot yet recruiting

Sex: All

Target Recruitment: 200

Inclusion Criteria

Patients with confirmed COPD
-Patients with suspected lung cancer and early COPD with a history of smoking

Exclusion Criteria

1) Patients with lung disease with pneumonia, tuberculosis, pneumothorax and pleural effusion
2) Thoracic CT images that cannot be performed with sufficient inhalation or have movement during shooting
3) Patients undergoing lung surgery
4) Video with sequelae of past inflammation involving more than one lung lobe
5) Images with artifacts due to patient movement and inserted medical devices
6) Image with contrast enhancement
7) Images not taken with thin-section (images with slice thickness exceeding 3mm)
8) Image of a minor patient under the age of 19

Study & Design

Study Type: Interventional Study

Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
? Among the subjects classified by the retrospective data, Percentile rank analysis, 200 COPD patients, early COPD suspected patients, and lung cancer suspected patients were selected, and the smoking cessation rate with 200 who showed indicators using software to prospectively recruited subjects is an independent sample t Test the difference between two groups with -test

Secondary Outcome Measures

Name	Time	Method
? Through follow-up observation, 200 prospectively recruited people were classified into two groups: aggravated group and stable group using clinical indicators such as acute exacerbation and dyspnea, and CT indicators obtained with software from each group were divided into independent sample t-tests. Test for differences between groups