Artificial Intelligence Used in Screening Adrenal Nodules

NCT07198152 | Recruiting DMC

• 1 other identifier: 2022yan068-002
• Study Type: observational
• Target: 600
• Locations: 1 country
• Sites: 1

Brief Summary

By collecting chest low-dose spiral CT plain scan images, extracting effective features from the adrenal gland regions, training and validating an artificial intelligence-based automatic diagnostic algorithm for adrenal nodules, we will ultimately develop an artificial intelligence software with independent intellectual property rights that is specifically designed for screening adrenal nodules in chest low-dose CT scans.

Conditions

Adrenal Incidentalomas; Thoracic CT-scan

Keywords

automatic segment; automatic classification

Outcome Measures

Primary Outcomes (1)

• Overall Diagnostic Accuracy

  From enrollment to the end of collection in one and a half years

Study Arms (2)

Nodular adrenal glands

Adrenal region segmentation and adrenal nodule identification in chest CT via artificial intelligence

Other: None intervention

Normal adrenal glands

Adrenal region delineation and adrenal gland identification in chest CT using artificial intelligence

Other: None intervention

Interventions

None intervention OTHER

this study is retrospective based on the CT images, which dose include any intervention.

Nodular adrenal glands; Normal adrenal glands

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The study population consists of individuals aged over 18 years, with no gender restrictions, who underwent chest low-dose CT scans as part of routine physical examinations. Notably, those with a history of malignant tumors are excluded, and only individuals with qualified imaging data (free from poor quality, artifacts, or other issues that hinder analysis) are included.

You may qualify if:

• The age of the population is over 18 years old, regardless of gender;
• Images from chest low-dose CT plain scan during routine physical examination;

You may not qualify if:

• Patients with a history of malignant tumors;
• Imaging images with poor quality, artifacts, etc., which make analysis impossible.

Sponsors & Collaborators

• Peking University First Hospital: lead

Study Sites (1)

Peking University First Hospital

Beijing, China

RECRUITING

Related Links

• MR-11-23-010450

MeSH Terms

Conditions

Adrenal incidentaloma

Central Study Contacts

Shiwei Chen

CONTACT

+86 159 1494 4390; 15914944390@163.com

Study Design

• Study Type: observational
• Observational Model: ECOLOGIC OR COMMUNITY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 25, 2025
• First Posted: September 30, 2025
• Study Start: September 1, 2025
• Primary Completion (Estimated): December 30, 2026
• Study Completion (Estimated): January 30, 2027
• Last Updated: September 30, 2025

Record last verified: 2024-09

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)