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NCT Number: NCT07829640

AI-Assisted Chest CT Interpretation Across the Lung Cancer Care Continuum

This investigator-initiated, single-center study consists of a retrospective artificial intelligence model-development stage and a prospective physician reader-study stage. Deidentified chest CT examinations acquired during routine clinical care between January 1, 2021, and December 31, 2024, will be used to develop, validate, and lock artificial intelligence models for lung cancer-related imaging tasks.

In the prospective stage, approximately 12 to 15 physicians with experience in chest CT interpretation will complete two reading sessions in randomized order: unaided interpretation and AI-assisted interpretation. The sessions will be separated by a washout period of at least 4 weeks. The primary objective is to compare diagnostic performance between AI-assisted and unaided interpretation. Secondary objectives include reading time, diagnostic confidence, inter-reader agreement, and errors related to incorrect AI suggestions.

All readings will be performed in an offline research environment. AI outputs will not be used for patient care, and the study will not add imaging examinations, treatment, or follow-up for patients.

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Key information

Conditions

Lung Cancer Lung Diseases Lung Neoplasms Neoplasms Neoplasms by Site Respiratory Tract Diseases Respiratory Tract Neoplasms Thoracic Neoplasms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, 430022, China

Location contact

Lian Yang, PhD

CONTACT

[email protected]

+86 18986273791

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Physicians with education or training relevant to medical imaging
Experience in interpreting chest CT examinations
Ability to complete the required study training and both reading sessions
Willingness to provide written informed consent and comply with study procedures

Exclusion criteria

Failure to complete the required study training
Inability or unwillingness to complete both reading sessions as required
Any protocol deviation likely to compromise the validity of the reader-study data

Treatment and study plan

Chest CT Artificial Intelligence Decision-Support System

Device

A locked research-use-only artificial intelligence system analyzes deidentified chest CT images and provides decision-support outputs to participating physicians. The system is evaluated only in an offline reader-study environment and is not used for actual patient care.

Primary outcomes

Difference in Diagnostic Accuracy Between AI-Assisted and Unaided Chest CT Interpretation

Time frame: At completion of the second reading session, after a washout period of at least 4 weeks

Diagnostic accuracy will be calculated as the proportion of case-level interpretations that agree with the prespecified reference standard based on pathology or clinical follow-up. The paired difference in diagnostic accuracy between AI-assisted and unaided interpretation will be estimated across participating physicians and cases using a multi-reader multi-case analysis and reported with a 95% confidence interval. Higher accuracy indicates better diagnostic performance.

Study contacts

Contact information is provided by the study sponsor or research team.

Lian Yang, PhD

CONTACT

[email protected]

+86 18986273791

Sponsors and collaborators

Lead sponsor

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

Other

Registry information

Official study title

Development of a Chest CT-Based Artificial Intelligence Model Across the Lung Cancer Care Continuum and a Prospective Randomized Crossover Reader Study of Its Clinical Decision-Support Performance

Important dates

Study start: 2026
Primary completion: 2028
Study completion: 2028
First posted: Sep 21, 2026
Registry last updated: Sep 21, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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