Evaluating the Real World Performance of an AI Based Lung Nodule Detection Tool

January 27, 2026 updated by: Amit Gupta, MD, University Hospitals Cleveland Medical Center

Performance Estimation of Triaging Artificial Intelligence Based Computer-Aided Detection Algorithm in Routine Chest Radiography

chest x-rays will be analyzed by AI software for a secondary read of lung nodules. Chest x-rays will either be sent to the AI tool to be read or to radiologists to read. If the image is sent to the AI tool, the AI software will generate a report on if it detects a lung nodule or not. The image will then be sent to a radiologist to determine if there is agreement or disagreement with the AI tool.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

The study is a prospective study for measuring the performance of an AI software in detecting lung nodules from chest X-rays. Data collected during the study will be analyzed for study purposes after end date of data collection.

There will be two study arms: the control arm and the interventional arm.

Control Arm:

There will be no interruption to the existing standard of care pathway.

Interventional Arm:

Use of AI will occur in parallel to the standard of care pathway.

Consistent with the control Arm, the radiologists or clinicians interpreting the chest x-ray images will proceed as usual based on the existing standard operating procedures of the study site. In addition, the AI software will function as a second reader; meaning images will be processed by the AI software which will generate a report.

In the event that the radiologist and the AI tool do not agree, cases will be reviewed by qualified study team members twice per week.

Study Type

Observational

Enrollment (Estimated)

45991

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

Study Population

Chest x-rays collected on patients aged 18-89

Description

Inclusion Criteria:

  • Chest X-ray images of patients aged 18 - 89 years.
  • Modality: CR/DR/DX.
  • PA/view
  • Lung nodules measuring 6 mm -30 mm (for chest X-ray images where presence of nodules is required).

Exclusion Criteria:

  • Incomplete view of the chest.
  • Lateral view
  • Known lung cancer at the time of Chest x-ray images.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Image going through AI tool
these are the images going through the AI tool
Device: AI Based CAD Software (qXR-Ln)
All x-ray images have already been obtained and will then be run through CAD software for secondary nodule detection
image not going through AI tool
these are the images not going through the AI tool

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
number of patients with actionable lung nodule as measured by CT scan
Time Frame: up to one year
up to one year
total number of patients having chest x-ray
Time Frame: up to one year
up to one year
number of patients with high risk lung nodule as measured by CT scan
Time Frame: up to one year
up to one year
total number of patients referred for a CT scan
Time Frame: up to one year
up to one year
number of lung nodule positive images
Time Frame: up to one year
up to one year
number of lung nodule negative images
Time Frame: up to one year
up to one year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University Hospitals Cleveland Medical Center

Investigators

  • Principal Investigator: Amit Gupta, MD, University Hospitals Cleveland Medical Center

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

June 24, 2025

Primary Completion (Estimated)

April 30, 2026

Study Completion (Estimated)

April 30, 2026

Study Registration Dates

First Submitted

September 12, 2024

First Submitted That Met QC Criteria

September 12, 2024

First Posted (Actual)

September 19, 2024

Study Record Updates

Last Update Posted (Actual)

January 29, 2026

Last Update Submitted That Met QC Criteria

January 27, 2026

Last Verified

January 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • STUDY20240362

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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