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NCT Number: NCT06597968

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

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.

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This study is active but is not currently recruiting participants.

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Key information

Conditions

Lung Nodule

Age range

18 year–89 year

Sex eligibility

All sexes

Study type

Observational

Primary location

University Hospitals

Cleveland, Ohio, 44106, United States

About this study

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.

Who can participate

Healthy volunteers accepted: No

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

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.

Treatment and study plan

AI Based CAD Software (qXR-Ln)

Device

All x-ray images have already been obtained and will then be run through CAD software for secondary nodule detection

Primary outcomes

  1. number of patients with actionable lung nodule as measured by CT scan

    Time frame: up to one year

  2. total number of patients having chest x-ray

    Time frame: up to one year

  3. number of patients with high risk lung nodule as measured by CT scan

    Time frame: up to one year

  4. total number of patients referred for a CT scan

    Time frame: up to one year

  5. number of lung nodule positive images

    Time frame: up to one year

  6. number of lung nodule negative images

    Time frame: up to one year

Sponsors and collaborators

Lead sponsor

University Hospitals Cleveland Medical Center

Other

Registry information

Official study title

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

Important dates

Study start
2025
Primary completion
2027
Study completion
2027
First posted
Sep 19, 2024
Registry last updated
Jul 14, 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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