Clinical trial NCT06597968: Evaluating the Real World Performance of an AI Based Lung Nodule Detection Tool
Official title: Performance Estimation of Triaging Artificial Intelligence Based Computer-Aided Detection Algorithm in Routine Chest Radiography
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Lung Nodule
Keywords: chest x-ray
CAD software
Lead sponsor: University Hospitals Cleveland Medical Center
Sponsor class: OTHER
First posted: 2024-09-19
Enrollment: 45991
Sex: ALL
Minimum age (years): 18
Maximum age (years): 89
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Chest x-rays collected on patients aged 18-89

Brief summary: 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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: number of patients with actionable lung nodule as measured by CT scan
timeFrame: up to one year
measure: total number of patients having chest x-ray
timeFrame: up to one year
measure: number of patients with high risk lung nodule as measured by CT scan
timeFrame: up to one year
measure: total number of patients referred for a CT scan
timeFrame: up to one year
measure: number of lung nodule positive images
timeFrame: up to one year
measure: number of lung nodule negative images
timeFrame: up to one year
Secondary outcomes:
Other outcomes:
Locations:
city: Cleveland
country: United States
facility: University Hospitals
state: Ohio
Study officials:
affiliation: University Hospitals Cleveland Medical Center
name: Amit Gupta, MD
role: PRINCIPAL_INVESTIGATOR
References: