Trial of Artificial Intelligence for Chest Radiography (ACER)

June 11, 2024 updated by: Charlene Liew, Duke-NUS Graduate Medical School

Artificial Intelligence for Chest Radiography: Impact on Economics, Patient Outcomes and Radiology Service Delivery

Randomized Clinical Trial of the impact of Chest radiograph AI-assisted triage and report generation upon clinical outcomes and an economic analysis of impact of AI decision support on radiology service delivery.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Randomized, prospective selection of patients. Control group involves radiologists reporting chest radiographs as per reference standard clinical workflow Intervention group involves radiologists assisted with AI reporting an AI-triaged worklist of chest radiographs using an AI report generation tool Clinical outcomes on patients are studied at pre-determined study endpoints, including time to discharge from the hospital and re-admission rates.

Economic analysis on cost-avoidance from man-hours saved from report generation and triage.

Study Type

Interventional

Enrollment (Estimated)

10000

Phase

  • Not Applicable

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

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

Inclusion Criteria:

  • All patients attending radiography to have chest radiographs during the study period

Exclusion Criteria:

  • age below 14
  • deceased before discharge
  • chest radiograph performed in non-standard projections

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

  • Primary Purpose: Diagnostic
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
No Intervention: Control Arm
Chest radiographs reported with AI assistance
Active Comparator: AI assisted
AI assisted detection, triage and reporting of CXR
Diagnostic test: AI
Artificial intelligence triage and reporting system

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Report generation time
Time Frame: 12 months
Time for radiologist to produce each individual CXR report
12 months
Turnaround Time
Time Frame: 12 months
Time from patient arrival at radiography department to time for clinical team to receive report
12 months
Time to discharge
Time Frame: 12 months
Time from patient arrival at radiography department to time to discharge from hospital
12 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
30-day patient readmission rate
Time Frame: 12 months
Rate of readmission of patient to hospital after discharge within 30 days
12 months

Collaborators and Investigators

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

Sponsor

Duke-NUS Graduate Medical School

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 (Estimated)

October 1, 2024

Primary Completion (Estimated)

September 1, 2025

Study Completion (Estimated)

December 1, 2025

Study Registration Dates

First Submitted

June 4, 2024

First Submitted That Met QC Criteria

June 11, 2024

First Posted (Actual)

June 13, 2024

Study Record Updates

Last Update Posted (Actual)

June 13, 2024

Last Update Submitted That Met QC Criteria

June 11, 2024

Last Verified

June 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2023/2280

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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