PneumoniaACERNCT06456203
Trial of Artificial Intelligence for Chest Radiography
A study testing AI in people with Pneumonia and Lung Cancer, run by Duke-NUS Graduate Medical School.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 14 to 130 yearsHealthy volunteers can take part
- Conditions the study is for
- Pneumonia, Lung Cancer
- What is being tested?
- AI
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 10,000 participants
- Where
- No sites listed yet
- Study dates
- Oct 2024to Dec 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 14 to 130 years
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- All patients attending radiography to have chest radiographs during the study period
Who cannot join
- age below 14
- deceased before discharge
- chest radiograph performed in non-standard projections
About this study
Official title: 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.
Read the 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.
- Conditions
- Pneumonia, Lung Cancer
- Being tested
- AI
- Keywords
- Radiology, artificial intelligence, triage, radiography
- Medical subject headings
- Pneumonia, Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AIDiagnostic test
Artificial intelligence triage and reporting system
| Study group | Role | What they receive |
|---|---|---|
| Control Arm | No intervention | Chest radiographs reported with AI assistance |
| AI assisted | Active comparator | AI assisted detection, triage and reporting of CXR Receives: Diagnostic Test: AI |
What is being measured
The primary outcome is the main question the study is designed to answer.
Report generation time
Time for radiologist to produce each individual CXR report
Measured: 12 months
Turnaround Time
Time from patient arrival at radiography department to time for clinical team to receive report
Measured: 12 months
Time to discharge
Time from patient arrival at radiography department to time to discharge from hospital
Measured: 12 months
Secondary outcomes (1)
30-day patient readmission rate
Rate of readmission of patient to hospital after discharge within 30 days
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Jun 2024
- Study startOct 2024 (estimated)
- Primary completionSep 2025 (estimated)
- Study completionDec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PneumoniaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 10,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Duke-NUS Graduate Medical School
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Charlene Liew, Duke-NUS Graduate Medical School
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06456203
- Sponsor's protocol ID
- 2023/2280
- Acronym
- ACER
- Status last verified
- Jun 2024
- First submitted
- 4 Jun 2024
- Last updated
- 13 Jun 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 Jun 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence for Chest Radiography: Impact on Economics, Patient Outcomes and Radiology Service Delivery
- ClinicalTrials.gov ID
- NCT06456203
- Acronym / sponsor ID
- ACER / 2023/2280
- Conditions
- Pneumonia, Lung Cancer
- Keywords
- Radiology, artificial intelligence, triage, radiography
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Duke-NUS Graduate Medical SchoolUniversity, hospital or other
- Study start
- Oct 2024 (estimated)
- Primary completion
- Sep 2025 (estimated)
- Study completion
- Dec 2025 (estimated)
- First posted
- 13 Jun 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Control Arm | No intervention | –Chest radiographs reported with AI assistance |
| AI assisted | Active comparator | Diagnostic Test: AIAI assisted detection, triage and reporting of CXR |
Who may qualify
- Ages
- 14 to 130 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (1 listed)
- All patients attending radiography to have chest radiographs during the study period
Key exclusion criteria (3 listed)
- age below 14
- deceased before discharge
- chest radiograph performed in non-standard projections
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AIArtificial intelligence triage and reporting system | Diagnostic test | AI assisted |
Primary outcome
Report generation time
Time frame: 12 months
Time for radiologist to produce each individual CXR report
Turnaround Time
Time frame: 12 months
Time from patient arrival at radiography department to time for clinical team to receive report
Time to discharge
Time frame: 12 months
Time from patient arrival at radiography department to time to discharge from hospital
1 secondary outcome
30-day patient readmission rate
Time frame: 12 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Jun 2024.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A study testing AI in people with Pneumonia and Lung Cancer, run by Duke-NUS Graduate Medical School.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 13 Jun 2024. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Report generation time (time frame: 12 months). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Jun 2024.