Chest--DiseasesNCT05224479
Clinical Validation of Machine Learning Triage of Chest Radiographs
A study testing Traditional workflow triage, Machine learning workflow triage and Random workflow triage in people with Chest--Diseases, run by Stanford University at 1 site in United States.
Trial summary
Artificial intelligence and machine learning have the potential to transform the practice of radiology, but real-world application of machine learning algorithms in clinical settings has been limited. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Chest--Diseases
- What is being tested?
- Traditional workflow triage, Machine learning workflow triage and Random workflow triage
- Could I receive a placebo?
- Yes, some participants may receive a placebo
- How many people
- 0 participants
- Where
- 1 siteUnited States
- Study dates
- Aug 2022to Nov 2022
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Radiologist at Stanford Hospital and Clinics
Who cannot join
- None
Trial locations and contacts
1 site in 1 country.
About this study
Artificial intelligence and machine learning have the potential to transform the practice of radiology, but real-world application of machine learning algorithms in clinical settings has been limited. An area in which machine learning could be applied to radiology is through the prioritization of unread studies in a radiologist's worklist. This project proposes a framework for integration and clinical validation of a machine learning algorithm that can accurately distinguish between normal and abnormal chest radiographs. Machine learning triage will be compared with traditional methods of study triage in a prospective controlled clinical trial. The investigators hypothesize that machine learning classification and prioritization of studies will result in quicker interpretation of abnormal studies. This has the potential to reduce time to initiation of appropriate clinical management in patients with critical findings. This project aims to provide a thoughtful and reproducible framework for bringing machine learning into clinical practice, potentially benefiting other areas of radiology and medicine more broadly.
- Conditions
- Chest--Diseases
- Being tested
- Traditional workflow triage, Machine learning workflow triage, Random workflow triage
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Traditional workflow triageOther
Workflow triage is based on order location, STAT designation, and first-in-first-out status.
Machine learning workflow triageOther
Workflow triage is based on the machine learning model's confidence of abnormality.
Random workflow triageOther
Workflow triage is based on random order.
| Study group | Role | What they receive |
|---|---|---|
| Traditional workflow triage | Active comparator | Radiologists follow standard triage of chest radiographs. Receives: Other: Traditional workflow triage; Other: Machine learning workflow triage; Other: Random workflow triage |
| Machine learning workflow triage | Active comparator | Radiologists follow machine learning triage of chest radiographs. Receives: Other: Traditional workflow triage; Other: Machine learning workflow triage; Other: Random workflow triage |
| Random workflow triage | Sham comparator | Radiologists follow randomly ordered triage of chest radiographs. Receives: Other: Traditional workflow triage; Other: Machine learning workflow triage; Other: Random workflow triage |
What is being measured
The primary outcome is the main question the study is designed to answer.
Turnaround time
Time from completion of radiograph to time that radiologist issues an assessment via preliminary or final report
Measured: up to 1 hour
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Feb 2022
- Study startAug 2022 (estimated)
- Primary completionNov 2022 (estimated)
- Study completionNov 2022 (estimated)
Why it stopped: Change in research plan
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Chest--DiseasesStudies of the same condition, open and under way.
- More trials run by Stanford UniversityThe sponsor's other registered studies.
- 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
- Crossover
- Model description
- Radiologists will triage chest radiographs using traditional, machine learning, and random methods.
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- Stanford University
- Sponsor type
- University, hospital or other
- Collaborators
- Society of Thoracic Radiology
- Responsible party
- Principal investigator, Emily Tsai, Stanford University
- Investigators
- Emily Tsai, MD, Principal investigator, Stanford University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05224479
- Sponsor's protocol ID
- 47832
- Status last verified
- Oct 2022
- First submitted
- 22 Jan 2022
- Last updated
- 1 Nov 2022
- 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 1 Nov 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Validation of Machine Learning Triage of Chest Radiographs
- ClinicalTrials.gov ID
- NCT05224479
- Acronym / sponsor ID
- 47832
- Conditions
- Chest--Diseases
- Study type
- Interventional
- Recruitment status
- WithdrawnReason stopped: Change in research plan
- Enrolment
- 0 participants (actual)
- Lead sponsor
- Stanford UniversityUniversity, hospital or other
- Collaborators
- Society of Thoracic Radiology
- Study start
- Aug 2022 (estimated)
- Primary completion
- Nov 2022 (estimated)
- Study completion
- Nov 2022 (estimated)
- First posted
- 4 Feb 2022 (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.
Artificial intelligence and machine learning have the potential to transform the practice of radiology, but real-world application of machine learning algorithms in clinical settings has been limited. An area in which machine learning could be applied to radiology is through the prioritization of unread studies in a radiologist's worklist. This project proposes a framework for integration and clinical validation of a machine learning algorithm that can accurately distinguish between normal and abnormal chest radiographs. Machine learning triage will be compared with traditional methods of study triage in a prospective controlled clinical trial. The investigators hypothesize that machine learning classification and prioritization of studies will result in quicker interpretation of abnormal studies. This has the potential to reduce time to initiation of appropriate clinical management in patients with critical findings. This project aims to provide a thoughtful and reproducible framework for bringing machine learning into clinical practice, potentially benefiting other areas of radiology and medicine more broadly.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Single blind (participant)
- Number of arms
- 3
- Model description
- Radiologists will triage chest radiographs using traditional, machine learning, and random methods.
| Arm | Type | Receives |
|---|---|---|
| Traditional workflow triage | Active comparator | Other: Traditional workflow triage, Other: Machine learning workflow triage, Other: Random workflow triageRadiologists follow standard triage of chest radiographs. |
| Machine learning workflow triage | Active comparator | Other: Traditional workflow triage, Other: Machine learning workflow triage, Other: Random workflow triageRadiologists follow machine learning triage of chest radiographs. |
| Random workflow triage | Sham comparator | Other: Traditional workflow triage, Other: Machine learning workflow triage, Other: Random workflow triageRadiologists follow randomly ordered triage of chest radiographs. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Radiologist at Stanford Hospital and Clinics
Key exclusion criteria (1 listed)
- None
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 |
|---|---|---|
| Traditional workflow triageWorkflow triage is based on order location, STAT designation, and first-in-first-out status. | Other | Machine learning workflow triage, Random workflow triage, Traditional workflow triage |
| Machine learning workflow triageWorkflow triage is based on the machine learning model's confidence of abnormality. | Other | Machine learning workflow triage, Random workflow triage, Traditional workflow triage |
| Random workflow triageWorkflow triage is based on random order. | Other | Machine learning workflow triage, Random workflow triage, Traditional workflow triage |
Primary outcome
Turnaround time
Time frame: up to 1 hour
Time from completion of radiograph to time that radiologist issues an assessment via preliminary or final report
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Nov 2022.
- 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.
Emily Tsai, MD principal investigator
Stanford University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Traditional workflow triage, Machine learning workflow triage and Random workflow triage in people with Chest--Diseases, run by Stanford University at 1 site in United States.
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 1 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: withdrawn, as of the sponsor's last update on 1 Nov 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. Some participants in this study may receive a placebo. 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?
The study was withdrawn before enrolling participants, so there are no results.
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?
Turnaround time (time frame: up to 1 hour). 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 1 Nov 2022.