Clinical Validation of Machine Learning Triage of Chest Radiographs
Public ClinicalTrials.gov record NCT05224479. Field values are reproduced from the official study page; the official ClinicalTrials.gov record remains the source of truth for eligibility, enrollment, and contact information.
Data is sourced from official ClinicalTrials.gov public API records. Always review the official ClinicalTrials.gov record for the latest information.
Brief summary
Reproduced verbatim from the official ClinicalTrials.gov record. Not medical advice.
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 identification
- NCT ID
- NCT05224479
- Recruitment status
- Withdrawn
- Study type
- Interventional
- Phase
- Not applicable
- Enrollment
- Not listed
Conditions and interventions
Conditions
Interventions
- Traditional workflow triage Other
- Machine learning workflow triage Other
- Random workflow triage Other
Other
Eligibility (public fields only)
- Age range
- 18 Years and older
- Sex
- All
- Healthy volunteers
- Accepts healthy volunteers
This page does not interpret eligibility. Detailed inclusion and exclusion criteria are on the official ClinicalTrials.gov record.
Study timeline
- Start date
- Jul 31, 2022
- Primary completion
- Oct 31, 2022
- Completion
- Oct 31, 2022
- Last update posted
- Oct 31, 2022
2022
United States locations
- U.S. sites
- 1
- U.S. states
- 1
- U.S. cities
- 1
| Facility | City | State | ZIP | Site status |
|---|---|---|---|---|
| Stanford University | Stanford | California | 94305 | — |
Site contact phone numbers, emails, and investigator names are intentionally not displayed here. Open the official ClinicalTrials.gov record for site contact information.
About this trial record page
- What this page shows
- Public field values for ClinicalTrials.gov record NCT05224479, including study identification, conditions, interventions, eligibility (age, sex, healthy volunteer), timeline, and U.S. site list.
- What this page does not do
- No medical advice, eligibility judgments, treatment recommendations, study quality scoring, or AI-generated medical summaries. No site contact phone numbers, emails, or investigator names.
- Where the data comes from
- Sourced from the official ClinicalTrials.gov public API. The official record is the source of truth.
- Last refresh
- Last update posted Oct 31, 2022 · Synced Sep 11, 2026
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Open the official record
The complete protocol, eligibility criteria, and contact information for NCT05224479 live on ClinicalTrials.gov.