Clinical Validation of Machine Learning Triage of Chest Radiographs
Study Details
Study Description
Brief 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. 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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
N/A |
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Active Comparator: Traditional workflow triage Radiologists follow standard triage of chest radiographs. |
Other: Traditional workflow triage
Workflow triage is based on order location, STAT designation, and first-in-first-out status.
Other: Machine learning workflow triage
Workflow triage is based on the machine learning model's confidence of abnormality.
Other: Random workflow triage
Workflow triage is based on random order.
|
| Active Comparator: Machine learning workflow triage Radiologists follow machine learning triage of chest radiographs. |
Other: Traditional workflow triage
Workflow triage is based on order location, STAT designation, and first-in-first-out status.
Other: Machine learning workflow triage
Workflow triage is based on the machine learning model's confidence of abnormality.
Other: Random workflow triage
Workflow triage is based on random order.
|
| Sham Comparator: Random workflow triage Radiologists follow randomly ordered triage of chest radiographs. |
Other: Traditional workflow triage
Workflow triage is based on order location, STAT designation, and first-in-first-out status.
Other: Machine learning workflow triage
Workflow triage is based on the machine learning model's confidence of abnormality.
Other: Random workflow triage
Workflow triage is based on random order.
|
Outcome Measures
Primary Outcome Measures
- Turnaround time [up to 1 hour]
Time from completion of radiograph to time that radiologist issues an assessment via preliminary or final report
Eligibility Criteria
Criteria
Inclusion Criteria:
- Radiologist at Stanford Hospital and Clinics
Exclusion Criteria:
- None
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Stanford University | Stanford | California | United States | 94305 |
Sponsors and Collaborators
- Stanford University
- Society of Thoracic Radiology
Investigators
- Principal Investigator: Emily Tsai, MD, Stanford University
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- 47832