Not provided
Not provided
Not provided
Not provided
Not provided
Change in research plan
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| Society of Thoracic Radiology | UNKNOWN |
Not provided
Not provided
Not provided
Not provided
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.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Traditional workflow triage | Active Comparator | Radiologists follow standard triage of chest radiographs. |
|
| Machine learning workflow triage | Active Comparator | Radiologists follow machine learning triage of chest radiographs. |
|
| Random workflow triage | Sham Comparator | Radiologists follow randomly ordered triage of chest radiographs. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Traditional workflow triage | Other | Workflow triage is based on order location, STAT designation, and first-in-first-out status. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Turnaround time | Time from completion of radiograph to time that radiologist issues an assessment via preliminary or final report | up to 1 hour |
Not provided
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Affiliation | Role |
|---|---|---|
| Emily Tsai, MD | Stanford University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Stanford University | Stanford | California | 94305 | United States |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Radiologists will triage chest radiographs using traditional, machine learning, and random methods.
Not provided
Not provided
Radiologists will be blinded when using machine learning and random triage methods.
| Machine learning workflow triage | Other | Workflow triage is based on the machine learning model's confidence of abnormality. |
|
| Random workflow triage | Other | Workflow triage is based on random order. |
|