An interventional study of Traditional workflow triage and Machine learning workflow triage in Chest--Diseases, sponsored by Stanford University. Withdrawn at 1 site in United States. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2022-11-01.
Sponsored by Stanford University · Not applicable, Interventional, and Diagnostic
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.
Stanford University is the lead sponsor of 2,162 studies on the registry; 429 are open to participants now.
Of its 267 completed or terminated interventional studies of FDA-regulated products, 207 (78%) have results posted.
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Exclusion Criteria:
Radiologists follow standard triage of chest radiographs.
Other: Traditional workflow triage · Other: Machine learning workflow triage · Other: Random workflow triage
Radiologists follow machine learning triage of chest radiographs.
Other: Traditional workflow triage · Other: Machine learning workflow triage · Other: Random workflow triage
Radiologists follow randomly ordered triage of chest radiographs.
Other: Traditional workflow triage · Other: Machine learning workflow triage · Other: Random workflow triage
Workflow triage is based on order location, STAT designation, and first-in-first-out status.
Workflow triage is based on the machine learning model's confidence of abnormality.
Workflow triage is based on random order.
Turnaround time
Time from completion of radiograph to time that radiologist issues an assessment via preliminary or final report
Time frame: up to 1 hour
Plan to share: No
No publications or documents are linked to this record.
This study is withdrawn, as verified in Oct 2022. You cannot join it, but the record below documents what was studied.
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