NCT05224479

Clinical Validation of Machine Learning Triage of Chest Radiographs

• Status: Withdrawn
• Phase: N/A
• Study type: Interventional
• Enrollment: 0 (actual)
• Sponsor: Stanford University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

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.

Conditions

• Chest--Diseases

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2022-08-01
• Primary completion: 2022-11-01
• Completion: 2022-11-01
• First posted: 2022-02-04
• Last updated: 2022-11-01

Locations

1 site across 1 country: United States