Clinical Validation of Machine Learning Triage of Chest Radiographs

Chest--Diseases Clinical Trial

Official title:

Clinical Validation of Machine Learning Triage of Chest Radiographs

Clinical Trial Details — Status: Withdrawn

Administrative data

• NCT number: NCT05224479
• Other study ID #: 47832
• Status: Withdrawn
• Phase: N/A
• Start date: August 2022
• Est. completion date: November 2022

Study information

• Verified date: October 2022
• Source: Stanford University
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Withdrawn
• Enrollment: 0
• Est. completion date: November 2022
• Est. primary completion date: November 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Radiologist at Stanford Hospital and Clinics

Exclusion Criteria:

• None

Related Conditions & MeSH terms

• Chest--Diseases

Intervention

Other:
• Traditional workflow triage
Workflow triage is based on order location, STAT designation, and first-in-first-out status.
• Machine learning workflow triage
Workflow triage is based on the machine learning model's confidence of abnormality.
• Random workflow triage
Workflow triage is based on random order.

Locations

Country	Name	City	State
United States	Stanford University	Stanford	California

Sponsors (2)

Lead Sponsor	Collaborator
Stanford University	Society of Thoracic Radiology

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Turnaround time	Time from completion of radiograph to time that radiologist issues an assessment via preliminary or final report	up to 1 hour