Clinical Validation of Machine Learning Triage of Chest Radiographs

Not Applicable

Withdrawn

• Conditions: Chest--Diseases
• Interventions: Other: Traditional workflow triage; Other: Machine learning workflow triage; Other: Random workflow triage

• Registration Number: NCT05224479
• Lead Sponsor: Stanford University

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.

Detailed Description

Not available

Basic Information
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Recruitment & Eligibility
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Study & Design
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Trial Locations

Study Timeline

• Study First Submitted: 2022-01-22
• Study First Submitted QC: 2022-02-03
• Study First Posted: 2022-02-04
• Study Start: 2022-08
• Status Verified: 2022-10
• Last Update Submitted: 2022-10-29
• Primary Completion: 2022-11
• Study Completion: 2022-11
• Last Update Posted: 2022-11-01

Recruitment & Eligibility

• Status: WITHDRAWN
• Sex: All
• Target Recruitment: Not specified

Inclusion Criteria

• Radiologist at Stanford Hospital and Clinics

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Exclusion Criteria

• None

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Study & Design

• Study Type: INTERVENTIONAL
• Study Design: CROSSOVER

Arm && Interventions

Group	Intervention	Description
Machine learning workflow triage	Traditional workflow triage	Radiologists follow machine learning triage of chest radiographs.
Random workflow triage	Traditional workflow triage	Radiologists follow randomly ordered triage of chest radiographs.
Machine learning workflow triage	Random workflow triage	Radiologists follow machine learning triage of chest radiographs.
Random workflow triage	Random workflow triage	Radiologists follow randomly ordered triage of chest radiographs.
Traditional workflow triage	Random workflow triage	Radiologists follow standard triage of chest radiographs.
Traditional workflow triage	Traditional workflow triage	Radiologists follow standard triage of chest radiographs.
Traditional workflow triage	Machine learning workflow triage	Radiologists follow standard triage of chest radiographs.
Machine learning workflow triage	Machine learning workflow triage	Radiologists follow machine learning triage of chest radiographs.
Random workflow triage	Machine learning workflow triage	Radiologists follow randomly ordered triage of chest radiographs.

Primary Outcome Measures

Name	Time	Method
Turnaround time	up to 1 hour	Time from completion of radiograph to time that radiologist issues an assessment via preliminary or final report

Trial Locations

Locations (1)

Stanford University; 🇺🇸 Stanford, California, United States