MedTrace Logo

Clinical Validation of Machine Learning Triage of Chest Radiographs

NCT05224479 · Status: WITHDRAWN · Phase: NA · Type: INTERVENTIONAL

Last updated 2022-11-01

No results posted yet for this study

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

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.

Sponsors & Collaborators

Principal Investigators

  • Emily Tsai, MD · Stanford University

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
SINGLE
Model
CROSSOVER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2022-08-31
Primary Completion
2022-11-30
Completion
2022-11-30

Countries

  • United States

Study Locations

More Related Trials

Entities

Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05224479 on ClinicalTrials.gov