Trial Search Results

Clinical Validation of Machine Learning Triage of Chest Radiographs

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 is currently accepting patients for this trial.

Lead Sponsor:

Stanford University

Collaborator: Society of Thoracic Radiology

Stanford Investigator(s):

Intervention(s):

  • Other: Traditional workflow triage
  • Other: Machine learning workflow triage
  • Other: Random workflow triage

Phase:

N/A

Eligibility


Inclusion Criteria:

   - Radiologist at Stanford Hospital and Clinics

Exclusion Criteria:

   - None

Ages Eligible for Study

18 Years - N/A

Genders Eligible for Study

All

Now accepting new patients

Contact Information

Stanford University
School of Medicine
300 Pasteur Drive
Stanford, CA 94305