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xrAI - Improving Quality and Efficiency in Chest Radiograph Interpretation

NCT04153045 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 28

Last updated 2020-09-22

No results posted yet for this study

Summary

xrAI (pronounced "X-ray") serves as a clinical assistance tool for trained clinical professionals who are interpreting chest radiographs. The tool is designed as a quality control and adjunct, limited, clinical decision support tool, and does not replace the role of clinical professionals. It highlights areas on chest radiographs for review by an interpreting clinician.

The objective of this study is to utilize machine learning and artificial intelligence algorithms (xrAI) to improve the quality and efficiency in the interpretation of chest radiographs by family doctors, nurse practitioners, emergency medicine physicians, internists, pulmonologists, and radiologists.

The hypothesis is that the addition of xrAI's analysis will reduce inter-observer variability in the interpretation of chest radiographs and increase participants' sensitivity, recall, and accuracy in pulmonary abnormality screening.

Conditions

  • Pulmonary Disease

Interventions

DEVICE

Radiograph interpretation for pulmonary abnormalities

The pulmonary abnormalities detected by xrAI and included in the definition of abnormality are as follows: any linear scar or fibrosis, atelectasis, consolidation, abscess or cavity, nodule, pleural effusion, severe cases of emphysema and COPD (mild cases with hyperinflation but not significant emphysema are not flagged), pneumothorax. Participants in the treatment group will interpret 500 images presented alongside the results of xrAI's processing in a dark room and asked to categorize each image into one of the following categories: lungs are clear, at least one pulmonary abnormality is present, not sure. Participants in the control group will be asked to interpret the same 500 images as the treatment group but without xrAI's analysis.

Sponsors & Collaborators

  • Saskatchewan Health Authority - Regina Area

    collaborator OTHER

  • 1QB Information Technologies Inc.

    lead INDUSTRY

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
SINGLE
Model
PARALLEL

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2020-02-12
Primary Completion
2020-04-21
Completion
2020-04-21

Countries

  • Canada

Study Locations

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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 NCT04153045 on ClinicalTrials.gov