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Spirometry Interpretation Performance of Primary Care Clinicians With/Without AI Software

NCT05933694 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 228

Last updated 2024-02-16

No results posted yet for this study

Summary

To evaluate whether an artificial intelligence decision support software (ArtiQ.Spiro) improves the diagnostic accuracy of spirometry interpreted by primary care clinicians, as measured by Clinician Diagnostic Accuracy (vs Reference Standard).

Conditions

Interventions

OTHER

Artificial Intelligence-powered Spirometry Interpretation Report

A report generated by artificial intelligence powered software that assessed technical quality of spirometry and estimates the diagnostic probability of six categories: COPD/Asthma/ILD/ Normal/Other obstructive/Other Unidentified

Sponsors & Collaborators

  • National Institute for Health Research, United Kingdom

    collaborator OTHER_GOV

  • Royal Brompton & Harefield NHS Foundation Trust

    lead OTHER

Principal Investigators

  • William Man · Royal Brompton & Harefield Hospitals

Study Design

Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
DOUBLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Max Age
99 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-06-27
Primary Completion
2024-06-27
Completion
2024-09-30

Countries

  • United Kingdom

Study Locations

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Entities

Diseases

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 NCT05933694 on ClinicalTrials.gov