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A Study to Assess the Impact of an Artificial Intelligence (AI) System on Chest X-ray Reporting

NCT05489471 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 20000

Last updated 2023-03-31

No results posted yet for this study

Summary

The study has an initial short retrospective component but is predominately a prospective study with two main parts.

Initially during a 1 month period whilst reporters are familiarising themselves with the software two local databases will be reviewed by the AI software:

* A training set of 100 chest X-rays (CXR) some of which contain nodules and is used as a training tool with previously documented radiologist performance.
* A set of previously reported radiographs in patients referred by the reporter for CT, ground truth created from the prior CT report and review by two radiologists if required.

This will allow comparison of stand-alone radiologist and AI performance

This is followed by a 6 month period involving multiple groups of reporters and approximately 20,000 cases looking at the impact of an AI system which assesses 10 abnormalities on chest X-ray and reporting on the sensitivity for detection of lesions and its impact on reporter confidence. Specifically the investigators would look at:

* Missed finding by AI, but detected by reporter
* Correctly detected finding by AI
* Missed finding by the reporter but detected by AI
* Finding detected by AI but disputed by the reporter

■ AI's impact on
* Radiological report
* Further recommended imaging
* Altering patient management
* improvement in report confidence as perceived by reporter

A subsequent 3 month period looking at the impact of AI produced worklists on report turnaround times and the patient pathway from chest X-ray to CT. the investigators would specifically look at:

* number of nodules detected
* number of CXRs recommended for follow up CT
* time taken from CXR to CT
* number of lung cancers detected after CT\[1\]
* Time to report, measured as previously from PACS and reporting software data

The population to be studied will be all patients over 16 years of age referred by their General Practitioner to Hull University Hospitals NHS Trust for a chest radiograph and any chest radiograph performed in the Hull Royal Infirmary ED radiology for patients over 16 years of age during the 6 month study period. The ED department images patients from the emergency department and in-patients within the hospital.

All radiographs will be reviewed initially without review of the AI information and then using the additional images. Reporters will mark the effect of the AI on their decision. All disagreements between the reporter and the AI will be reviewed by senior reporters and a consensus decision made.

Conditions

  • Artificial Intelligence

Interventions

OTHER

Artificial intelligence review

The AI looks for ten different abnormalities on each chest X-ray and produces a heat map and percentage confidence score if it detects an abnormality.

Sponsors & Collaborators

  • Lunit Inc.

    collaborator INDUSTRY

  • Hull University Teaching Hospitals NHS Trust

    lead OTHER_GOV

Principal Investigators

  • Gerard Avery · Hull University Teaching Hospitals NHS Trust

Eligibility

Min Age
16 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-05-31
Primary Completion
2023-07-31
Completion
2023-07-31

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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 NCT05489471 on ClinicalTrials.gov