AI Assisted Detection of Chest X-Rays

Trial Title: AI Assisted Detection of Chest X-Rays

NCT ID: NCT06075836

Condition: Pulmonary Nodules, Solitary
Pulmonary Nodules, Multiple
Pulmonary Consolidation
Pneumothorax
Pneumothorax; Acute
Atelectasis
Pulmonary Calcification
Cardiomegaly
Fibrosis Lung
Pleural Effusion
Pleural Effusions, Chronic
Pneumoperitoneum

Conditions: Official terms:
Multiple Pulmonary Nodules
Pneumoperitoneum
Pleural Effusion
Pulmonary Atelectasis
Pneumothorax
Pulmonary Fibrosis
Solitary Pulmonary Nodule
Cardiomegaly

Conditions: Keywords:
Radiology
Emergency Medicine
Artificial Intelligence
Chest XR
X rays

Study type: Observational

Overall status: Active, not recruiting

Study design:

Time perspective: Retrospective

Intervention:

Intervention type: Other
Intervention name: Cases reading
Description: The reading will be done remotely via the Report and Image Quality Control site (www.RAIQC.com), an online platform allowing medical imaging viewing and reporting. Participants can work from any location, but the work must be done from a computer with internet access. For avoidance of doubt, the work cannot be performed from a phone or tablet. The project is divided into two phases and participants are required to complete both phases. The estimated total involvement in the project is up to 20-24 hours. Phase 1: Time allowed: 2 weeks - Review 500 chest X-rays and express a clinical opinion through a structured reporting template (multiple choice, no open text required). Rest/washout period of 2 weeks. Phase 2 - Time allowed: 2 weeks - Review 500 chest X-rays together with an AI report for each case and express your clinical opinion through the same structured reporting template used in Phase A.
Arm group label: Readers/Participants

Intervention type: Other
Intervention name: Ground truthing
Description: Two consultant thoracic radiologists will independently review the images to establish the 'ground truth' findings on the CXRs, where a consensus is reached this will then be used as the reference standard. In the case of disagreement, a third senior thoracic radiologist's opinion (>20 years experience) will undertake arbitration. A difficulty score will be assigned to each abnormality by the ground truthers using a 4-point Likert scale (1 being easy/obvious to 4 being hard/poorly visualised).
Arm group label: Ground truthers

Summary: This study has been added as a sub study to the Simulation Training for Emergency Department Imaging 2 study (ClinicalTrials.gov ID NCT05427838). The Lunit INSIGHT CXR is a validation study that aims to assess the utility of an Artificial Intelligence-based (AI) chest X-ray (CXR) interpretation tool in assisting the diagnostic accuracy, speed, and confidence of a varied group of healthcare professionals. The study will be conducted using 500 retrospectively collected inpatient and emergency department CXRs from two United Kingdom (UK) hospital trusts. Two fellowship trained thoracic radiologists will independently review all studies to establish the ground truth reference standard. The Lunit INSIGHT CXR tool will be used to analyze each CXR, and its performance will be measured against the expert readers. The study will evaluate the utility of the algorithm in improving reader accuracy and confidence as measured by sensitivity, specificity, positive predictive value, and negative predictive value. The study will measure the performance of the algorithm against ten abnormal findings, including pulmonary nodules/mass, consolidation, pneumothorax, atelectasis, calcification, cardiomegaly, fibrosis, mediastinal widening, pleural effusion, and pneumoperitoneum. The study will involve readers from various clinical professional groups with and without the assistance of Lunit INSIGHT CXR. The study will provide evidence on the impact of AI algorithms in assisting healthcare professionals such as emergency medicine and general medicine physicians who regularly review images in their daily practice.

Criteria for eligibility:

Study pop:
General radiologists/radiographers/physicians reviewing chest X-rays as part of their routine clinical practice, currently working in the National Health Service (NHS).

Sampling method: Non-Probability Sample
Criteria:
Inclusion Criteria: - General radiologists/radiographers/physicians who review CXRs as part of their routine clinical practice Exclusion Criteria: - Thoracic radiologists - Non-radiology physicians with previous formal postgraduate CXR reporting training. - Non-radiology physicians with previous career in radiology, respiratory medicine or thoracic surgery to registrar or consultant level

Gender: All

Minimum age: N/A

Maximum age: N/A

Healthy volunteers: Accepts Healthy Volunteers

Locations:

Facility:
Name: Oxford University Hospitals NHS Foundation Trust

Address:
City: Oxford
Zip: OX3 9DU
Country: United Kingdom

Start date: October 31, 2023

Completion date: June 2025

Lead sponsor:
Agency: Oxford University Hospitals NHS Trust
Agency class: Other

Source: Oxford University Hospitals NHS Trust

Record processing date: ClinicalTrials.gov processed this data on November 12, 2024

Source: ClinicalTrials.gov page: https://clinicaltrials.gov/ct2/show/NCT06075836
https://www.england.nhs.uk/publication/diagnostics-recovery-and-renewal-report-of-the-independent-review-of-diagnostic-services-for-nhs-england/
https://www.rcr.ac.uk/publication/clinical-radiology-uk-workforce-census-2019-report
https://www.nice.org.uk/advice/mib292/chapter/summary