Utility of an AI-based CXR Interpretation Tool in Assisting Diagnostic Accuracy, Speed, and Confidence of Healthcare Professionals: a Study Using 500 Retrospectively Collected Inpatient and Emergency Department CXRs From Two UK Hospital Trusts
Trial ID: NCT06075836
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-10-31
Completion date: 2025-06-01
Sponsor: Oxford University Hospitals NHS Trust
Conditions: Pulmonary Nodules, Solitary; Pulmonary Nodules, Multiple; Pulmonary Consolidation; Pneumothorax; Pneumothorax; Acute; Atelectasis; Pulmonary Calcification; Cardiomegaly; Fibrosis Lung; Pleural Effusion; Pleural Effusions, Chronic; Pneumoperitoneum
Interventions: Cases reading; Ground truthing
Locations: Oxford University Hospitals NHS Foundation Trust
Countries: United Kingdom
Enrollment: 33
Age range: None - None
Sex: ALL

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.

Eligibility 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

Outcomes:
Performance of AI algorithm: sensitivity; Performance of AI algorithm: specificity; Performance of AI algorithm: Area under the ROC Curve (AU ROC); Performance of readers with and without AI assistance: Sensitivity; Performance of readers with and without AI assistance: Specificity; Performance of readers with and without AI assistance: Area under the ROC Curve (AU ROC); Reader speed with vs without AI assistance.