Pulmonary Nodules, Solitary, Pulmonary Nodules, Multiple, Pulmonary Consolidation, Pneumothorax, Pneumothorax; Acute, Atelectasis, Pulmonary Calcification, Cardiomegaly, Fibrosis Lung, Pleural Effusion, Pleural Effusions, Chronic, Pneumoperitoneum
Conditions
Keywords
Radiology, Emergency Medicine, Artificial Intelligence, Chest XR, X rays
Brief 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.
Interventions
OTHERCases reading
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.
OTHERGround truthing
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).
Sponsors
Oxford University Hospitals NHS Trust
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Performance of AI algorithm: sensitivity | During 4 weeks of reading time | Evaluation of the Lunit INSIGHT CXR algorithm will be performed comparing it to the reference standard in order to determine sensitivity. |
| Performance of AI algorithm: specificity | During 4 weeks of reading time | Evaluation of the Lunit INSIGHT CXR algorithm will be performed comparing it to the reference standard in order to determine specificity. |
| Performance of AI algorithm: Area under the ROC Curve (AU ROC) | During 4 weeks of reading time | Evaluation of the Lunit INSIGHT CXR algorithm will be performed comparing it to the reference standard. Continuous probability score from the algorithm will be utilized for the ROC analyses, while binary classification results with a predefined operating cut-off will be used for evaluation of sensitivity, specificity, positive predictive value, and negative predictive value. |
| Performance of readers with and without AI assistance: Sensitivity | During 4 weeks of reading time | The study will include two sessions (with and without AI overlay), with all 30 readers reviewing all 500 CXR cases each time separated by a washout period to mitigate recall bias. The cases will be randomised between the two reads and for every reader. |
| Performance of readers with and without AI assistance: Specificity | During 4 weeks of reading time | The study will include two sessions (with and without AI overlay), with all 30 readers reviewing all 500 CXR cases each time separated by a washout period to mitigate recall bias. The cases will be randomised between the two reads and for every reader. |
| Performance of readers with and without AI assistance: Area under the ROC Curve (AU ROC) | During 4 weeks of reading time | The study will include two sessions (with and without AI overlay), with all 30 readers reviewing all 500 CXR cases each time separated by a washout period to mitigate recall bias. The cases will be randomised between the two reads and for every reader. |
| Reader speed with vs without AI assistance. | During 4 weeks of reading time | Mean time taken to review a scan, with vs without AI assistance. |
Countries
United Kingdom
Outcome results
None listed