Respiratory Tract (Lung and Bronchial) Diseases

All Conditions

Symptoms and General Pathology

Neoplasms

Heart and Blood Diseases

Nutritional and Metabolic Diseases

Digestive System Diseases

Rare Diseases

Pleural Effusion

Emergencies

Fibrosis

Pulmonary Atelectasis

Multiple Pulmonary Nodules

Cardiomegaly

Calcinosis

Pneumothorax

Pneumoperitoneum

Pulmonary Fibrosis

Solitary Pulmonary Nodule

Pleural Diseases

Respiratory Tract Diseases

Lung Diseases

Lung Neoplasms

Respiratory Tract Neoplasms

Thoracic Neoplasms

Peritoneal Diseases

Gastrointestinal Diseases

Lung Diseases, Interstitial

Heart Diseases

Hypertrophy

Pathological Conditions, Anatomical

Chronic Graft Versus Host Disease

Acute Graft Versus Host Disease

Reader Selection: 30 readers will be selected from the following five clinical specialty groups:

* emergency medicine (ED)
* adult intensive care (ICU)
* adult general medicine (AGM)
* radiographers (Rad)
* general radiologists

Each specialty group consists of 6 members of ranked seniority. For the physicians this consists of:

* Two 'Juniors' (Foundation Year 1 - Specialty Training 2 years)
* Two 'Middle Grades' (Registrar from Specialty Training 3 to 6 years)
* Two Consultants

For the radiographers, this consists of:

* Two 'Junior/Newly qualified radiographers' (up to 18 months experience post qualification)
* Two 'Mid-experience radiographers' (approx. 3 years' experience)
* Two 'Reporting radiographers' (5+ years' experience)

Two consultant thoracic radiologists. A third senior thoracic radiologist's opinion (\>20 years experience) will undertake arbitration.

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.

Cases reading

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).

Ground truthing

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.

AI Assisted Detection of Chest X-Rays

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

Evaluation of the Lunit INSIGHT CXR algorithm will be performed comparing it to the reference standard in order to determine sensitivity.

Evaluation of the Lunit INSIGHT CXR algorithm will be performed comparing it to the reference standard in order to determine specificity.

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.

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.

Mean time taken to review a scan, with vs without AI assistance.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization	Link
AI Assisted Detection of Chest X-Rays	NCT06075836	Pulmonary Nodul... Pulmonary Nodul... Pulmonary Conso... Pneumothorax Pneumothorax; A... Atelectasis Pulmonary Calci... Cardiomegaly Fibrosis Lung Pleural Effusio... Pleural Effusio... Pneumoperitoneu...	Cases reading Ground truthing	-	Oxford University Hospitals NHS Trust
AI Assisted Detection of Chest X-Rays	NCT06075836	Pulmonary Nodul... Pulmonary Nodul... Pulmonary Conso... Pneumothorax Pneumothorax; A... Atelectasis Pulmonary Calci... Cardiomegaly Fibrosis Lung Pleural Effusio... Pleural Effusio... Pneumoperitoneu...	Cases reading Ground truthing	-	Oxford University Hospitals NHS Trust

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