Clinical trial · Observational
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (12)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Atelectasis | — | UNRESOLVED | — |
| Cardiomegaly | — | UNRESOLVED | — |
| Fibrosis Lung | — | UNRESOLVED | — |
| Pleural Effusion | — | UNRESOLVED | — |
| Pleural Effusions, Chronic | — | UNRESOLVED | — |
| Pneumoperitoneum | — | UNRESOLVED | — |
| Pneumothorax | — | UNRESOLVED | — |
| Pneumothorax; Acute | — | UNRESOLVED | — |
| Pulmonary Calcification | — | UNRESOLVED | — |
| Pulmonary Consolidation |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Cases reading | Other | — | UNRESOLVED |
| Ground truthing | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Readers/Participants
- description
- 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)
- interventionNames
- Other: Cases reading
- label
- Ground truthers
- description
- Two consultant thoracic radiologists. A third senior thoracic radiologist's opinion (\>20 years experience) will undertake arbitration.
- interventionNames
- Other: Ground truthing
Primary outcomes (7)
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
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
References
Publications (12)
- BACKGROUNDGreenhalgh R, Howlett DC, Drinkwater KJ. Royal College of Radiologists national audit evaluating the provision of imaging in the severely injured patient and compliance with national guidelines. Clin Radiol. 2020 Mar;75(3):224-231. doi: 10.1016/j.crad.2019.10.025. Epub 2019 Dec 19. PMID 31864722
- BACKGROUNDSpiritoso R, Padley S, Singh S. Chest X-ray interpretation in UK intensive care units: A survey 2014. J Intensive Care Soc. 2015 Nov;16(4):339-344. doi: 10.1177/1751143715580141. Epub 2015 May 18. PMID 28979441
- BACKGROUNDWilson C. X-ray misinterpretation in urgent care: where does it occur, why does it occur, and does it matter? N Z Med J. 2022 Apr 1;135:49-65. PMID 35728184
- BACKGROUNDJones CM, Buchlak QD, Oakden-Rayner L, Milne M, Seah J, Esmaili N, Hachey B. Chest radiographs and machine learning - Past, present and future. J Med Imaging Radiat Oncol. 2021 Aug;65(5):538-544. doi: 10.1111/1754-9485.13274. Epub 2021 Jun 25. PMID 34169648
- BACKGROUNDAhmad HK, Milne MR, Buchlak QD, Ektas N, Sanderson G, Chamtie H, Karunasena S, Chiang J, Holt X, Tang CHM, Seah JCY, Bottrell G, Esmaili N, Brotchie P, Jones C. Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review. Diagnostics (Basel). 2023 Feb 15;13(4):743. doi: 10.3390/diagnostics13040743. PMID 36832231
- BACKGROUNDvan Beek EJR, Ahn JS, Kim MJ, Murchison JT. Validation study of machine-learning chest radiograph software in primary and emergency medicine. Clin Radiol. 2023 Jan;78(1):1-7. doi: 10.1016/j.crad.2022.08.129. Epub 2022 Sep 25. PMID 36171164
- BACKGROUNDKundu R, Das R, Geem ZW, Han GT, Sarkar R. Pneumonia detection in chest X-ray images using an ensemble of deep learning models. PLoS One. 2021 Sep 7;16(9):e0256630. doi: 10.1371/journal.pone.0256630. eCollection 2021.