AI Assisted Detection of Chest X-Rays

NCT06075836 | Completed

• 1 other identifier: 310995 - B
• Study Type: observational
• Target: 33
• Locations: 1 country
• Sites: 1

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.

Conditions

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

Keywords

Radiology; Emergency Medicine; Artificial Intelligence; Chest XR; X rays

Outcome Measures

Primary Outcomes (7)

• Performance of AI algorithm: sensitivity

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

  During 4 weeks of reading time

• Performance of AI algorithm: specificity

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

  During 4 weeks of reading time

• Performance of AI algorithm: Area under the ROC Curve (AU ROC)

  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.

  During 4 weeks of reading time

• Performance of readers with and without AI assistance: Sensitivity

  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.

  During 4 weeks of reading time

• Performance of readers with and without AI assistance: Specificity

  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.

  During 4 weeks of reading time

• Performance of readers with and without AI assistance: Area under the ROC Curve (AU ROC)

  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.

  During 4 weeks of reading time

• Reader speed with vs without AI assistance.

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

  During 4 weeks of reading time

Study Arms (2)

Readers/Participants

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)

Other: Cases reading

Ground truthers

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

Other: Ground truthing

Interventions

Cases reading OTHER

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.

Readers/Participants

Ground truthing OTHER

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 truthers

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

• General radiologists/radiographers/physicians who review CXRs as part of their routine clinical practice

You may not qualify if:

• 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

Sponsors & Collaborators

• Oxford University Hospitals NHS Trust: lead

Study Sites (1)

Oxford University Hospitals NHS Foundation Trust

Oxford, Oxfordshire, OX3 9DU, United Kingdom

Location

Related Publications (12)

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• Spiritoso 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.

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• Jones 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.

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• Ahmad 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 BACKGROUND

• van 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 BACKGROUND

• Kundu 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.

  PMID: 34492046 BACKGROUND

• Matsumoto T, Kodera S, Shinohara H, Ieki H, Yamaguchi T, Higashikuni Y, Kiyosue A, Ito K, Ando J, Takimoto E, Akazawa H, Morita H, Komuro I. Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning. Int Heart J. 2020 Jul 30;61(4):781-786. doi: 10.1536/ihj.19-714. Epub 2020 Jul 18.

  PMID: 32684597 BACKGROUND

• Hillis JM, Bizzo BC, Mercaldo S, Chin JK, Newbury-Chaet I, Digumarthy SR, Gilman MD, Muse VV, Bottrell G, Seah JCY, Jones CM, Kalra MK, Dreyer KJ. Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs. JAMA Netw Open. 2022 Dec 1;5(12):e2247172. doi: 10.1001/jamanetworkopen.2022.47172.

  PMID: 36520432 BACKGROUND

• Homayounieh F, Digumarthy S, Ebrahimian S, Rueckel J, Hoppe BF, Sabel BO, Conjeti S, Ridder K, Sistermanns M, Wang L, Preuhs A, Ghesu F, Mansoor A, Moghbel M, Botwin A, Singh R, Cartmell S, Patti J, Huemmer C, Fieselmann A, Joerger C, Mirshahzadeh N, Muse V, Kalra M. An Artificial Intelligence-Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study. JAMA Netw Open. 2021 Dec 1;4(12):e2141096. doi: 10.1001/jamanetworkopen.2021.41096.

  PMID: 34964851 BACKGROUND

• Wu JT, Wong KCL, Gur Y, Ansari N, Karargyris A, Sharma A, Morris M, Saboury B, Ahmad H, Boyko O, Syed A, Jadhav A, Wang H, Pillai A, Kashyap S, Moradi M, Syeda-Mahmood T. Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents. JAMA Netw Open. 2020 Oct 1;3(10):e2022779. doi: 10.1001/jamanetworkopen.2020.22779.

  PMID: 33034642 BACKGROUND

• Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS Med. 2018 Nov 6;15(11):e1002683. doi: 10.1371/journal.pmed.1002683. eCollection 2018 Nov.

  PMID: 30399157 BACKGROUND

Related Links

• Richards M. Diagnostics: Recovery and Renewal - Report of the Independent Review of Diagnostic Services for NHS England. NHS England 2022.
• Royal College of Radiologists. Clinical radiology UK workforce census 2019 report. Royal College of Radiologists 2020.
• www.nice.org.uk. (2022). Summary \| Artificial intelligence for analysing chest X-ray images \| Advice \| NICE. \[online\] Available at:

MeSH Terms

Conditions

Solitary Pulmonary Nodule; Multiple Pulmonary Nodules; Pneumothorax; Pulmonary Atelectasis; Cardiomegaly; Pulmonary Fibrosis; Pleural Effusion; Bronchiolitis Obliterans Syndrome; Pneumoperitoneum

Condition Hierarchy (Ancestors)

Lung Diseases; Respiratory Tract Diseases; Lung Neoplasms; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Pleural Diseases; Heart Diseases; Cardiovascular Diseases; Hypertrophy; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms; Lung Diseases, Interstitial; Fibrosis; Pathologic Processes; Organizing Pneumonia; Bronchiolitis Obliterans; Bronchiolitis; Bronchitis; Bronchial Diseases; Lung Diseases, Obstructive; Graft vs Host Disease; Immune System Diseases; Peritoneal Diseases; Digestive System Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Primary Investigator

Study Record Dates

• First Submitted: October 4, 2023
• First Posted: October 10, 2023
• Study Start: October 31, 2023
• Primary Completion: October 31, 2024
• Study Completion: June 1, 2025
• Last Updated: November 24, 2025

Record last verified: 2025-11

Locations

GB (1)