Pulmonary Nodules, SolitaryAID-CXRNCT06075836
AI Assisted Detection of Chest X-Rays
An observational study of Pulmonary Nodules, Solitary and Pulmonary Nodules, Multiple, run by Oxford University Hospitals NHS Trust at 1 site in United Kingdom.
Trial 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… Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Pulmonary Nodules, Solitary, Pulmonary Nodules, Multiple, Pulmonary Consolidation
- What is being tested?
- Cases reading and Ground truthing
- Type of treatment
- Pill or oral medicine
- How many people
- 33 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 31 Oct 2023to 1 Jun 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
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).
Who may be able to join
- General radiologists/radiographers/physicians who review CXRs as part of their routine clinical practice
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
United Kingdom1 site
Oxford University Hospitals NHS Foundation Trust
Oxford, Oxfordshire, OX3 9DU
About this study
Official title: 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
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
- Being tested
- Cases reading, Ground truthing
- Keywords
- Radiology, Emergency Medicine, Artificial Intelligence, Chest XR, X rays
- Medical subject headings
- Solitary Pulmonary Nodule, Multiple Pulmonary Nodules, Pneumothorax, Pulmonary Atelectasis, Cardiomegaly, Pulmonary Fibrosis, Pleural Effusion, Bronchiolitis Obliterans Syndrome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Cases readingOther
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.
Ground truthingOther
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).
| Study group | Role | What they receive |
|---|---|---|
| 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) Receives: Other: Cases reading | |
| Ground truthers | Two consultant thoracic radiologists. A third senior thoracic radiologist's opinion (>20 years experience) will undertake arbitration. Receives: Other: Ground truthing |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: During 4 weeks of reading time
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Oct 2023
- Study start31 Oct 2023
- Primary completion31 Oct 2024
- Study completion1 Jun 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for Pulmonary Nodules, SolitaryStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 33 participants (actual)
Sponsor and investigators
- Lead sponsor
- Oxford University Hospitals NHS Trust
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Alex Novak, Oxford University Hospitals NHS Trust
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06075836
- Sponsor's protocol ID
- 310995 - B
- Acronym
- AID-CXR
- Status last verified
- Nov 2025
- First submitted
- 4 Oct 2023
- Last updated
- 24 Nov 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Greenhalgh 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.
PubMed 31864722Spiritoso 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.
PubMed 28979441Wilson 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.
PubMed 35728184Jones 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.
PubMed 34169648Ahmad 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.
PubMed 36832231van 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.
PubMed 36171164Kundu 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.
PubMed 34492046Matsumoto 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.
PubMed 32684597Hillis 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.
PubMed 36520432Homayounieh 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.
PubMed 34964851Wu 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.
PubMed 33034642Zech 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.
PubMed 30399157
Study at a glance
From the registry record, last updated by the sponsor on 24 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT06075836
- Acronym / sponsor ID
- AID-CXR / 310995 - B
- Conditions
- Pulmonary Nodules, Solitary, Pulmonary Nodules, Multiple, Pulmonary Consolidation, Pneumothorax, Pneumothorax; Acute, Atelectasis
- Keywords
- Radiology, Emergency Medicine, Artificial Intelligence, Chest XR, X rays
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 33 participants (actual)
- Lead sponsor
- Oxford University Hospitals NHS TrustUniversity, hospital or other
- Study start
- 31 Oct 2023 (actual)
- Primary completion
- 31 Oct 2024 (actual)
- Study completion
- 1 Jun 2025 (actual)
- First posted
- 10 Oct 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Readers/Participants | – | Other: Cases readingReader 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) |
| Ground truthers | – | Other: Ground truthingTwo consultant thoracic radiologists. A third senior thoracic radiologist's opinion (>20 years experience) will undertake arbitration. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- 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).
Key inclusion criteria (1 listed)
- General radiologists/radiographers/physicians who review CXRs as part of their routine clinical practice
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Cases readingThe 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. | Other | Readers/Participants |
| Ground truthingTwo 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). | Other | Ground truthers |
Primary outcome
Performance of AI algorithm: sensitivity
Time frame: 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
Time frame: 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)
Time frame: 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
Time frame: 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
Time frame: 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)
Time frame: 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.
Time frame: During 4 weeks of reading time
Mean time taken to review a scan, with vs without AI assistance.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Nov 2025.
- Background: PubMed 31864722Greenhalgh 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.
- Background: PubMed 28979441Spiritoso 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.
- Background: PubMed 35728184Wilson 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.
- Background: PubMed 34169648Jones 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.
- Background: PubMed 36832231Ahmad 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.
- Background: PubMed 36171164van 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.
- Background: PubMed 34492046Kundu 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.
- Background: PubMed 32684597Matsumoto 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.
- Background: PubMed 36520432Hillis 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.
- Background: PubMed 34964851Homayounieh 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.
- Background: PubMed 33034642Wu 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.
- Background: PubMed 30399157Zech 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.
- Richards M. Diagnostics: Recovery and Renewal - Report of the Independent Review of Diagnostic Services for NHS England. NHS England 2022.Link provided by the sponsor.
- Royal College of Radiologists. Clinical radiology UK workforce census 2019 report. Royal College of Radiologists 2020.Link provided by the sponsor.
- www.nice.org.uk. (2022). Summary \| Artificial intelligence for analysing chest X-ray images \| Advice \| NICE. \[online\] Available at:Link provided by the sponsor.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pulmonary Nodules, Solitary and Pulmonary Nodules, Multiple, run by Oxford University Hospitals NHS Trust at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 24 Nov 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Performance of AI algorithm: sensitivity (time frame: During 4 weeks of reading time). The study lists 7 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06075836, which also lists 12 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Nov 2025.