DyspneaXRAINCT05117320
Artificial Intelligence to Improve Physicians' Interpretation of Chest X-Rays in Breathless Patients
A study testing AI support in people with Dyspnea, run by Bispebjerg Hospital at 1 site in Denmark.
Trial summary
Identifying the cause of breathlessness in acute patients in the emergency department is critical and challenging. The chest X-ray is central but challenging to read for non-radiologist physicians. Often the physicians read the CXR alone due to off-hours and shortage of radiology specialists. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Dyspnea
- What is being tested?
- AI support
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 33 participants
- Where
- 1 siteDenmark
- Study dates
- 19 Oct 2021to Jul 2022
- Phase
- Interventional
- 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
Who may be able to join
- Medical Doctor (MD)
- Working experience with emergency patients
Who cannot join
- Current or former employment as a radiologist
- Unwillingness to consent
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Artificial Intelligence to Improve Chest X-ray Reading in Acute Dyspnoeic Patients: A Randomized Controlled Trial
Identifying the cause of breathlessness in acute patients in the emergency department is critical and challenging. The chest X-ray is central but challenging to read for non-radiologist physicians. Often the physicians read the CXR alone due to off-hours and shortage of radiology specialists. Artificial Intelligence (AI) has the potential to aid the reading of chest X-rays. The hypothesis is that AI applied to chest X-rays improves emergency physicians' diagnostic accuracy in acute breathless patients.
Read the detailed description
Background:
Acute dyspnoea is a common symptom in the emergency department (ED) but possible differential diagnoses are numerous. The chest X-ray (CXR) is of great importance in distinguishing between these diagnoses and initiating proper treatment but is challenging to interpret for non-radiologist physicians. Radiology departments are confronted with a demand to read a constantly increasing number of acutely performed CXRs, which exceeds the necessary resources. Therefore, in the acute setting, emergency physicians must often read and diagnose the CXR alone. Altogether, there is an unmet need for help with the CXR interpretation in the ED.
Artificial intelligence (AI) software for interpreting CXR has been developed for the detection of pathological findings. In this study, the primary aim is to investigate if AI improves the diagnosis on CXR by non-radiologist physicians in consecutive dyspnoeic patients in the emergency department.
The investigators hypothesize, that AI applied to chest X-rays improves the emergency physicians' diagnostic accuracy in acute dyspnoeic patients. The study has the potential to impact the implementation of AI in clinical practice.
Method:
In a randomized, controlled cross-over study and multi-reader multi-case study, a total of 33 emergency physicians will review CXRs from 231 prospectively collected patients including vital patient information. Each physician will review data from 46 patients. In random order, and on two different days, each CXR is reviewed once with and once without AI-support. Each physician is asked to assess a diagnosis of heart failure, a diagnosis of pneumonia, and whether the CXR is with or without acute remarkable findings. The reference standard is the radiological diagnoses obtained by two independent thorax radiologists blinded to all clinical data.
The physicians report their diagnoses in an online questionnaire based on REDCap®. Information that may affect diagnostic accuracy are also collected, such as level of education and experience with CXR reading, along with questions about how sure the physician feels of their tentative diagnosis. The physicians are asked about their interest in, former experience with and expectations to AI, along with an evaluation of these qualities afterwards.
- Conditions
- Dyspnea
- Being tested
- AI support
- Keywords
- Dyspnea, Dyspnea; Cardiac, Artificial Intelligence, Deep Learning, Emergency Department, Diagnostic, Physicians, Emergency Service, Hospital, X-Rays, Pneumonia, Heart Failure Acute, Diagnostic Accuracy, Multi-reader multi-case (MRMC), Chest X-ray, Randomized
- Medical subject headings
- Dyspnea, Emergencies, Disease, Pneumonia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI supportDevice
Also called Oxipit.ai
Images were allocated to participants. In randomized allocation, one half of the images for each participant are viewed with AI support and the other half is viewed without AI support on the first trial day. On the second trial day the same images are viewed without versus with AI, respectively. This ensures that all images are read twice by the same participant both with and without AI support.
| Study group | Role | What they receive |
|---|---|---|
| AI support | Experimental | Not described Receives: Device: AI support |
| Non-AI support | No intervention | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of diagnosing ADHF on acute CXR with vs without AI
The primary outcome is the difference in diagnostic accuracy of the non-radiologist physicians' diagnosis of ADHF on acute CXR compared with the gold standard. Odds of correct diagnosis are compared using an odds ratio with 95% confidence interval estimated using conditional logistic regression stratified by each image with and without AI. Thus, the improvement in the odds of correct classification after versus before AI support is reported. The significance level is 0.025.
Measured: 3 months
Accuracy of diagnosing pneumonia on acute CXR with vs without AI
The primary outcome is the difference in diagnostic accuracy of the non-radiologist physicians' diagnosis of pneumonia on acute CXR compared with the gold standard. Odds of correct diagnosis are compared using an odds ratio with 95% confidence interval estimated using conditional logistic regression stratified by each image with and without AI. Thus, the improvement in the odds of correct classification after versus before AI support is reported. The significance level is 0.025.
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Nov 2021
- Study start19 Oct 2021
- Primary completionFeb 2022 (estimated)
- Study completionJul 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for DyspneaStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Model description
- In a crossover and multi-reader multi-case study, physicians read CXRs from acute dyspnoic patients. Each physician retrospectively interprets each image twice in two sessions - once with and once without AI-support in random order.The wash-out period was a minimum four weeks. The images were randomly allocated to the physicians via block randomization. Each image was viewed by at least one physician once with and once without AI on trial day 1.
- Masking
- None (open label)
- Enrolment
- 33 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Bispebjerg Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Enlitic.com, Oxipit.ai
- Responsible party
- Principal investigator, Olav Wendelboe Nielsen, Bispebjerg Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05117320
- Sponsor's protocol ID
- FACTUAL-XRAI 1.0
- Acronym
- XRAI
- Status last verified
- Dec 2021
- First submitted
- 1 Nov 2021
- Last updated
- 11 Jan 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 11 Jan 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence to Improve Chest X-ray Reading in Acute Dyspnoeic Patients: A Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT05117320
- Acronym / sponsor ID
- XRAI / FACTUAL-XRAI 1.0
- Conditions
- Dyspnea
- Keywords
- Dyspnea, Dyspnea; Cardiac, Artificial Intelligence, Deep Learning, Emergency Department, Diagnostic, Physicians, Emergency Service, Hospital
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 33 participants (estimated)
- Lead sponsor
- Bispebjerg HospitalUniversity, hospital or other
- Collaborators
- Enlitic.com, Oxipit.ai
- Study start
- 19 Oct 2021 (actual)
- Primary completion
- Feb 2022 (estimated)
- Study completion
- Jul 2022 (estimated)
- First posted
- 11 Nov 2021 (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.
Identifying the cause of breathlessness in acute patients in the emergency department is critical and challenging. The chest X-ray is central but challenging to read for non-radiologist physicians. Often the physicians read the CXR alone due to off-hours and shortage of radiology specialists. Artificial Intelligence (AI) has the potential to aid the reading of chest X-rays. The hypothesis is that AI applied to chest X-rays improves emergency physicians' diagnostic accuracy in acute breathless patients.
Detailed description
Background:
Acute dyspnoea is a common symptom in the emergency department (ED) but possible differential diagnoses are numerous. The chest X-ray (CXR) is of great importance in distinguishing between these diagnoses and initiating proper treatment but is challenging to interpret for non-radiologist physicians. Radiology departments are confronted with a demand to read a constantly increasing number of acutely performed CXRs, which exceeds the necessary resources. Therefore, in the acute setting, emergency physicians must often read and diagnose the CXR alone. Altogether, there is an unmet need for help with the CXR interpretation in the ED.
Artificial intelligence (AI) software for interpreting CXR has been developed for the detection of pathological findings. In this study, the primary aim is to investigate if AI improves the diagnosis on CXR by non-radiologist physicians in consecutive dyspnoeic patients in the emergency department.
The investigators hypothesize, that AI applied to chest X-rays improves the emergency physicians' diagnostic accuracy in acute dyspnoeic patients. The study has the potential to impact the implementation of AI in clinical practice.
Method:
In a randomized, controlled cross-over study and multi-reader multi-case study, a total of 33 emergency physicians will review CXRs from 231 prospectively collected patients including vital patient information. Each physician will review data from 46 patients. In random order, and on two different days, each CXR is reviewed once with and once without AI-support. Each physician is asked to assess a diagnosis of heart failure, a diagnosis of pneumonia, and whether the CXR is with or without acute remarkable findings. The reference standard is the radiological diagnoses obtained by two independent thorax radiologists blinded to all clinical data.
The physicians report their diagnoses in an online questionnaire based on REDCap®. Information that may affect diagnostic accuracy are also collected, such as level of education and experience with CXR reading, along with questions about how sure the physician feels of their tentative diagnosis. The physicians are asked about their interest in, former experience with and expectations to AI, along with an evaluation of these qualities afterwards.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- In a crossover and multi-reader multi-case study, physicians read CXRs from acute dyspnoic patients. Each physician retrospectively interprets each image twice in two sessions - once with and once without AI-support in random order.The wash-out period was a minimum four weeks. The images were randomly allocated to the physicians via block randomization. Each image was viewed by at least one physician once with and once without AI on trial day 1.
| Arm | Type | Receives |
|---|---|---|
| AI support | Experimental | Device: AI support |
| Non-AI support | No intervention | – |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (2 listed)
- Medical Doctor (MD)
- Working experience with emergency patients
Key exclusion criteria (2 listed)
- Current or former employment as a radiologist
- Unwillingness to consent
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 |
|---|---|---|
| AI supportAlso: Oxipit.aiImages were allocated to participants. In randomized allocation, one half of the images for each participant are viewed with AI support and the other half is viewed without AI support on the first trial day. On the second trial day the same images are viewed without versus with AI, respectively. This ensures that all images are read twice by the same participant both with and without AI support. | Device | AI support |
Primary outcome
Accuracy of diagnosing ADHF on acute CXR with vs without AI
Time frame: 3 months
The primary outcome is the difference in diagnostic accuracy of the non-radiologist physicians' diagnosis of ADHF on acute CXR compared with the gold standard. Odds of correct diagnosis are compared using an odds ratio with 95% confidence interval estimated using conditional logistic regression stratified by each image with and without AI. Thus, the improvement in the odds of correct classification after versus before AI support is reported. The significance level is 0.025.
Accuracy of diagnosing pneumonia on acute CXR with vs without AI
Time frame: 3 months
The primary outcome is the difference in diagnostic accuracy of the non-radiologist physicians' diagnosis of pneumonia on acute CXR compared with the gold standard. Odds of correct diagnosis are compared using an odds ratio with 95% confidence interval estimated using conditional logistic regression stratified by each image with and without AI. Thus, the improvement in the odds of correct classification after versus before AI support is reported. The significance level is 0.025.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Jan 2022.
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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?
A study testing AI support in people with Dyspnea, run by Bispebjerg Hospital at 1 site in Denmark.
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 2 inclusion and 2 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: status not confirmed, as of the sponsor's last update on 11 Jan 2022. 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 Denmark. 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?
Results are not available in the source record.
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?
Accuracy of diagnosing ADHF on acute CXR with vs without AI (time frame: 3 months). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 11 Jan 2022.