Clinical trial NCT05117320: Artificial Intelligence to Improve Physicians' Interpretation of Chest X-Rays in Breathless Patients
Official title: Artificial Intelligence to Improve Chest X-ray Reading in Acute Dyspnoeic Patients: A Randomized Controlled Trial
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Dyspnea
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
Lead sponsor: Bispebjerg Hospital
Sponsor class: OTHER
Collaborators: name: Enlitic.com
name: Oxipit.ai
First posted: 2021-11-11
Enrollment: 33
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: CROSSOVER
Intervention 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.
Primary purpose: DIAGNOSTIC

Brief 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. 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.

Eligibility criteria: Inclusion Criteria:

* Medical Doctor (MD)
* Working experience with emergency patients

Exclusion Criteria:

* Current or former employment as a radiologist
* Unwillingness to consent

Primary outcomes:
measure: Accuracy of diagnosing ADHF on acute CXR with vs without AI
timeFrame: 3 months
measure: Accuracy of diagnosing pneumonia on acute CXR with vs without AI
timeFrame: 3 months
Secondary outcomes:
Other outcomes:
Locations:
city: Copenhagen
country: Denmark
facility: University Hospital Bispebjerg and Frederiksberg
Study officials:
References: