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.