artificial intelligence to improve physicians' interpretation of chest x-rays in breathless patients

Conditions: Dyspnea

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.

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

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