Dyspnea is one of the most common reasons for presentation to the Emergency Department (ED), comprising nearly 5 million visits in 2020 [1]. There are a multitude of causes for acute dyspnea in ED patients (e.g., heart failure, asthma, chronic obstructive pulmonary disease [COPD], pulmonary embolism). One of the more common etiologies is acute decompensated heart failure, which requires urgent diagnosis and targeted interventions to reduce morbidity and mortality. However, it can be challenging to diagnose this clinically, as patients often have more than one medical condition which can predispose to dyspnea [[2], [3], [4]]. Moreover, many of the common history and physical examination findings have been found to have poor diagnostic utility [5]. Chest radiographs may also be less accurate, with data suggesting that thoracic point-of-care ultrasound (POCUS) may be superior for identifying pulmonary edema [6].

When using POCUS, pulmonary edema is identified by the presence of sonographic B lines. B lines are discrete, beam-like vertical hyperechoic reverberation artifacts arising from the pleural line that extend to the bottom of the ultrasound screen and move synchronously with lung sliding [7]. The presence of three or more B lines in the space between two contiguous ribs are suggestive of pulmonary edema [7]. Identification of these findings can help improve diagnostic accuracy overall and shorten the time to diagnosis by assessing for these while the clinician is at the patient's bedside [6]. This could also be useful in resource-limited settings and to assess responses to therapeutic interventions [8].

Importantly, POCUS is a user-dependent skill that requires structured training and assessment [9,10]. Artificial intelligence (AI) has been increasingly utilized to automate assessments and improve diagnostic accuracy. Prior studies focused on AI for pulmonary edema have primarily been performed by non-physicians or early learners [11,12], while others have been limited by small sample sizes [13]. There are limited data directly comparing the diagnostic accuracy of AI with real-time assessment by trained physicians. This aspect is critical to better understand the potential role in comparison with typical use of B line assessment in practice and how AI would compare.

The primary aim of this study was to directly compare the sensitivity and specificity of lung ultrasound with AI versus a trained physician sonographer performing this in real-time. As a secondary objective, we sought to compare the sensitivity and specificity among patients with a low versus high body mass index (BMI).