LAUSR

Pulmonary embolism (PE) is a major cause of morbidity and mortality [1]. Computed tomography pulmonary angiography (CTPA) is the gold standard for diagnosing PE [2] and a common investigation which contributes to potentially avoidable radiation exposure. CTPA use has quadrupled in the past two decades [3], and this has been associated with lower rates of PE detection [4] and possible overdiagnosis [5]. Combining novel machine learning algorithms with extended D-dimer cut-offs may improve pulmonary embolism prediction and reduce patient radiation exposure resulting from avoidable scans https://bit.ly/3oUKd7X