  With more than 60,000 deaths annually in the United States, Pulmonary
Embolism (PE) is among the most fatal cardiovascular diseases. It is caused by
an artery blockage in the lung; confirming its presence is time-consuming and
is prone to over-diagnosis. The utilization of automated PE detection systems
is critical for diagnostic accuracy and efficiency. In this study we propose a
two-stage attention-based CNN-LSTM network for predicting PE, its associated
type (chronic, acute) and corresponding location (leftsided, rightsided or
central) on computed tomography (CT) examinations. We trained our model on the
largest available public Computed Tomography Pulmonary Angiogram PE dataset
(RSNA-STR Pulmonary Embolism CT (RSPECT) Dataset, N=7279 CT studies) and tested
it on an in-house curated dataset of N=106 studies. Our framework mirrors the
radiologic diagnostic process via a multi-slice approach so that the accuracy
and pathologic sequela of true pulmonary emboli may be meticulously assessed,
enabling physicians to better appraise the morbidity of a PE when present. Our
proposed method outperformed a baseline CNN classifier and a single-stage
CNN-LSTM network, achieving an AUC of 0.95 on the test set for detecting the
presence of PE in the study.
