  Pulmonary embolus (PE) refers to obstruction of pulmonary arteries by blood
clots. PE accounts for approximately 100,000 deaths per year in the United
States alone. The clinical presentation of PE is often nonspecific, making the
diagnosis challenging. Thus, rapid and accurate risk stratification is of
paramount importance. High-risk PE is caused by right ventricular (RV)
dysfunction from acute pressure overload, which in return can help identify
which patients require more aggressive therapy. Reconstructed four-chamber
views of the heart on chest CT can detect right ventricular enlargement. CT
pulmonary angiography (CTPA) is the golden standard in the diagnostic workup of
suspected PE. Therefore, it can link between diagnosis and risk stratification
strategies. We developed a weakly supervised deep learning algorithm, with an
emphasis on a novel attention mechanism, to automatically classify RV strain on
CTPA. Our method is a 3D DenseNet model with integrated 3D residual attention
blocks. We evaluated our model on a dataset of CTPAs of emergency department
(ED) PE patients. This model achieved an area under the receiver operating
characteristic curve (AUC) of 0.88 for classifying RV strain. The model showed
a sensitivity of 87% and specificity of 83.7%. Our solution outperforms
state-of-the-art 3D CNN networks. The proposed design allows for a fully
automated network that can be trained easily in an end-to-end manner without
requiring computationally intensive and time-consuming preprocessing or
strenuous labeling of the data.We infer that unmarked CTPAs can be used for
effective RV strain classification. This could be used as a second reader,
alerting for high-risk PE patients. To the best of our knowledge, there are no
previous deep learning-based studies that attempted to solve this problem.
