Weakly supervised attention model for RV strain classification from volumetric CTPA scans

Evaluation of the right ventricle (RV) is a key component of the clinical assessment of many cardiovascular and pulmonary disorders. In this work, we focus on RV strain classification from patients who were diagnosed with pulmonary embolism (PE) in computed tomography pulmonary angiography (CTPA) scans. PE is a life-threatening condition, often without warning signs or symptoms. Early diagnosis and accurate risk stratification are critical for decreasing mortality rates. High-risk PE relies on the presence of RV dysfunction resulting from acute pressure overload. PE severity classification and specifically, high-risk PE diagnosis are crucial for appropriate therapy. CTPA is the golden standard in the diagnostic workup of suspected PE. Therefore, it can link between diagnosis and risk stratification strategies.

We retrieved data of consecutive patients who underwent CTPA and were diagnosed with PE and extracted a single binary label of "RV strain biomarker" from the CTPA scan report. This label was used as a weak label for classification. Our solution applies a 3D DenseNet network architecture, further improved by integrating residual attention blocks into the network's layers.

This model achieved an area under the receiver operating characteristic curve (AUC) of 0.88 for classifying RV strain. For Youden's index, 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.

This current solution demonstrates that a small dataset of readily available unmarked CTPAs can be used for effective RV strain classification. To our knowledge, this is the first work that attempts to solve the problem of RV strain classification from CTPA scans and this is the first work where medical images are used in such an architecture. Our generalized self-attention blocks can be incorporated into various existing classification architectures making this a general methodology that can be applied to 3D medical datasets.