Application of deep learning techniques in non-contrast computed tomography pulmonary angiogram for pulmonary embolism diagnosis

TL;DR

This paper presents a deep learning model that accurately detects pulmonary embolism in non-contrast CT images, potentially enabling faster diagnosis without the risks associated with contrast media.

Contribution

It introduces a 3D convolutional neural network for classifying pulmonary embolism in non-contrast CT scans, demonstrating high accuracy and feasibility.

Findings

85% classification accuracy

0.84 AUC score

Feasibility of non-contrast CT diagnosis

Abstract

Pulmonary embolism is a life-threatening disease, early detection and treatment can significantly reduce mortality. In recent years, many studies have been using deep learning in the diagnosis of pulmonary embolism with contrast medium computed tomography pulmonary angiography, but the contrast medium is likely to cause acute kidney injury in patients with pulmonary embolism and chronic kidney disease, and the contrast medium takes time to work, patients with acute pulmonary embolism may miss the golden treatment time. This study aims to use deep learning techniques to automatically classify pulmonary embolism in CT images without contrast medium by using a 3D convolutional neural network model. The deep learning model used in this study had a significant impact on the pulmonary embolism classification of computed tomography images without contrast with 85\% accuracy and 0.84 AUC, which confirms the feasibility of the model in the diagnosis of pulmonary embolism.