Detecting Pulmonary Embolism from Computed Tomography Using Convolutional Neural Network

TL;DR

This paper proposes a deep learning-based convolutional neural network system to detect pulmonary embolism from chest CT images, enabling faster diagnosis and reducing the need for invasive angiography.

Contribution

It introduces a novel CNN approach for PE detection directly from CT images, facilitating immediate diagnosis during initial imaging.

Findings

Rapid PE detection from CT images

Reduces time to diagnosis by over a week

Potential to decrease unnecessary angiography

Abstract

The clinical symptoms of pulmonary embolism (PE) are very diverse and non-specific, which makes it difficult to diagnose. In addition, pulmonary embolism has multiple triggers and is one of the major causes of vascular death. Therefore, if it can be detected and treated quickly, it can significantly reduce the risk of death in hospitalized patients. In the detection process, the cost of computed tomography pulmonary angiography (CTPA) is high, and angiography requires the injection of contrast agents, which increase the risk of damage to the patient. Therefore, this study will use a deep learning approach to detect pulmonary embolism in all patients who take a CT image of the chest using a convolutional neural network. With the proposed pulmonary embolism detection system, we can detect the possibility of pulmonary embolism at the same time as the patient's first CT image, and schedule the CTPA test immediately, saving more than a week of CT image screening time and providing timely diagnosis and treatment to the patient.