Paper Info
Title
Automated detection of pulmonary embolism from CT-angiograms using deep learning
Abstract
The aim of this study was to develop and evaluate a deep neural network model in the automated detection of pulmonary embolism (PE) from computed tomography pulmonary angiograms (CTPAs) using only weakly labelled training data. We developed a deep neural network model consisting of two parts: a convolutional neural network architecture called InceptionResNet V2 and a long-short term memory network to process whole CTPA stacks as sequences of slices. Two versions of the model were created using either chest X-rays (Model A) or natural images (Model B) as pre-training data. We retrospectively collected 600 CTPAs to use in training and validation and 200 CTPAs to use in testing. CTPAs were annotated only with binary labels on both stack- and slice-based levels. Performance of the models was evaluated with ROC and precision–recall curves, specificity, sensitivity, accuracy, as well as positive and negative predictive values. Both models performed well on both stack- and slice-based levels. On the stack-based level, Model A reached specificity and sensitivity of 93.5% and 86.6%, respectively, outperforming Model B slightly (specificity 90.7% and sensitivity 83.5%). However, the difference between their ROC AUC scores was not statistically significant (0.94 vs 0.91, p = 0.07). We show that a deep learning model trained with a relatively small, weakly annotated dataset can achieve excellent performance results in detecting PE from CTPAs.
Year
DOI
Venue
2022
10.1186/s12880-022-00763-z
BMC Medical Imaging
Keywords
DocType
Volume
Artificial intelligence, Emergency radiology, Pulmonary embolism, Deep learning, Automated detection
Journal
22
Issue
ISSN
Citations 
1
1471-2342
1
PageRank 
References 
Authors
0.40
4
6
Name
Order
Citations
PageRank
Heidi Huhtanen110.40
Mikko Nyman210.40
Tarek Mohsen310.40
Arho Virkki410.40
Antti Karlsson510.40
Jussi Hirvonen610.40