1. Enhanced classification performance using deep learning based segmentation for pulmonary embolism detection in CT angiography
- Author
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Ali Teymur Kahraman, Tomas Fröding, Dimitris Toumpanakis, Christian Jamtheim Gustafsson, and Tobias Sjöblom
- Subjects
Computed tomography pulmonary angiography ,Pulmonary embolism ,nnU-net ,Deep learning ,Science (General) ,Q1-390 ,Social sciences (General) ,H1-99 - Abstract
Purpose: To develop a deep learning-based algorithm that automatically and accurately classifies patients as either having pulmonary emboli or not in CT pulmonary angiography (CTPA) examinations. Materials and methods: For model development, 700 CTPA examinations from 652 patients performed at a single institution were used, of which 149 examinations contained 1497 PE traced by radiologists. The nnU-Net deep learning-based segmentation framework was trained using 5-fold cross-validation. To enhance classification, we applied logical rules based on PE volume and probability thresholds. External model evaluation was performed in 770 and 34 CTPAs from two independent datasets. Results: A total of 1483 CTPA examinations were evaluated. In internal cross-validation and test set, the trained model correctly classified 123 of 128 examinations as positive for PE (sensitivity 96.1 %; 95 % C.I. 91–98 %; P
- Published
- 2024
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