Academic Journal
Enhanced classification performance using deep learning based segmentation for pulmonary embolism detection in CT angiography
| Title: | Enhanced classification performance using deep learning based segmentation for pulmonary embolism detection in CT angiography |
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| Authors: | Ali Teymur Kahraman, Tomas Fröding, Dimitris Toumpanakis, Christian Jamtheim Gustafsson, Tobias Sjöblom |
| Source: | Heliyon, Vol 10, Iss 19, Pp e38118- (2024) |
| Publisher Information: | Elsevier, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Science (General) LCC:Social sciences (General) |
| Subject Terms: | Computed tomography pulmonary angiography, Pulmonary embolism, nnU-net, Deep learning, Science (General), Q1-390, Social sciences (General), H1-99 |
| More Details: | 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 |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2405-8440 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2405844024141497; https://doaj.org/toc/2405-8440 |
| DOI: | 10.1016/j.heliyon.2024.e38118 |
| Access URL: | https://doaj.org/article/c9ffb4fe863a4b02a0d975e59e9d7d0a |
| Accession Number: | edsdoj.9ffb4fe863a4b02a0d975e59e9d7d0a |
| Database: | Directory of Open Access Journals |
| ISSN: | 24058440 |
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| DOI: | 10.1016/j.heliyon.2024.e38118 |
| Published in: | Heliyon |
| Language: | English |