Leveraging open dataset and transfer learning for accurate recognition of chronic pulmonary embolism from CT angiogram maximum intensity projection images

Autor:	Tuomas Vainio, Teemu Mäkelä, Anssi Arkko, Sauli Savolainen, Marko Kangasniemi
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Artificial intelligence Computed tomography angiography Deep learning Neural networks (computer) Pulmonary embolism Medical physics. Medical radiology. Nuclear medicine R895-920
Zdroj:	European Radiology Experimental, Vol 7, Iss 1, Pp 1-13 (2023)
Druh dokumentu:	article
ISSN:	2509-9280
DOI:	10.1186/s41747-023-00346-9
Popis:	Abstract Background Early diagnosis of the potentially fatal but curable chronic pulmonary embolism (CPE) is challenging. We have developed and investigated a novel convolutional neural network (CNN) model to recognise CPE from CT pulmonary angiograms (CTPA) based on the general vascular morphology in two-dimensional (2D) maximum intensity projection images. Methods A CNN model was trained on a curated subset of a public pulmonary embolism CT dataset (RSPECT) with 755 CTPA studies, including patient-level labels of CPE, acute pulmonary embolism (APE), or no pulmonary embolism. CPE patients with right-to-left-ventricular ratio (RV/LV)
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/7d350180478842e0bde767e32ec86018 Zobrazit plný text záznamu Full text from SpringerLink View record in DOAJ