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Titel:

Machine learning assisted feature identification and prediction of hemodynamic endpoints using computed tomography in patients with CTEPH.

Dokumenttyp:
Journal Article
Autor(en):
Gawlitza, Joshua; Endres, Sophie; Fries, Peter; Graf, Markus; Wilkens, Heinrike; Stroeder, Jonas; Buecker, Arno; Massmann, Alexander; Ziegelmayer, Sebastian
Abstract:
Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare but potentially curable cause of pulmonary hypertension (PH). Currently PH is diagnosed by right heart catheterisation. Computed tomography (CT) is used for ruling out other causes and operative planning. This study aims to evaluate importance of different quantitative/qualitative imaging features and develop a supervised machine learning (ML) model to predict hemodynamic risk groups. 127 Patients with diagnosed CTEPH who received p...     »
Zeitschriftentitel:
Int J Cardiovasc Imaging
Jahr:
2024
Band / Volume:
40
Heft / Issue:
3
Seitenangaben Beitrag:
569-577
Volltext / DOI:
doi:10.1007/s10554-023-03026-2
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/38143250
Print-ISSN:
1569-5794
TUM Einrichtung:
Institut für Diagnostische und Interventionelle Radiologie (Prof. Makowski)
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