eprintid: 35269
rev_number: 10
eprint_status: archive
userid: 4613
dir: disk0/00/03/52/69
datestamp: 2024-08-06 10:14:40
lastmod: 2024-08-06 10:14:40
status_changed: 2024-08-06 10:14:40
type: HD.PhdThesisAb
metadata_visibility: show
creators_name: Nageler, Gregor
title: Deep learning-based assessment of internal carotid artery anatomy to predict difficult intracranial access in endovascular recanalization of acute ischemic stroke
subjects: ddc-610
divisions: i-911100
adv_faculty: af-05
keywords: Neurologie
date: 2024
own_urn: urn:nbn:de:bsz:16-heidok-352696
date_accepted: 2024-03-07
advisor: HASH(0x55a9a63475f8)
language: ger
bibsort: NAGELERGREDEEPLEARNI2024
full_text_status: public
citation:   Nageler, Gregor  (2024) Deep learning-based assessment of internal carotid artery anatomy to predict difficult intracranial access in endovascular recanalization of acute ischemic stroke.  [Abstract of a medical dissertation]     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/35269/1/Nageler_Gregor.pdf