Radiographic Assessment of CVC Malpositioning: How can AI best support clinicians?

Lasse Hansen, Malte Sieren, Malte Hobe, Axel Saalbach, Heinrich Schulz, Jörg Barkhausen, Mattias P Heinrich

Original language	English
Title of host publication	International Conference on Medical Imaging with Deep Learning
Publication date	01.07.2021
Publication status	Published - 01.07.2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 4 Quality Education
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production
SDG 14 Life Below Water
SDG 15 Life on Land

Research Areas and Centers

Academic Focus: Biomedical Engineering
Centers: Center for Artificial Intelligence Luebeck (ZKIL)

DFG Research Classification Scheme

2.22-30 Radiology
2.22-32 Medical Physics, Biomedical Technology
2.22-07 Medical Informatics and Medical Bioinformatics

Cite this

@inproceedings{9ec4da2911a64e3e8ae1ef3cdf93e6d1,

title = "Radiographic Assessment of CVC Malpositioning: How can AI best support clinicians?",

author = "Lasse Hansen and Malte Sieren and Malte Hobe and Axel Saalbach and Heinrich Schulz and J{\"o}rg Barkhausen and Heinrich, \{Mattias P\}",

year = "2021",

month = jul,

day = "1",

language = "English",

booktitle = "International Conference on Medical Imaging with Deep Learning",

}

TY - GEN

T1 - Radiographic Assessment of CVC Malpositioning: How can AI best support clinicians?

AU - Hansen, Lasse

AU - Sieren, Malte

AU - Hobe, Malte

AU - Saalbach, Axel

AU - Schulz, Heinrich

AU - Barkhausen, Jörg

AU - Heinrich, Mattias P

PY - 2021/7/1

Y1 - 2021/7/1

M3 - Conference contribution

BT - International Conference on Medical Imaging with Deep Learning

ER -