2024
DOI: 10.1101/2024.06.14.23300272
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

DeepNeo: Deep Learning for neointimal tissue characterization using optical coherence tomography

Valentin Koch,
Olle Holmberg,
Edna Blum
et al.

Abstract: AimsThis study aimed to develop a deep-learning algorithm to enable a fully-automated analysis and interpretation of optical coherence tomography (OCT) pull-backs from patients after percutaneous coronary intervention (PCI).Methods and resultsIn 1148 frames from 92 OCTs, neointima was manually classified as homogeneous, heterogenous, neoatherosclerosis, or not analyzable at quadrant level by an experienced expert. Additionally, stent and lumen contours were annotated in 90 frames to enable segmentation of lume… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 43 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?