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Koch, V. ; Holmberg, O. ; Blum, E.* ; Sancar, E. ; Aytekin, A.* ; Seguchi, M.* ; Xhepa, E.* ; Wiebe, J.* ; Cassese, S.* ; Kufner, S.* ; Kessler, T.* ; Sager, H.* ; Voll, F.* ; Rheude, T.* ; Lenz, T.* ; Kastrati, A.* ; Schunkert, H.* ; Schnabel, J.A. ; Joner, M.* ; Marr, C. ; Nicol, P.*

Deep learning model DeepNeo predicts neointimal tissue characterization using optical coherence tomography.

Commun. Med. 5:124 (2025)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
BACKGROUND: Accurate interpretation of optical coherence tomography (OCT) pullbacks is critical for assessing vascular healing after percutaneous coronary intervention (PCI). Manual analysis is time-consuming and subjective, highlighting the need for a fully automated solution. METHODS: In this study, 1148 frames from 92 OCT pullbacks were manually annotated to classify neointima as homogeneous, heterogeneous, neoatherosclerosis, or not analyzable on a quadrant level. Stent and lumen contours were annotated in 305 frames for segmentation of the lumen, stent struts, and neointima. We used these annotations to train a deep learning algorithm called DeepNeo. Performance was further evaluated in an animal model (male New Zealand White Rabbits) of neoatherosclerosis using co-registered histopathology images as the gold standard. RESULTS: DeepNeo demonstrates a strong classification performance for neointimal tissue, achieving an overall accuracy of 75%, which is comparable to manual classification accuracy by two clinical experts (75% and 71%). In the animal model of neoatherosclerosis, DeepNeo achieves an accuracy of 87% when compared with histopathological findings. For segmentation tasks in human pullbacks, the algorithm shows strong performance with mean Dice overlap scores of 0.99 for the lumen, 0.66 for stent struts, and 0.86 for neointima. CONCLUSIONS: To the best of our knowledge, DeepNeo is the first deep learning algorithm enabling fully automated segmentation and classification of neointimal tissue with performance comparable to human experts. It could standardize vascular healing assessments after PCI, support therapeutic decisions, and improve risk detection for cardiac events.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Of-the-art; Stent Implantation; Bare-metal; Neoatherosclerosis; Thrombosis; Pathology; Outcomes; Marker
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 2730-664X
e-ISSN 2730-664X
Zeitschrift Communications Medicine
Quellenangaben Band: 5, Heft: 1, Seiten: , Artikelnummer: 124 Supplement: ,
Verlag Springer
Verlagsort Campus, 4 Crinan St, London, N1 9xw, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540007-001
Förderungen Helmholtz Munich
German Cardiac Society
Helmholtz Association
BMBF-funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI)
European Research Council under the European Union
German Research Foundation (DFG)
Heisenberg program
C.M. has received funding from the European Research Council under the European Union's Horizon 2020 research and innovation program (grant agreement number 866411).
DOI 10.1038/s43856-025-00835-5
WOS ID 001469255700004
Scopus ID 105003113203
PubMed ID 40247001
Erfassungsdatum 2025-05-10
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