PuSH - Publikationsserver des Helmholtz Zentrums München

Holmberg, O. ; Lenz, T.* ; Koch, V. ; Alyagoob, A.* ; Utsch, L.* ; Rank, A.* ; Sabic, E.* ; Seguchi, M.* ; Xhepa, E.* ; Kufner, S.* ; Cassese, S.* ; Kastrati, A.* ; Marr, C. ; Joner, M.* ; Nicol, P.*

Histopathology-based deep-learning predicts atherosclerotic lesions in intravascular imaging.

Front. Cardiovasc. Med. 8:779807 (2021)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Background: Optical coherence tomography is a powerful modality to assess atherosclerotic lesions, but detecting lesions in high-resolution OCT is challenging and requires expert knowledge. Deep-learning algorithms can be used to automatically identify atherosclerotic lesions, facilitating identification of patients at risk. We trained a deep-learning algorithm (DeepAD) with co-registered, annotated histopathology to predict atherosclerotic lesions in optical coherence tomography (OCT). Methods: Two datasets were used for training DeepAD: (i) a histopathology data set from 7 autopsy cases with 62 OCT frames and co-registered histopathology for high quality manual annotation and (ii) a clinical data set from 51 patients with 222 OCT frames in which manual annotations were based on clinical expertise only. A U-net based deep convolutional neural network (CNN) ensemble was employed as an atherosclerotic lesion prediction algorithm. Results were analyzed using intersection over union (IOU) for segmentation. Results: DeepAD showed good performance regarding the prediction of atherosclerotic lesions, with a median IOU of 0.68 ± 0.18 for segmentation of atherosclerotic lesions. Detection of calcified lesions yielded an IOU = 0.34. When training the algorithm without histopathology-based annotations, a performance drop of >0.25 IOU was observed. The practical application of DeepAD was evaluated retrospectively in a clinical cohort (n = 11 cases), showing high sensitivity as well as specificity and similar performance when compared to manual expert analysis. Conclusion: Automated detection of atherosclerotic lesions in OCT is improved using a histopathology-based deep-learning algorithm, allowing accurate detection in the clinical setting. An automated decision-support tool based on DeepAD could help in risk prediction and guide interventional treatment decisions.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Artificial Intelligence ; Atherosclerosis ; Deep Learning ; Histopathology ; Intravascular Imaging ; Optical Coherence Tomography; Optical Coherence Tomography; Plaque Characterization; Coronary; Quantification; Disease
ISSN (print) / ISBN 2297-055X
e-ISSN 2297-055X
Zeitschrift Frontiers in Cardiovascular Medicine
Quellenangaben Band: 8, Heft: , Seiten: , Artikelnummer: 779807 Supplement: ,
Verlag Frontiers
Verlagsort Lausanne
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Institute of AI for Health (AIH)
Förderungen European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme
German Cardiac Society