Deep learning-based plaque characterization in hybrid IVUS-OCT images is superior to single-modality deep learning analysis and human experts: head-to-head comparison against histology.

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Title: Deep learning-based plaque characterization in hybrid IVUS-OCT images is superior to single-modality deep learning analysis and human experts: head-to-head comparison against histology.
Authors: Bajaj R; Department of Cardiology, St Michael's Hospital, Toronto, Canada.; Division of Cardiology, University of Toronto, Toronto, Canada.; Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK., Huang X; School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK., Alves-Kotzev N; Sunnybrook Health Sciences Centre, Toronto, Canada., Weyers JJ; Sunnybrook Health Sciences Centre, Toronto, Canada., Levine M; Department of Cardiology, Medstar Cardiovascular Research Network, Medstar Washington Hospital Center, Washington, DC, USA., Garg M; Department of Cardiology, Medstar Cardiovascular Research Network, Medstar Washington Hospital Center, Washington, DC, USA., Mohamed M; Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK., Maung S; Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK., Parasa R; Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK.; Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK., Çap M; Department of Cardiology, University of Health Sciences, Diyarbakir Gazi Yasargil Education and Research Hospital, Diyarbakir, Turkey., Torii R; Department of Mechanical Engineering, University College London, London, UK., Krams R; School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK., Butany J; Laboratory Medicine and Pathobiology, University of Toronto., Biccirè FG; Department of Cardiology, Bern University Hospital, Bern, Switzerland., Garcia-Garcia H; Department of Cardiology, Medstar Cardiovascular Research Network, Medstar Washington Hospital Center, Washington, DC, USA., Raber L; Department of Cardiology, Bern University Hospital, Bern, Switzerland., Mathur A; Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK.; Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK., Baumbach A; Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK.; Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK., Zhang Q; School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK., Courtney BK; Sunnybrook Health Sciences Centre, Toronto, Canada., Bourantas CV; Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK.; Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK.
Source: Cardiovascular research [Cardiovasc Res] 2026 Mar 16; Vol. 122 (2), pp. 245-257.
Publication Type: Journal Article; Comparative Study
Journal Info: Publisher: Oxford Journals Country of Publication: England NLM ID: 0077427 Publication Model: Print Cited Medium: Internet ISSN: 1755-3245 (Electronic) Linking ISSN: 00086363 NLM ISO Abbreviation: Cardiovasc Res Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1755-3245
DOI:10.1093/cvr/cvaf281