A deep learning methodology for fully-automated quantification of calcific burden in high-resolution intravascular ultrasound images Article Swipe
Xingwei He
,
Mohamed O. Mohamed
,
Nicole Ng
,
Thamil Kumaran
,
Retesh Bajaj
,
Nathan Angelo Lecaros Yap
,
Emrah Erdoğan
,
Mehmet Eren
,
Anthony Mathur
,
Ahmet Emir Ulutaş
,
Bo Gao
,
Yao‐Jun Zhang
,
Andreas Baumbach
,
Jouke Dijkstra
,
Christos V. Bourantas
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7537474/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7537474/v1
Purpose Quantification of the calcific burden is valuable in percutaneous coronary intervention (PCI) planning and in research to assess its changes after pharmacotherapies targeting plaque progression. In intravascular ultrasound (IVUS) images this analysis is currently performed manually and time consuming. To overcome these limitations, we introduce a deep-learning (DL) method for seamless detection of the calcific tissue Methods IVUS images from 197 vessels were analysed by an expert who identified the presence of calcium, and these estimations were used to train a DL model for fast detection of calcific deposits. The output of the model was tested in a set of 30 vessels against the estimations of the two experts. Comparison was performed at a frame-, lesion- and segment level. Results In total 26,211 frames were included in the training and 5,138 in the test set. The estimations of the DL method for the presence of calcium were similar to the experts (kappa 0.842 and 0.848, p < 0.001), while the correlation between the DL approach and the 2 experts for the arc of calcium (0.946 and 0.947, p < 0.001) and calcific area (0.745 and 0.706, p < 0.001) were high. Lesion- (0.971 and 0.990, p < 0.001) and segment-level analysis (0.980 and 0.981, p < 0.001) demonstrated a high correlation between the method and the 2 experts for calcific burden. Conclusion The proposed DL method is able to accurately detect the calcific tissue and quantify its burden. These features render it useful in research and are expected to facilitate its application in the clinical workflows to guide PCI.
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- article
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-7537474/v1
- https://www.researchsquare.com/article/rs-7537474/latest.pdf
- OA Status
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- References
- 38
- OpenAlex ID
- https://openalex.org/W4414188066
All OpenAlex metadata
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https://doi.org/10.21203/rs.3.rs-7537474/v1Digital Object Identifier
- Title
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A deep learning methodology for fully-automated quantification of calcific burden in high-resolution intravascular ultrasound imagesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-09-15Full publication date if available
- Authors
-
Xingwei He, Mohamed O. Mohamed, Nicole Ng, Thamil Kumaran, Retesh Bajaj, Nathan Angelo Lecaros Yap, Emrah Erdoğan, Mehmet Eren, Anthony Mathur, Ahmet Emir Ulutaş, Bo Gao, Yao‐Jun Zhang, Andreas Baumbach, Jouke Dijkstra, Christos V. BourantasList of authors in order
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https://doi.org/10.21203/rs.3.rs-7537474/v1Publisher landing page
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https://www.researchsquare.com/article/rs-7537474/latest.pdfDirect link to full text PDF
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goldOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-7537474/latest.pdfDirect OA link when available
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38Number of works referenced by this work
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