Automatic Characterisation of Human Atherosclerotic Plaque Composition from Intravascular Optical Coherence Tomography Using Artificial Intelligence

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DOI

  • Miao Chu, Shanghai Jiao Tong Univ, Shanghai Jiao Tong University, Shanghai Peoples Hosp 1, Dept Lab Med
  • ,
  • Haibo Jia
  • ,
  • Juan Luis Gutiérrez-Chico
  • ,
  • Akiko Maehara
  • ,
  • Ziad A Ali
  • ,
  • Xiaoling Zeng
  • ,
  • Luping He
  • ,
  • Chen Zhao
  • ,
  • Mitsuaki Matsumura
  • ,
  • Peng Wu
  • ,
  • Ming Zeng
  • ,
  • Takashi Kubo
  • ,
  • Bo Xu
  • ,
  • Lianglong Chen
  • ,
  • Bo Yu
  • ,
  • Gary S Mintz
  • ,
  • William Wijns
  • ,
  • Niels Ramsing Holm
  • Shengxian Tu

BACKGROUND: Intravascular optical coherence tomography (IVOCT) enables detailed plaque characterisation in-vivo, but visual assessment is time-consuming and subjective.

AIMS: This study aims to develop and validate an automatic framework for IVOCT plaque characterisation using artificial intelligence (AI).

METHODS: IVOCT pullbacks from 5 international centres were analysed in a corelab, annotating basic plaque components, inflammatory markers and other structures. A deep convolutional network with encoding-decoding architecture and pseudo-3D input was developed and trained using hybrid loss. The proposed network was integrated into commercial software to be externally validated on additional IVOCT pullbacks from three international corelabs, taking the consensus among corelabs as reference.

RESULTS: Annotated images from 509 pullbacks (391 patients) were divided into 10,517 and 1,156 cross-sections for the training and testing datasets, respectively. Dice coefficient of the model was 0.906 for fibrous plaque, 0.848 for calcium and 0.772 for lipid in the testing dataset. Excellent agreement in plaque burden quantification was observed between the model and manual measurements (R2=0.98). In the external validation, the software correctly identified 518 out of 598 plaque regions from 300 IVOCT cross-sections, with a diagnostic accuracy of 97.6%[95%CI:93.4%-99.3%] in fibrous plaque, 90.5%[95%CI:85.2%-94.1%] in lipid and 88.5%[95%CI:82.4%-92.7%] in calcium. The median time required for analysis was 21.4 (18.6-25.0) seconds per pullback.

CONCLUSIONS: A novel AI framework for automatic plaque characterisation in IVOCT was developed, providing excellent diagnostic accuracy in both internal and external validation. This model might reduce subjectivity in image interpretation and facilitate IVOCT quantification of plaque composition, with potential applications in research and IVOCT-guided PCI.

OriginalsprogEngelsk
TidsskriftEuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology
ISSN1774-024X
DOI
StatusE-pub ahead of print - 2 feb. 2021

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