Dataset Information

Radial Wall Strain Assessment From AI-Assisted Angiography: Feasibility and Agreement With OCT as Reference Standard.

ABSTRACT:

Background

High-strain spots in coronary arteries are associated with plaque vulnerability and predict future events. Artificial intelligence currently enables the calculation of radial wall strain (RWS) from coronary angiography (RWS_Angio). This study aimed to determine the agreement between novel RWS_Angio and RWS derived from optical coherence tomography (OCT) followed by finite element analysis, as the established reference standard (RWS_OCT).

Methods

All lesions from a previous OCT study were enrolled. OCT was automatically coregistered with angiography. RWS_Angio was computed as the relative luminal deformation throughout the cardiac cycle, whereas RWS_OCT was analyzed using finite element analysis on OCT cross-sections at 1-mm intervals. The luminal deformation in the direction of minimal lumen diameter was used to derive RWS_OCT, using the same definition as RWS_Angio. The maximal RWS_OCT and RWS_Angio at healthy segments adjacent to the interrogated lesion were also analyzed.

Results

Finite element analysis was performed in 578 OCT cross-sections from 45 lesions stemming from 36 patients. RWS_Angio showed good correlation and agreement with RWS_OCT (r = 0.91; P < .001; Lin coefficient = 0.85). RWS_Angio in atherosclerotic segments was significantly higher than that in healthy segments (12.6% [11.0, 16.0] vs 4.5% [2.9, 5.5], P < .001). The intraclass correlation coefficients for intra- and interobserver variability in repeated RWS_Angio analysis were 0.92 (95% CI, 0.87-0.95) and 0.88 (95% CI, 0.81-0.92), respectively. The mean analysis time of RWS_OCT and RWS_Angio for each lesion was 95.0 ± 41.1 and 0.9 ± 0.1 minutes, respectively.

Conclusions

Radial wall strain from coronary angiography can be rapidly and easily computed solely from angiography, showing excellent agreement with strain derived from coregistered OCT. This novel and simple method might provide a cost-effective biomechanical assessment in large populations.

SUBMITTER: Huang J

PROVIDER: S-EPMC11307920 | biostudies-literature | 2023 Mar-Apr

REPOSITORIES: biostudies-literature

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Publications

Radial Wall Strain Assessment From AI-Assisted Angiography: Feasibility and Agreement With OCT as Reference Standard.

Huang Jiayue J Tu Shengxian S Li Chunming C Hong Huihong H Wang Zhiqing Z Chen Lianglong L Gutiérrez-Chico Juan Luis JL Wijns William W

Journal of the Society for Cardiovascular Angiography & Interventions 20221223 2

<h4>Background</h4>High-strain spots in coronary arteries are associated with plaque vulnerability and predict future events. Artificial intelligence currently enables the calculation of radial wall strain (RWS) from coronary angiography (RWS<sub>Angio</sub>). This study aimed to determine the agreement between novel RWS<sub>Angio</sub> and RWS derived from optical coherence tomography (OCT) followed by finite element analysis, as the established reference standard (RWS<sub>OCT</sub>).<h4>Method ...[more]

PMID: 39129795

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Dataset Information

Radial Wall Strain Assessment From AI-Assisted Angiography: Feasibility and Agreement With OCT as Reference Standard.

Background

Methods

Results

Conclusions

Publications

Radial Wall Strain Assessment From AI-Assisted Angiography: Feasibility and Agreement With OCT as Reference Standard.

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