This paper presents a feasibility study for practical applications of an impedance-based real-time health monitoring technique applying PZT (LeadZirconate-Titanate) patches to concrete structures. First, comparison between experimental and analytical studies for damage detection on a plain concrete beam is made. In the experimental study, progressive surface damage inflicted artificially on the plain concrete beam is assessed by using both lateral and thickness modes of the PZT patches. Then, an analytical study based on finite element (FE) models is carried out to verify the validity of the experimental result. Secondly, multiple (shear and flexural) cracks incurred in a reinforced concrete (RC) beam under a third point bending test are monitored continuously by using a sensor array system composed of the PZT patches. In this study, a root mean square deviation (RMSD) in the impedance signatures of the PZT patches is used as a damage indicator.
Background/Introduction
Healed coronary plaques, morphologically characterized by a layered pattern, are signatures of previous plaque disruption and healing. Recent optical coherence tomography (OCT) studies showed that layered plaque is associated with vascular vulnerability and rapid plaque progression. However, the diagnosis of layered plaque requires expertise in OCT image interpretation and is susceptible to interobserver variability.
Purpose
We aimed to develop a deep learning (DL) model for an accurate diagnosis of layered plaque.
Methods
We developed a Visual Transformer (ViT)-based DL model emulating the cardiologists who review consecutive OCT frames to make a diagnosis (Figure 1), and compared it to the standard convolutional neural network (CNN) model. We used 302,415 cross-sectional OCT images from 873 patients collected from 9 sites: 237,021 images from 581 patients for training and internal validation from 8 sites, and 65394 images from 292 patients collected from another site for external validation.
Results
Model performances were evaluated using the area under the receiver operating characteristics (AUC). In the five-fold cross validation, the ViT-based model showed better performance than the standard CNN-based model with AUC of 0.886 (95% confidence interval [CI], 0.882–0.891) compared with 0.797 (95% CI, 0.790–0.804). The ViT-based model also outperformed the standard CNN-based model in the external validation, with an AUC of 0.857 (95% CI, 0.849–0.864) compared to 0.806 (95% CI, 0.797–0.815) (Figure 2).
Conclusion(s)
The ViT-based DL model will help cardiologists to make an accurate diagnosis of layered plaque, which might help to stratify the risk of future adverse cardiac events.
Funding Acknowledgement
Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Mrs. Gillian Gray through the Allan Gray Fellowship Fund in Cardiology
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.