Dataset Information

Rapid lipid-laden plaque identification in intravascular optical coherence tomography imaging based on time-series deep learning.

ABSTRACT: SignificanceCoronary heart disease has the highest rate of death and morbidity in the Western world. Atherosclerosis is an asymptomatic condition that is considered the primary cause of cardiovascular diseases. The accumulation of low-density lipoprotein triggers an inflammatory process in focal areas of arteries, which leads to the formation of plaques. Lipid-laden plaques containing a necrotic core may eventually rupture, causing heart attack and stroke. Lately, intravascular optical coherence tomography (IV-OCT) imaging has been used for plaque assessment. The interpretation of the IV-OCT images is performed visually, which is burdensome and requires highly trained physicians for accurate plaque identification.AimOur study aims to provide high throughput lipid-laden plaque identification that can assist in vivo imaging by offering faster screening and guided decision making during percutaneous coronary interventions.ApproachAn A-line-wise classification methodology based on time-series deep learning is presented to fulfill this aim. The classifier was trained and validated with a database consisting of IV-OCT images of 98 artery sections. A trained physician with expertise in the analysis of IV-OCT imaging provided the visual evaluation of the database that was used as ground truth for training and validation.ResultsThis method showed an accuracy, sensitivity, and specificity of 89.6%, 83.6%, and 91.1%, respectively. This deep learning methodology has the potential to increase the speed of lipid-laden plaques identification to provide a high throughput of more than 100 B-scans/s.ConclusionsThese encouraging results suggest that this method will allow for high throughput video-rate atherosclerotic plaque assessment through automated tissue characterization for in vivo imaging by providing faster screening to assist in guided decision making during percutaneous coronary interventions.

SUBMITTER: Rico-Jimenez JJ

PROVIDER: S-EPMC9616160 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Rapid lipid-laden plaque identification in intravascular optical coherence tomography imaging based on time-series deep learning.

Rico-Jimenez Jose J JJ Jo Javier A JA

Journal of biomedical optics 20221001 10

<h4>Significance</h4>Coronary heart disease has the highest rate of death and morbidity in the Western world. Atherosclerosis is an asymptomatic condition that is considered the primary cause of cardiovascular diseases. The accumulation of low-density lipoprotein triggers an inflammatory process in focal areas of arteries, which leads to the formation of plaques. Lipid-laden plaques containing a necrotic core may eventually rupture, causing heart attack and stroke. Lately, intravascular optical ...[more]

PMID: 36307914

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Project description:Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.

Project description:BackgroundDiabetes mellitus (DM) or pre-diabetes status is closely associated with features of vulnerable coronary lesions in patients with stable coronary heart disease or acute coronary syndrome. However, the association between duration of diabetes and the morphologies and features of vulnerable plaques has not been fully investigated in patients with acute myocardial infarction (AMI).MethodsWe enrolled a total of 279 patients who presented with AMI between March 2017 and March 2019 and underwent pre-intervention optical coherence tomography imaging of culprit lesions. Patients with DM were divided into two subgroups: a Short-DM group with DM duration of < 10 years and a Long-DM group with DM duration of ≥ 10 years. Baseline clinical data and culprit-plaque characteristics were compared between patients without DM (the non-DM group), those in the Short-DM group, and those in the Long-DM group.ResultsPatients with DM represented 34.1% of the study population (95 patients). The Short- and Long-DM groups included 64 (67.4%) and 31 patients (32.6%), respectively. Glycated hemoglobin A1c (HbA1c) levels were significantly higher in the Long-DM group than the Non- or Short-DM groups (8.4% [Long-DM] versus 5.7% [Non-DM] and 7.6% [Short-DM], P < 0.001). In addition, the highest prevalence of lipid-rich plaques, thin-cap fibroatheroma (TCFA), and plaque ruptures of culprit lesions were observed in the Long-DM group (lipid-rich plaques: 80.6% [Long-DM] versus 52.2% [Non-DM] and 62.5% [Short-DM], P = 0.007; TCFA: 41.9% [Long-DM] versus 19.6% [Non-DM] and 31.3% [Short-DM], P = 0.012; plaque rupture: 74.2% [Long-DM] versus 46.7% [Non-DM] and 48.4% [Short-DM], P = 0.017). The frequency of calcification was significantly higher among patients with DM than among those without (62.1% versus 46.2%, P = 0.016); however, no significant differences were found between the DM subgroups (61.3% [Long-DM] versus 62.5% [Short-DM], P = 0.999).ConclusionsIncreased duration of DM combined with higher HbA1c levels influences culprit-plaque characteristics in patients with DM who suffer AMI. These findings might account for the higher risks of cardiac death in DM patients with long disease duration. Trial registration This study is registered at clinicaltrials.gov as NCT03593928.

Dataset Information

Rapid lipid-laden plaque identification in intravascular optical coherence tomography imaging based on time-series deep learning.

Publications

Rapid lipid-laden plaque identification in intravascular optical coherence tomography imaging based on time-series deep learning.

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