Deep Learning-Based Approach to Automatically Assess Coronary Distensibility Following Kawasaki Disease.

Kawasaki disease is an acute vasculitis affecting children, which can lead to coronary artery (CA) aneurysms. Optical coherence tomography (OCT) has identified CA wall damage in KD patients, but it is unclear if these findings correlate with any distensibility changes in the CA and how these changes evolve over time. This paper seeks to establish the link between OCT findings and vessel distensibility with a novel deep learning coronary artery segmentation system and use the segmentation framework to automatically analyze the temporal evolution of coronary stiffness over many years. 27 KD patients underwent catheterization with coronary angiography of the left coronary artery (LCA), followed by OCT of proximal and distal segments of the LCA. Changes in the CA caliber over the cardiac cycle were measured automatically and compared against OCT findings suggestive of KD-related vascular damage. In addition, 34 KD patients with regressed or persistent CA aneurysms were followed with serial CA angiography over an average of 14.5 years. Distensibility changes were calculated using a deep learning coronary artery segmentation framework and evaluated longitudinally. Distensibility in the coronary arteries after KD negatively correlated with increasing severity of OCT findings of KD-related vessel damage. KD patients have a significant increase in CA wall stiffness at 1 year after diagnosis, which then plateaus subsequently, compared to controls. Also, patients with persistent CA aneurysms have a statistically significant increase in wall stiffness over time in comparison to those with regressed CA aneurysms. Distensibility changes in the CA of KD patients calculated using our automated deep learning approach correlates with the severity of OCT findings of KD-related CA damage. This decreased distensibility peaks at 1 year in KD patients when following longitudinally and is more severe in those with persistent CA aneurysms.

Intravascular imaging of coronary artery: Bridging the gap between clinical needs and technical advances.

Coronary artery disease is the leading cause of mortality worldwide. Almost seven million deaths are reported each year due to coronary disease. Coronary artery events in the adult are primarily due to atherosclerosis with seventy-five percent of the related mortality caused by plaque rupture. Despite significant progress made to improve intravascular imaging of coronary arteries, there is still a large gap between clinical needs and technical developments. The goal of this review is to identify the gap elements between clinical knowledge and recent advances in the domain of medical image analysis. Efficient image analysis computational models should be designed with respect to the exact clinical needs, and detailed features of the tissues under review. In this review, we discuss the detailed clinical features of the intracoronary plaques for mathematical and biomedical researchers. We emphasize the importance of integrating this clinical knowledge validated by clinicians to investigate the potentially effective models for proper features efficiency in the scope of leveraging the state-of-the-art of coronary image analyses.

A deep learning-based model for characterization of atherosclerotic plaque in coronary arteries using optical coherence tomography images.

PURPOSE: Coronary artery events are mainly associated with atherosclerosis in adult population, which is recognized as accumulation of plaques in arterial wall tissues. Optical Coherence Tomography (OCT) is a light-based imaging system used in cardiology to analyze intracoronary tissue layers and pathological formations including plaque accumulation. This state-of-the-art catheter-based imaging system provides intracoronary cross-sectional images with high resolution of 10-15 µm. But interpretation of the acquired images is operator dependent, which is not only very time-consuming but also highly error prone from one observer to another. An automatic and accurate coronary plaque tagging using OCT image post-processing can contribute to wide adoption of the OCT system and reducing the diagnostic error rate. METHOD: In this study, we propose a combination of spatial pyramid pooling module with dilated convolutions for semantic segmentation to extract atherosclerotic tissues regardless of their types and training a sparse auto-encoder to reconstruct the input features and enlarge the training data as well as plaque type characterization in OCT images. RESULTS: The results demonstrate high precision of the proposed model with reduced computational complexity, which can be appropriate for real-time analysis of OCT images. At each step of the work, measured accuracy, sensitivity, specificity of more than 93% demonstrate high performance of the model. CONCLUSION: The main focus of this study is atherosclerotic tissue characterization using OCT imaging. This contributes to wide adoption of the OCT imaging system by providing clinicians with a fully automatic interpretation of various atherosclerotic tissues. Future studies will be focused on analyzing atherosclerotic vulnerable plaques, those coronary plaques which are prone to rupture.

An automatic diagnostic system of coronary artery lesions in Kawasaki disease using intravascular optical coherence tomography imaging.

Intravascular optical coherence tomography (IV-OCT) is a light-based imaging modality with high resolution, which employs near-infrared light to provide tomographic intracoronary images. Morbidity caused by coronary heart disease is a substantial cause of acute coronary syndrome and sudden cardiac death. The most common intracoronay complications caused by coronary artery disease are intimal hyperplasia, calcification, fibrosis, neovascularization and macrophage accumulation, which require efficient prevention strategies. OCT can provide discriminative information of the intracoronary tissues, which can be used to train a robust fully automatic tissue characterization model based on deep learning. In this study, we aimed to design a diagnostic model of coronary artery lesions. Particularly, we trained a random forest using convolutional neural network features to distinguish between normal and diseased arterial wall structure. Then, based on the arterial wall structure, fully convolutional network is designed to extract the tissue layers in normal cases, and pathological tissues regardless of lesion type in pathological cases. Then, the type of the lesions can be characterized with high precision using our previous model. The results demonstrate the robustness of the model with the approximate overall accuracy up to 90%.

Characterization of coronary artery pathological formations from OCT imaging using deep learning.

Coronary artery disease is the number one health hazard leading to the pathological formations in coronary artery tissues. In severe cases, they can lead to myocardial infarction and sudden death. Optical Coherence Tomography (OCT) is an interferometric imaging modality, which has been recently used in cardiology to characterize coronary artery tissues providing high resolution ranging from 10 to 20 µm. In this study, we investigate different deep learning models for robust tissue characterization to learn the various intracoronary pathological formations caused by Kawasaki disease (KD) from OCT imaging. The experiments are performed on 33 retrospective cases comprising of pullbacks of intracoronary cross-sectional images obtained from different pediatric patients with KD. Our approach evaluates deep features computed from three different pre-trained convolutional networks. Then, a majority voting approach is applied to provide the final classification result. The results demonstrate high values of accuracy, sensitivity, and specificity for each tissue (up to 0.99 ± 0.01). Hence, deep learning models and especially, majority voting method are robust for automatic interpretation of the OCT images.

Difference Between Persistent Aneurysm, Regressed Aneurysm, and Coronary Dilation in Kawasaki Disease: An Optical Coherence Tomography Study.

BACKGROUND: Coronary artery (CA) aneurysms are a serious complication of Kawasaki disease (KD). Conventional imaging techniques often described segments with regressed aneurysms as normal, whereas studies have shown significant endothelial dysfunction. METHODS: KD patients with aneurysms scheduled for routine coronary angiography underwent optical coherence tomography (OCT) imaging between 2013 and 2016. Microstructural coronary changes were compared between normal CA segments and those with dilation, regressed aneurysms, and persistent aneurysms. RESULTS: OCT was performed on 33 patients aged 12.0 ± 5.4 years, 8.5 ± 5.4 years after KD diagnosis. Of the 79 segments analyzed, 25 had persistent aneurysms, 22 regressed aneurysms, 11 CA dilation, and 21 no CA involvement. Intimal thickness was 489 ± 173 µm, 304 ± 158 µm, 102 ± 68 µm, and 63 ± 29 µm, respectively (P < 0.001). There was a linear correlation between the maximum aneurysm size and the intimal thickness, as well as coronary dimension at the time of OCT. Fibrosis (54 segments, 68%) and cellular infiltration (22 segments, 28%) were found more often in segments with CA involvement, but also those without (P = 0.01; P = 0.02). Destruction of the media (34 segments, 43%), calcifications (6 segments, 8%), neovascularization (18 segments, 23%), and white thrombi (8 segments, 10%) were found almost exclusively in segments with a history of aneurysms. CONCLUSIONS: Intimal hyperplasia, fibrosis, and cellular infiltration were found in all categories of CA involvement, whereas calcification, destruction of the media, neovascularization, and white thrombi were found essentially only in segments with saccular or fusiform aneurysms. Prospective studies with outcome correlations are needed to see if this is associated with an increased risk of late adverse events.

Coronary Wall Structural Changes in Patients With Kawasaki Disease: New Insights From Optical Coherence Tomography (OCT).

BACKGROUND: Coronary artery aneurysms (CAA) are serious complications of Kawasaki disease (KD). Optical coherence tomography (OCT) is a high-resolution intracoronary imaging modality that characterizes coronary artery wall structure. The purpose of this work was to describe CAA wall sequelae after KD. METHODS AND RESULTS: KD patients scheduled for routine coronary angiography underwent OCT imaging between March 2013 and August 2014. Subjects' clinical courses, echocardiography, and coronary angiography examinations were reviewed retrospectively. OCT was performed in 18 patients aged 12.4±5.5 years, 9.0±5.1 years following onset of KD. Of those, 14 patients (77.7%) had a history of CAA (7 with giant CAA and 7 with regressed CAA at time of OCT). Intracoronary nitroglycerin was given to all patients (88.4±45.5 µg/m(2)). Mean radiation dose was 10.9±5.2 mGy/kg. One patient suffered from a transitory uneventful vasospasm at the site of a regressed CAA; otherwise no major procedural complications occurred. The most frequent abnormality observed on OCT was intimal hyperplasia (15 patients, 83.3%) seen at both aneurysmal sites and angiographically normal segments amounting to 390.8±166.0 µm for affected segments compared to 61.7±17 µm for unaffected segments (P<0.001). Disappearance of the media, and presence of fibrosis, calcifications, macrophage accumulation, neovascularization, and white thrombi were seen in 72.2%, 77.8%, 27.8%, 44.4%, and 33.3% of patients. CONCLUSIONS: In this study, OCT proved safe and insightful in the setting of KD, with the potential to add diagnostic value in the assessment of coronary abnormalities in KD. The depicted coronary structural changes correspond to histological findings previously described in KD.

Impact of coronary artery disease on left ventricular ejection fraction recovery following transcatheter aortic valve implantation.

OBJECTIVES: The objective of the present study was to assess if the presence and severity of CAD is associated with decreased LVEF recovery after TAVI. BACKGROUND: Coronary artery disease (CAD) and low left ventricular ejection fraction (LVEF) are common findings in patients undergoing transcatheter aortic valve implantation (TAVI). The impact of CAD on LVEF recovery after TAVI has not been specifically evaluated. METHODS: All patients with LVEF≤50% who underwent TAVI between March 2006 and May 2012 were included in the study. The presence and severity of coronary artery disease was measured using the Duke Myocardial Jeopardy Score (DMJS). A DMJS = 0 corresponds to patients without CAD or complete revascularization and a DMJS > 0 to those with incomplete revascularization. LVEF recovery was assessed by transthoracic echocardiography, measuring the change in LVEF from baseline to 3-months post-TAVI. Myocardial viability was evaluated in a subgroup of patients using cardiac magnetic resonance (CMR) imaging pre-TAVI. RESULTS: Fifty-six patients were included in the study. Twenty-eight patients (50%) had a DMJS > 0. At 3 months, patients with incomplete revascularization (DMJS > 0) demonstrated less LVEF recovery post-TAVI (2.0 ± 9.2% versus 11.7 ± 8.9% if DMJS = 0; P = 0.001). On multivariate analysis, DMJS and presence of significant delayed-enhancement were found to be independent predictors of LVEF recovery. Patients with incomplete revascularization exhibited a worse prognosis with higher mortality at 30-days (22.2% versus 0% if DMJS = 0; P = 0.010) and 1-year (25.9% versus 3.5% if DMJS = 0; P = 0.019). CONCLUSIONS: The present study demonstrates an independent association between incomplete revascularization and decreased LVEF recovery in patients with left ventricular dysfunction undergoing TAVI for severe aortic stenosis.