1. Articles from Miao Chu

    1-11 of 11
    1. Risk Stratification in Acute Coronary Syndrome by Comprehensive Morphofunctional Assessment With Optical Coherence Tomography

      Risk Stratification in Acute Coronary Syndrome by Comprehensive Morphofunctional Assessment With Optical Coherence Tomography

      Background: Artificial intelligence enables simultaneous evaluation of plaque morphology and computational physiology from optical coherence tomography (OCT). Objectives: This study sought to appraise the predictive value of major adverse cardiovascular events (MACE) by combined plaque morphology and computational physiology. Methods: A total of 604 patients with acute coronary syndrome who underwent OCT imaging in ≥1 nonculprit vessel during index coronary angiography were retrospectively enrolled. A novel morphologic index, named the lipid-to-cap ratio (LCR), and a functional parameter to evaluate the physiologic significance of coronary stenosis from OCT, namely, the optical flow ratio (OFR), were calculated from OCT, together with classical ...

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    2. In-hospital heart failure in patients with Takotsubo cardiomyopathy due to coronary artery disease: An artificial intelligence and optical coherence tomography study

      In-hospital heart failure in patients with Takotsubo cardiomyopathy due to coronary artery disease: An artificial intelligence and optical coherence tomography study

      Background: Takotsubo syndrome (TTS) is often associated with symptoms of heart failure (HF) during the acute phase of the disease. 3-dimensional optical coherence tomography (OCT) may be used to assess the extent of angiographically silent underlying coronary artery disease (CAD). This study aims to use an artificial intelligence algorithm to analyze OCT findings and to determine whether the presence of pre-existing CAD predisposes TTS patients to present HF at admission. Methods: This is an observational and retrospective study that enrolled TTS patients who underwent coronary angiography and OCT examination of left anterior descending (LAD) coronary artery. Plaque characterization was automatically ...

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    3. Radial wall strain: a novel angiographic measure of plaque composition and vulnerability

      Radial wall strain: a novel angiographic measure of plaque composition and vulnerability

      Background: The lipid-to-cap ratio (LCR) and thin-cap fibroatheroma (TCFA) derived from optical coherence tomography (OCT) are indicative of plaque vulnerability. Aims: We aimed to explore the association of a novel method to estimate radial wall strain (RWS) from angiography with plaque composition and features of vulnerability assessed by OCT. Methods: Anonymised data from patients with intermediate stenosis who underwent coronary angiography (CAG) and OCT were analysed in a core laboratory. Angiography-derived RWS max was computed as the maximum deformation of lumen diameter throughout the cardiac cycle, expressed as a percentage of the largest lumen diameter. The LCR and TCFA were ...

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    4. Physiologic and compositional coronary artery disease extension in patients with takotsubo syndrome assessed using artificial intelligence: an optical coherence tomography study

      Physiologic and compositional coronary artery disease extension in patients with takotsubo syndrome assessed using artificial intelligence: an optical coherence tomography study

      Background: Takotsubo syndrome (TTS) is an acute and reversible ventricular motion abnormality without epicardial coronary obstruction. Optical flow ratio (OFR) is an approach to evaluate the coronary stenosis significance based on three-dimensional optical coherence tomography (3D-OCT). The aim of this study is to utilize OCT and an artificial intelligence plaque characterization model to show the prevalence and composition of atherosclerotic disease in coronary vessels of patients with TTS. Methods: This is a retrospective and observational study which enrolled patients with TTS who underwent coronary angiography and OCT examination. OCT images were analyzed for tissue characterization and OFR computation using a ...

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    5. Artificial intelligence and optical coherence tomography for the automatic characterisation of human atherosclerotic plaques

      Artificial intelligence and optical coherence tomography for the automatic characterisation of human atherosclerotic plaques

      Background: Intravascular optical coherence tomography (IVOCT) enables detailed plaque characterisation in vivo , but visual assessment is time-consuming and subjective. Aims: This study aimed to develop and validate an automatic framework for IVOCT plaque characterisation using artificial intelligence (AI). Methods: IVOCT pullbacks from five international centres were analysed in a core lab, 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 core labs, taking the ...

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    6. Comprehensive appraisal of cardiac motion artefact in optical coherence tomography

      Comprehensive appraisal of cardiac motion artefact in optical coherence tomography

      Background: The relation between cardiac motion artefact (CMA) in optical coherence tomography (OCT) and the phases of cardiac cycle is unclear. Methods: Optical coherence tomography pullbacks containing metallic stents were co-registered with angiography and retrospectively analyzed. The beginning of three phases, namely ejection, rapid-inflow and diastasis, was identified in angiography. Rotation, shortening, elongation and repetition were qualitatively labelled as CMA artefacts. Platforms with coaxial longitudinal connectors (ML8 and Magmaris) entered a quantitative sub-study, consisting of measuring the length of their connector at the beginning of each phase. Results: A total of 261 stents (127 patients) were analyzed, including 105 stents ...

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

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

      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 ...

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    8. Identification of the type of stent with three-dimensional optical coherence tomography: the SPQR study

      Identification of the type of stent with three-dimensional optical coherence tomography: the SPQR study

      Background: The ability of optical coherence tomography (OCT) to identify specific types of stent has never been systematically studied. Methods and results: A series of 212 consecutive patients with OCT from six international centres were retrospectively screened, finding 294 metallic stents or scaffolds in 146 patients. The sample was analysed by two blinded operators, applying a dedicated protocol in 4 steps to identify the type of stent: 1) 3D and automatic strut detection (ASD), 2) 3D tissue view, 3) Longitudinal view with ASD, 4) Mode “stent only” and ASD. The protocol correctly identified 285 stents (96.9%, kappa 0.965 ...

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    9. Automatic stent reconstruction in optical coherence tomography based on a deep convolutional model

      Automatic stent reconstruction in optical coherence tomography based on a deep convolutional model

      Intravascular optical coherence tomography (IVOCT) can accurately assess stent apposition and expansion, thus enabling the optimisation of a stenting procedure to minimize the risk of device failure. This paper presents a deep convolutional based model for automatic detection and segmentation of stent struts. The input of pseudo-3D images aggregated the information from adjacent frames to refine the probability of strut detection. In addition, multi-scale shortcut connections were implemented to minimize the loss of spatial resolution and refine the segmentation of strut contours. After training, the model was independently tested in 21,363 cross-sectional images from 170 IVOCT image pullbacks. The ...

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    10. Diagnostic accuracy and reproducibility of optical flow ratio for functional evaluation of coronary stenosis in a prospective series

      Diagnostic accuracy and reproducibility of optical flow ratio for functional evaluation of coronary stenosis in a prospective series

      Background: Evaluating prospectively the feasibility, accuracy and reproducibility of optical flow ratio (OFR), a novel method of computational physiology based on optical coherence tomography (OCT). Methods and results: Sixty consecutive patients (76 vessels) underwent prospectively OCT, angiography-based quantitative flow ratio (QFR) and fractional flow ratio (FFR). OFR was computed offline in a central core-lab by analysts blinded to FFR. OFR was feasible in 98.7% of the lesions and showed excellent agreement with FFR (ICCa = 0.83, r = 0.83, slope = 0.80, intercept = 0.17, kappa = 0.84). The area under curve to predict an FFR ≤ 0.80 was ...

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    1-11 of 11
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    Echoes from Picasso: Explanation of an unusual artefact in optical coherence tomography Diagnostic accuracy and reproducibility of optical flow ratio for functional evaluation of coronary stenosis in a prospective series Automatic stent reconstruction in optical coherence tomography based on a deep convolutional model Identification of the type of stent with three-dimensional optical coherence tomography: the SPQR study Automatic Characterisation of Human Atherosclerotic Plaque Composition from Intravascular Optical Coherence Tomography Using Artificial Intelligence Comprehensive appraisal of cardiac motion artefact in optical coherence tomography Physiologic and compositional coronary artery disease extension in patients with takotsubo syndrome assessed using artificial intelligence: an optical coherence tomography study Artificial intelligence and optical coherence tomography for the automatic characterisation of human atherosclerotic plaques Radial wall strain: a novel angiographic measure of plaque composition and vulnerability In-hospital heart failure in patients with Takotsubo cardiomyopathy due to coronary artery disease: An artificial intelligence and optical coherence tomography study A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography Quantitative assessment of textural features in the early detection of diabetic retinopathy with optical coherence tomography angiography