Artificial Intelligence-Based Fully Automated Quantitative Coronary Angiography vs Optical Coherence Tomography-Guided PCI (FLASH Trial).

Background: Recently developed artificial intelligence-based coronary angiography (AI-QCA, fully automated) provides real-time, objective, and reproducible quantitative analysis of coronary angiography without requiring additional time or labor.

Objectives: This study aimed to evaluate the efficacy of AI-QCA-assisted percutaneous coronary intervention (PCI) compared to optical coherence tomography (OCT)-guided PCI in terms of post-PCI results.

Methods: This trial enrolled 400 patients with significant coronary artery disease undergoing PCI from 13 participating centers in South Korea.

Fully automated quantitative coronary angiography versus optical coherence tomography guidance for coronary stent implantation (FLASH): Study protocol for a randomized controlled noninferiority trial.

Background: Artificial intelligence-based quantitative coronary angiography (AI-QCA) has been developed to provide a more objective and reproducible data about the severity of coronary artery stenosis and the dimensions of the vessel for intervention in real-time, overcoming the limitations of significant inter- and intraobserver variability, and time-consuming nature of on-site QCA, without requiring extra time and effort. Compared with the subjective nature of visually estimated conventional CAG guidance, AI-QCA guidance provides a more practical and standardized angiography-based approach. Although the advantage of intravascular imaging-guided PCI is increasingly recognized, their broader adoption is limited by clinical and economic barriers in many catheterization laboratories.

Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography.

Background: Invasive coronary angiography (ICA) is a primary imaging modality that visualizes the lumen area of coronary arteries for diagnosis and interventional guidance. In the current practice of quantitative coronary analysis (QCA), semi-automatic segmentation tools require labor-intensive and time-consuming manual correction, limiting their application in the catheterization room.

Purpose: This study aims to propose rank-based selective ensemble methods that improve the segmentation performance and reduce morphological errors that limit fully automated quantification of coronary artery using deep-learning segmentation of ICA.

Deep-Learning Segmentation of Urinary Stones in Noncontrast Computed Tomography.

Noncontrast CT (NCCT) relies on labor-intensive examinations of CT slices to identify urolithiasis in the urinary tract, and, despite the use of deep-learning algorithms, false positives remain. A total of 410 NCCT axial scans from patients undergoing surgical treatment for urolithiasis were used for model development. The deep learning model was customized to combine a urolithiasis segmentation with per-slice classification for screening.

Automated multi-class classification for prediction of tympanic membrane changes with deep learning models.

Backgrounds And Objective: Evaluating the tympanic membrane (TM) using an otoendoscope is the first and most important step in various clinical fields. Unfortunately, most lesions of TM have more than one diagnostic name. Therefore, we built a database of otoendoscopic images with multiple diseases and investigated the impact of concurrent diseases on the classification performance of deep learning networks.

Impact of Serial Coronary Stenoses on Various Coronary Physiologic Indices.

Background: Determining the functional significance of each individual coronary lesion in patients with serial coronary stenoses is challenging. It has been proposed that nonhyperemic pressure ratios, such as the instantaneous wave free ratio (iFR) and the ratio of resting distal to proximal coronary pressure (Pd/Pa) are more accurate than fractional flow reserve (FFR) because autoregulation should maintain stable resting coronary flow and avoid hemodynamic interdependence (cross-talk) that occurs during hyperemia. This study aimed to measure the degree of hemodynamic interdependence of iFR, resting Pd/Pa, and FFR in a porcine model of serial coronary stenosis.

Deep-learning segmentation of ultrasound images for automated calculation of the hydronephrosis area to renal parenchyma ratio.

Purpose: We investigated the feasibility of measuring the hydronephrosis area to renal parenchyma (HARP) ratio from ultrasound images using a deep-learning network.

Materials And Methods: The coronal renal ultrasound images of 195 pediatric and adolescent patients who underwent pyeloplasty to repair ureteropelvic junction obstruction were retrospectively reviewed. After excluding cases without a representative longitudinal renal image, we used a dataset of 168 images for deep-learning segmentation.

Electronic Medical Record-Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation.

Background: Although there is a growing interest in prediction models based on electronic medical records (EMRs) to identify patients at risk of adverse cardiac events following invasive coronary treatment, robust models fully utilizing EMR data are limited.

Objective: We aimed to develop and validate machine learning (ML) models by using diverse fields of EMR to predict the risk of 30-day adverse cardiac events after percutaneous intervention or bypass surgery.

Methods: EMR data of 5,184,565 records of 16,793 patients at a quaternary hospital between 2006 and 2016 were categorized into static basic (eg, demographics), dynamic time-series (eg, laboratory values), and cardiac-specific data (eg, coronary angiography).

Impact of coronary calcium score and lesion characteristics on the diagnostic performance of machine-learning-based computed tomography-derived fractional flow reserve.

Aims: To evaluate the impact of coronary artery calcium (CAC) score, minimal lumen area (MLA), and length of coronary artery stenosis on the diagnostic performance of the machine-learning-based computed tomography-derived fractional flow reserve (ML-FFR).

Methods And Results: In 471 patients with coronary artery disease, computed tomography angiography (CTA) and invasive coronary angiography were performed with fractional flow reserve (FFR) in 557 lesions at a single centre. Diagnostic performances of ML-FFR, computational fluid dynamics-based CT-FFR (CFD-FFR), MLA, quantitative coronary angiography (QCA), and visual stenosis grading were evaluated using invasive FFR as a reference standard.

A zero-dimensional predictive model for the pressure drop in the stenotic coronary artery based on its geometric characteristics.

The diameter- or area-reduction ratio measured from coronary angiography, commonly used in clinical practice, is not accurate enough to represent the functional significance of the stenosis, i.e., the pressure drop across the stenosis.

Fate of lumen size in distal coronary segment following successful chronic total occlusion recanalization.

Background: Restoration of anterograde blood flow leads to alterations in vascular wall stress that may influence lumen size distal to chronic total occlusion (CTO) lesions. We sought to assess changes in lumen diameter of segments distal to the stent segment of successfully recanalized CTO.

Methods: We analyzed 507 consecutive CTO cases with stent implantation that underwent follow-up angiography at a single high-volume center (mean follow-up of 13.

Effects of pannus formation on the flow around a bileaflet mechanical heart valve.

Some patients with a bileaflet mechanical heart valve (BMHV) show significant increases in the transvalvular pressure drop and abnormal leaflet motion due to a pannus (an abnormal fibrovascular tissue) formed on the ventricular side, even in the absence of physical contact between the pannus and leaflets. We investigate the effects of the pannus shape (circular or semi-circular ring), implantation location and height on the leaflet motion, flow structure and transvalvular pressure drop using numerical simulations. The valve model considered resembles a 25 mm masters HP valve.

T-Net: Nested encoder-decoder architecture for the main vessel segmentation in coronary angiography.

In this paper, we proposed nested encoder-decoder architecture named T-Net. T-Net consists of several small encoder-decoders for each block constituting convolutional network. T-Net overcomes the limitation that U-Net can only have a single set of the concatenate layer between encoder and decoder block.

Deep learning segmentation of major vessels in X-ray coronary angiography.

X-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable training is required to identify the target vessels and understand the tree structure of coronary arteries. Despite the use of computer-aided tools, such as the edge-detection method, manual correction is necessary for accurate segmentation of coronary vessels.

A homobifunctional imidoester-based microfluidic system for simultaneous DNA and protein isolation from solid or liquid biopsy samples.

The isolation of bio-molecules such as proteins and nucleic acids is a necessary step for both diagnostic and analytical processes in the broad fields of research and clinical applications. Although a myriad of isolation technologies have been developed, a method for simultaneous protein and nucleic acid isolation has not been explored for clinical use. Obtaining samples from certain cancers or rare diseases can be difficult.

Impact of Subtended Myocardial Mass Assessed by Coronary Computed Tomographic Angiography-Based Myocardial Segmentation.

Although decision-making for revascularization is based on the extent of ischemic myocardium, the prognostic implication of supplying myocardial territories has not yet been studied. To evaluate the clinical impact of the coronary artery-based myocardial segmentation (CAMS)-derived myocardial volume subtended to the poststenotic segment, and to determine clinically relevant coronary lesions, coronary computed tomography angiography, invasive coronary angiography, and preprocedure fractional flow reserve (FFR) data were analyzed in 664 deferred lesions (in 577 patients) and 401 treated lesions (in 369 patients) with drug-eluting stent implantation, respectively. Using CAMS method, the myocardial volume subtended to a stenotic coronary segment (V) was assessed.

Automated detection of vulnerable plaque in intravascular ultrasound images.

Acute coronary syndrome (ACS) is a syndrome caused by a decrease in blood flow in the coronary arteries. The ACS is usually related to coronary thrombosis and is primarily caused by plaque rupture followed by plaque erosion and calcified nodule. Thin-cap fibroatheroma (TCFA) is known to be the most similar lesion morphologically to a plaque rupture.

Effect of pannus formation on the prosthetic heart valve: In vitro demonstration using particle image velocimetry.

Although hemodynamic influence of the subprosthetic tissue, termed as pannus, may contribute to prosthetic aortic valve dysfunction, the relationship between pannus extent and hemodynamics in the prosthetic valve has rarely been reported. We investigated the fluid dynamics of pannus formation using in vitro experiments with particle image velocimetry. Subvalvular pannus formation caused substantial changes in prosthetic valve transvalvular peak velocity, transvalvular pressure gradient (TPG) and opening angle.

Incremental Value of Subtended Myocardial Mass for Identifying FFR-Verified Ischemia Using Quantitative CT Angiography: Comparison With Quantitative Coronary Angiography and CT-FFR.

Objectives: This study examined the incremental value of subtended myocardial mass (V) as assessed by coronary computed tomography angiography (CTA) for identifying lesion-specific ischemia verified by invasive fractional flow reserve (FFR) in quantitative coronary CTA.

Background: FFR is determined not only by coronary stenosis severity, but also by V. One-step evaluation of combined V and coronary lesion morphology may improve the accuracy of coronary CTA for identifying ischemia-producing lesions.

Impact of coronary lumen reconstruction on the estimation of endothelial shear stress: in vivo comparison of three-dimensional quantitative coronary angiography and three-dimensional fusion combining optical coherent tomography.

Aims: It is not clearly elucidated how the fusion technique improves the accuracy of endothelial shear stress (ESS) prediction, in comparison with that of three-dimensional (3D) quantitative coronary angiography (QCA) alone. We aimed to evaluate the difference in geometric measurements and haemodynamic estimation between 3D QCA and a 3D fusion model combining 3D QCA and optical coherence tomography (OCT).

Methods And Results: Computational fluid dynamics was assessed in the coronary models of 20 patients.