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. METHODS: The FLASH (fully automated quantitative coronary angiography versus optical coherence tomography guidance for coronary stent implantation) trial is a randomized, investigator-initiated, multicenter, open-label, noninferiority trial comparing the AI-QCA-assisted PCI strategy with optical coherence tomography-guided PCI strategy in patients with significant coronary artery disease. All operators will utilize a novel, standardized AI-QCA software and PCI protocol in the AI-QCA-assisted group. A total of 400 patients will be randomized to either group at a 1:1 ratio. The primary endpoint is the minimal stent area (mm2), determined by the final OCT run after completion of PCI. Clinical follow-up and cost-effectiveness evaluations are planned at 1 month and 6 months for all patients enrolled in the study. RESULTS: Enrollment of a total of 400 patients from the 13 participating centers in South Korea will be completed in February 2024. Follow-up of the last enrolled patients will be completed in August 2024, and primary results will be available by late 2024. CONCLUSION: The FLASH is the first clinical trial to evaluate the feasibility of AI-QCA-assisted PCI, and will provide the clinical evidence on AI-QCA assistance in the field of coronary intervention. CLINICAL TRIAL REGISTRATION: URL: https://www. CLINICALTRIALS: gov. Unique identifier: NCT05388357.

Multi-VENC acquisition of four-dimensional phase-contrast MRI to improve precision of velocity field measurement.

PURPOSE: The present study aims to improve precision of four-dimensional (4D) phase-contrast (PC) MRI technique by using multiple velocity encoding (VENC) parameters. THEORY AND METHODS: The 3D flow fields in an in vitro stenosis phantom and an in vivo ascending aorta were determined using a 4D PC-MRI sequence with multiple VENC values. The velocity field obtained for large VENC was combined with that from small VENC, unless velocity data were lost by phase aliasing and phase dispersion. Noise levels of the combined velocity fields were compared with the increasing overlapping number of VENC parameters. RESULTS: The phantom measurement showed that the multi-VENC acquisition reduced the noise levels in radial and axial velocities (> 24 cm/s at VENC = 300 cm/s) down to 0.80 ± 0.45 cm/s and 5.60 ± 2.63 cm/s, respectively. This increased the velocity-to-noise ratio (VNR) by approximately two-fold to six-fold depending on the locations. As a result, the multi-VENC measurement could visualize the low-velocity recirculating flows more clearly. CONCLUSION: The multi-VENC measurement of 4D PC-MRI sequence increased the VNR distribution by reducing velocity noise. The improved VNR can be beneficial for investigating blood flow structures in a flow field with a high velocity dynamic range.

Four-dimensional flow MRI for evaluation of post-stenotic turbulent flow in a phantom: comparison with flowmeter and computational fluid dynamics.

OBJECTIVES: To validate 4D flow MRI in a flow phantom using a flowmeter and computational fluid dynamics (CFD) as reference. METHODS: Validation of 4D flow MRI was performed using flow phantoms with 75 % and 90 % stenosis. The effect of spatial resolution on flow rate, peak velocity and flow patterns was investigated in coronal and axial scans. The accuracy of flow rate with 4D flow MRI was evaluated using a flowmeter as reference, and the peak velocity and flow patterns obtained were compared with CFD analysis results. RESULTS: 4D flow MRI accurately measured the flow rate in proximal and distal regions of the stenosis (percent error ≤3.6 % in axial scanning with 1.6-mm resolution). The peak velocity of 4D flow MRI was underestimated by more than 22.8 %, especially from the second half of the stenosis. With 1-mm isotropic resolution, the maximum thickness of the recirculating flow region was estimated within a 1-mm difference, but the turbulent velocity fluctuations mostly disappeared in the post-stenotic region. CONCLUSION: 4D flow MRI accurately measures the flow rates in the proximal and distal regions of a stenosis in axial scan but has limitations in its estimation of peak velocity and turbulent characteristics. KEY POINTS: • 4D flow MRI accurately measures the flow rate in axial scan. • The peak velocity was underestimated by 4D flow MRI. •4D flow MRI demonstrates the principal pattern of post-stenotic flow.

Optimal Minimal Stent Area and Impact of Stent Underexpansion in Left Main Up-Front 2-Stent Strategy.

BACKGROUND: We previously reported the use of minimal stent area to predict angiographic in-stent restenosis after drug-eluting stent implantation for unprotected left main (LM) disease. We aimed to evaluate the optimal minimal stent area criteria for up-front LM 2-stenting based on long-term clinical outcomes. METHODS: We identified 292 consecutive patients with LM bifurcation stenosis who were treated using the crush technique. The final minimal stent area was measured in the ostial left anterior descending artery (LAD), ostial left circumflex artery (LCX), and distal LM. The primary outcome was 5-year major adverse cardiac events, including all-cause death, myocardial infarction, and target lesion revascularization. RESULTS: The minimal stent area cutoff values that best predicted the 5-year major adverse cardiac events were 11.8 mm2 for distal LM (area under the curve, 0.57; P=0.15), 8.3 mm2 for LAD ostium (area under the curve, 0.62; P=0.02), and 5.7 mm2 for LCX ostium (area under the curve, 0.64; P=0.01). Using these criteria, the risk of 5-year major adverse cardiac events was significantly associated with stent underexpansion in the LAD ostium (hazard ratio, 3.14; [95% CI, 1.23-8.06]; P=0.02) and LCX ostium (hazard ratio, 2.60 [95% CI, 1.11-6.07]; P=0.03) but not in the distal LM (hazard ratio, 0.81 [95% CI, 0.34-1.91]; P=0.63). Patients with stent underexpansion in both ostial LAD and LCX had a significantly higher rate of 5-year major adverse cardiac events than those with no or 1 underexpanded stent of either ostium (P<0.01). CONCLUSIONS: Stent underexpansion in the LAD and LCX ostium was significantly associated with long-term outcomes in patients who underwent up-front 2-stenting for LM bifurcation stenosis.

Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning.

BACKGROUND: Quantitative coronary angiography (QCA) offers objective and reproducible measures of coronary lesions. However, significant inter- and intra-observer variability and time-consuming processes hinder the practical application of on-site QCA in the current clinical setting. This study proposes a novel method for artificial intelligence-based QCA (AI-QCA) analysis of the major vessels and evaluates its performance. METHODS: AI-QCA was developed using three deep-learning models trained on 7658 angiographic images from 3129 patients for the precise delineation of lumen boundaries. An automated quantification method, employing refined matching for accurate diameter calculation and iterative updates of diameter trend lines, was embedded in the AI-QCA. A separate dataset of 676 coronary angiography images from 370 patients was retrospectively analyzed to compare AI-QCA with manual QCA performed by expert analysts. A match was considered between manual and AI-QCA lesions when the minimum lumen diameter (MLD) location identified manually coincided with the location identified by AI-QCA. Matched lesions were evaluated in terms of diameter stenosis (DS), MLD, reference lumen diameter (RLD), and lesion length (LL). RESULTS: AI-QCA exhibited a sensitivity of 89% in lesion detection and strong correlations with manual QCA for DS, MLD, RLD, and LL. Among 995 matched lesions, most cases (892 cases, 80%) exhibited DS differences ≤10%. Multiple lesions of the major vessels were accurately identified and quantitatively analyzed without manual corrections. CONCLUSION: AI-QCA demonstrates promise as an automated tool for analysis in coronary angiography, offering potential advantages for the quantitative assessment of coronary lesions and clinical decision-making.

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

Background: 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. Materials and Methods: 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. Prediction models of the axial, coronal, and sagittal views were trained, and an additive model with an intersection of the coronal and sagittal predictions added to the axial outcome was introduced. Automated quantification of clinical metrics was evaluated in three-dimensional models of urinary stones. Results: The axial model detected 88.92% of urinary stones and produced a dice similarity coefficient of 87.56% in the urolithiasis segmentation. For urolithiasis (>5 mm), the sensitivity of the axial model reached 95.10%. False positives were reduced to 0.34 per patient using an ensemble of individual models. The additive model improved the sensitivity to 90.97% by detecting more small urolithiasis (<5 mm). All clinical metrics of size, long-axis diameter, volume, mean stone density, stone heterogeneity index, and skin-to-stone distance showed a strong correlation of R2 > 0.964. Conclusions: The proposed system could reduce the burden on the physician for imaging diagnosis and help determine treatment strategies for urinary stones through automated quantification of clinical metrics with high accuracy and reproducibility.

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. METHODS: Two selective ensemble methods proposed in this work integrated the weighted ensemble approach with per-image quality estimation. The segmentation outcomes from five base models with different loss functions were ranked either by mask morphology or estimated dice similarity coefficient (DSC). The final output was determined by imposing different weights according to the ranks. The ranking criteria based on mask morphology were formulated from empirical insight to avoid frequent types of segmentation errors (MSEN), while the estimation of DSCs was performed by comparing the pseudo-ground truth generated from a meta-learner (ESEN). Five-fold cross-validation was performed with the internal dataset of 7426 coronary angiograms from 2924 patients, and prediction model was externally validated with 556 images of 226 patients. RESULTS: The selective ensemble methods improved the segmentation performance with DSCs up to 93.07% and provided a better delineation of coronary lesion with local DSCs of up to 93.93%, outperforming all individual models. Proposed methods also minimized the chances of mask disconnection in the most narrowed regions to 2.10%. The robustness of the proposed methods was also evident in the external validation. Inference time for major vessel segmentation was approximately one-sixth of a second. CONCLUSION: Proposed methods successfully reduced morphological errors in the predicted masks and were able to enhance the robustness of the automatic segmentation. The results suggest better applicability of real-time QCA-based diagnostic methods in routine clinical settings.

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. STUDY DESIGN: This retrospective study investigated the impact of concurrent diseases in the tympanic membrane on diagnostic performance using multi-class classification. A customized architecture of EfficientNet-B4 was introduced to predict the primary class (otitis media with effusion (OME), chronic otitis media (COM), and 'None' without OME and COM) and secondary classes (attic cholesteatoma, myringitis, otomycosis, and ventilating tube). RESULTS: Deep-learning classifications accurately predicted the primary class with dice similarity coefficient (DSC) of 95.19%, while misidentification between COM and OME rarely occurred. Among the secondary classes, the diagnosis of attic cholesteatoma and myringitis achieved a DSC of 88.37% and 88.28%, respectively. Although concurrent diseases hampered the prediction performance, there was only a 0.44% probability of inaccurately predicting two or more secondary classes (29/6,630). The inference time per image was 2.594 ms on average. CONCLUSION: Deep-learning classification can be used to support clinical decision-making by accurately and reproducibly predicting tympanic membrane changes in real time, even in the presence of multiple concurrent diseases.

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. Ten novel networks, such as combinations of DeepLabV3+ and UNet++, were assessed for their ability to calculate hydronephrosis and kidney areas, and the ensemble method was applied for further improvement. By dividing the image set into four, cross-validation was conducted, and the segmentation performance of the deep-learning network was evaluated using sensitivity, specificity, and dice similarity coefficients by comparison with the manually traced area. RESULTS: All 10 networks and ensemble methods showed good visual correlation with the manually traced kidney and hydronephrosis areas. The dice similarity coefficient of the 10-model ensemble was 0.9108 on average, and the best 5-model ensemble had a dice similarity coefficient of 0.9113 on average. We included patients with severe hydronephrosis who underwent renal ultrasonography at a single institution; thus, external validation of our algorithm in a heterogeneous ultrasonography examination setup with a diverse set of instruments is recommended. CONCLUSIONS: Deep-learning-based calculation of the HARP ratio is feasible and showed high accuracy for imaging of the severity of hydronephrosis using ultrasonography. This algorithm can help physicians make more accurate and reproducible diagnoses of hydronephrosis using ultrasonography.

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. METHODS: In 6 anesthetized female swine, 381 serial coronary stenoses were created in the left anterior descending artery using 2 balloon catheters. The degree of hemodynamic interdependence was calculated by measuring the absolute changes in iFR, resting Pd/Pa, and FFR across the fixed stenosis as the severity of the other stenosis varied. RESULTS: The hemodynamic interdependence of iFR, resting Pd/Pa, and FFR was 0.039±0.048, 0.021±0.026, and 0.034±0.034, respectively (all P<0.001). When the functional significance of serial stenoses was less severe (0.70-0.90 for each index), the hemodynamic interdependence was 0.009±0.020, 0.007±0.013, and 0.017±0.022 for iFR, resting Pd/Pa, and FFR, respectively (all P<0.001). However, in more severe serial coronary stenoses (<0.60 for each index), hemodynamic interdependence was 0.060±0.050, 0.037±0.030, and 0.051±0.037 for iFR, resting Pd/Pa, and FFR, respectively (all P<0.001). CONCLUSIONS: When assessing serial coronary stenoses, nonhyperemic pressure ratios are affected by hemodynamic interdependence. When the functional significance of serial coronary stenoses is severe, the effect is similar to that which is seen with FFR.

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). The data were randomly split into training, tuning, and testing sets in a ratio of 3:1:1. Each model was evaluated with 5-fold cross-validation and with an external EMR-based cohort at a tertiary hospital. Logistic regression (LR), random forest (RF), gradient boosting machine (GBM), and feedforward neural network (FNN) algorithms were applied. The primary outcome was 30-day mortality following invasive treatment. RESULTS: GBM showed the best performance with area under the receiver operating characteristic curve (AUROC) of 0.99; RF had a similar AUROC of 0.98. AUROCs of FNN and LR were 0.96 and 0.93, respectively. GBM had the highest area under the precision-recall curve (AUPRC) of 0.80, and the AUPRCs of RF, LR, and FNN were 0.73, 0.68, and 0.63, respectively. All models showed low Brier scores of <0.1 as well as highly fitted calibration plots, indicating a good fit of the ML-based models. On external validation, the GBM model demonstrated maximal performance with an AUROC of 0.90, while FNN had an AUROC of 0.85. The AUROCs of LR and RF were slightly lower at 0.80 and 0.79, respectively. The AUPRCs of GBM, LR, and FNN were similar at 0.47, 0.43, and 0.41, respectively, while that of RF was lower at 0.33. Among the categories in the GBM model, time-series dynamic data demonstrated a high AUROC of >0.95, contributing majorly to the excellent results. CONCLUSIONS: Exploiting the diverse fields of the EMR data set, the ML-based 30-day adverse cardiac event prediction models demonstrated outstanding results, and the applied framework could be generalized for various health care prediction models.

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. Diagnostic performances were analysed according to lesion characteristics including the MLA, length of stenosis, CAC score, and stenosis degree. ML-FFR was obtained by automated feature selection and model building from quantitative CTA. A total of 272 lesions showed significant ischaemia, defined by invasive FFR ≤0.80. There was a significant correlation between CFD-FFR and ML-FFR (r = 0.99, P < 0.001). ML-FFR showed moderate sensitivity and specificity in the per-patient analysis. Diagnostic performances of CFD-FFR and ML-FFR did not decline in patients with high CAC scores (CAC > 400). Sensitivities of CFD-FFR and ML-FFR showed a downward trend along with the increase in lesion length and decrease in MLA. The area under the curve (AUC) of ML-FFR (0.73) was higher than those of QCA and visual grading (AUC = 0.65 for both, P < 0.001) and comparable to those of MLA (AUC = 0.71, P = 0.21) and CFD-FFR (AUC = 0.73, P = 0.86). CONCLUSION: ML-FFR showed comparable results to MLA and CFD-FFR for the prediction of lesion-specific ischaemia. Specificities and accuracies of CFD-FFR and ML-FFR decreased with smaller MLA and long lesion length.

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 13.5 months). Segments ≤40 mm distal to the stent edge were analyzed using quantitative coronary angiography. RESULTS: At follow-up, lumen diameters significantly increased; diameter changes of 0.26 ± 0.47 (percent diameter change of 18.2%) at 5 mm distal, mean lumen diameter changes of 0.23 ± 0.35 (14.3%) and minimal lumen diameter changes of 0.22 ± 0.80 (24.7%) (all p < 0.001). Lumen enlargement was similar between visually shrunken and stenosed vessels (degree of stenosis ≥20% with luminal irregularities) distal to stents; 5 mm distal (0.32 ± 0.48 vs. 0.30 ± 0.48, p = 0.76), mean lumen diameter changes (0.26 ± 0.37mm vs. 0.26±0.33 mm, p = 0.94), minimal lumen diameter changes (0.28 ± 0.43 mm vs. 0.22 ± 1.30 mm, p = 0.48). There was no association between degree of in-stent narrowing and changes in distal lumen diameter (Spearman r = -0.02, p = 0.59). Multivariate logistic regression for the predictors of greater lumen enlargement indicated that patients with left ventricle dysfunction (ejection fraction ≤45%) had greater enlargement [odds ratio (OR): 2.53, 95% confidence interval (CI): 1.23-5.23, p = 0.01]. Conversely, a low hematocrit (male <40%, and female <35%) was associated with attenuated lumen enlargement (OR: 0.68 95% CI: 0.47-0.98; p = 0.04). CONCLUSIONS: Lumen diameter distal to CTO lesions significantly increased following successful revascularization, regardless of diseased status of the distal bed or degree of in-stent narrowing. These findings implicate appropriate determination of stent size, stent coverage length, as well as management strategies of distal vessels.

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. To be more precise, the U-Net symmetrically forms the concatenate layers, so the low-level feature of the encoder is connected to the latter part of the decoder, and the high-level feature is connected to the beginning of the decoder. T-Net arranges the pooling and up-sampling appropriately during the encoding process, and likewise during the decoding process so that feature-maps of various sizes are obtained in a single block. As a result, all features from the low-level to the high-level extracted from the encoder are delivered from the beginning of the decoder to predict a more accurate mask. We evaluated T-Net for the problem of segmenting three main vessels in coronary angiography images. The experiment consisted of a comparison of U-Net and T-Nets under the same conditions, and an optimized T-Net for the main vessel segmentation. As a result, T-Net recorded a Dice Similarity Coefficient score (DSC) of 83.77%, 10.69% higher than that of U-Net, and the optimized T-Net recorded a DSC of 88.97% which was 15.89% higher than that of U-Net. In addition, we visualized the weight activation of the convolutional layer of T-Net and U-Net to show that T-Net actually predicts the mask from earlier decoders. Therefore, we expect that T-Net can be effectively applied to other similar medical image segmentation problems.

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. We propose a new zero-dimensional model for the pressure drop across the stenosis considering its geometric characteristics and flow rate. To identify the geometric parameters affecting the pressure drop, we perform three-dimensional numerical simulations for thirty-three patient-specific coronary stenoses. From these numerical simulations, we show that the pressure drop is mostly determined by the curvature as well as the area-reduction ratio of the stenosis before the minimal luminal area (MLA), but heavily depends on the area-expansion ratio after the MLA due to flow separation. Based on this result, we divide the stenosis into the converging and diverging parts in the present zero-dimensional model. The converging part is segmented into a series of straight and curved pipes with curvatures, and the loss of each pipe is estimated by an empirical relation between the total pressure drop, flow rate, and pipe geometric parameters (length, diameter, and curvature). The loss in the diverging part is predicted by a relation among the total pressure drop, Reynolds number, and area expansion ratio with the coefficients determined by a machine learning method. The pressure drops across the stenoses predicted by the present zero-dimensional model agree very well with those obtained from three-dimensional numerical simulations.

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. The mean systolic pressure drop is significantly increased with increasing pannus height, irrespective of its implantation orientation. Near the peak inflow rate, the flow behind the pannus becomes highly turbulent, and the transvalvular pressure drop is markedly increased by the pannus. At the end of valve opening and the start of valve closing, oscillatory motions of the leaflets occur due to periodic shedding of vortex rings behind the pannus, and their amplitudes become large with increasing pannus height. When the pannus shape is asymmetric (e.g., a semi-circular ring) and its height reaches about 0.1D (D (= 25 mm) is the diameter of an aorta), abnormal leaflet motions occur: two leaflets move asymmetrically, and valve closing is delayed in time or incomplete, which increases the regurgitation volume. The peak energy loss coefficients due to panni are obtained from simulation data and compared with those predicted by a one-dimensional model. The comparison indicates that the one-dimensional model is applicable for the BMHV with and without pannus.

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. In this paper, we propose methods to classify TCFA using various machine learning classifiers including feed-forward neural network (FNN), K-nearest neighbor (KNN), random forest (RF), and convolutional neural network (CNN) to figure out a classifier that shows optimal TCFA classification accuracy. In addition, we suggest pixel range-based feature extraction method to extract the ratio of pixels in the different region of interests to reflect the physician's TCFA discrimination criteria. Our feature extraction method examines the pixel distribution of the intravascular ultrasound (IVUS) image at a given ROI, which allows us to extract general characteristics of the IVUS image while simultaneously reflecting the different properties of the vessel's substances such as necrotic core and calcified nodule depending on the brightness of the pixel. A total of 12,325 IVUS images were labeled with corresponding optical coherence tomography (OCT) images to train and evaluate the classifiers. We achieved 0.859, 0.848, 0.844, and 0.911 area under the ROC curve (AUC) in the order of using FNN, KNN, RF, and CNN classifiers. As a result, the CNN classifier performed best and the top 10 features of the feature-based classifiers (FNN, KNN, RF) were found to be similar to the physician's TCFA diagnostic criteria. Graphical Abstract AUC result of proposed classifiers.

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. In addition, the heterogeneity of cancer tissues typically leads to inconsistent results when analyzing biomolecules. We here describe a homobifunctional imidoester (HI)-based microfluidic system for simultaneous DNA and protein isolation from either a solid or liquid single biopsy sample. An efficient and cost effective microfluidic design with less air bubbles was identified among several candidates using simulation and experimental results from the streamlining of isolation processing. HI groups were used as capture reagents for the simultaneous isolation of bio-molecules from a single specimen in a single microfluidic system. The clinical utility of this system for the simultaneous isolation of DNA and proteins within 40 min was validated in cancer cell lines and 23 tissue biopsies from colorectal cancer patients. The quantity of isolated protein and DNA was high using this system compared to the spin-column method. This HI-based microfluidic system shows good rapidity, affordability, and portability in the isolation of bio-molecules from limited samples for subsequent clinical analysis.

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. In the present study, we proposed a robust method for major vessel segmentation using deep learning models with fully convolutional networks. When angiographic images of 3302 diseased major vessels from 2042 patients were tested, deep learning networks accurately identified and segmented the major vessels in X-ray coronary angiography. The average F1 score reached 0.917, and 93.7% of the images exhibited a high F1 score > 0.8. The most narrowed region at the stenosis was distinctly captured with high connectivity. Robust predictability was validated for the external dataset with different image characteristics. For major vessel segmentation, our approach demonstrated that prediction could be completed in real time with minimal image preprocessing. By applying deep learning segmentation, QCA analysis could be further automated, thereby facilitating the use of QCA-based diagnostic methods.

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 (Vsub) 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 Vsub. One-step evaluation of combined Vsub and coronary lesion morphology may improve the accuracy of coronary CTA for identifying ischemia-producing lesions. METHODS: A total of 246 intermediate coronary artery lesions (30% to 80% diameter stenosis) in 220 patients (mean age 61.7 years, 168 men) interrogated by FFR were retrospectively studied. Coronary CTA data were used to assess the Vsub by coronary artery stenosis, minimal lumen area (MLA), percentage of aggregated plaque volume (%APV), positive remodeling, and low-attenuation plaque. The ability of Vsub/MLA2 to discriminate lesions with FFR ≤0.80 was examined. Diagnostic performance, odds ratios, and category-less net reclassification improvements of coronary CTA parameters for FFR-verified (≤0.80) ischemia were evaluated. On-site computed tomography (CT) derived-FFR (CT-FFR) and quantitative coronary angiography (QCA) data were also compared. RESULTS: Of 246 lesions, 84 (34.1%) showed an FFR ≤0.80. Vsub was independently associated with an FFR ≤0.80 (odds ratio: 1.04/1 cm3; p = 0.032) and showed incremental value over MLA. Vsub/MLA2 >4.16 was the best single parameter for discriminating an FFR ≤0.80 with 83.3% sensitivity and 67.9% specificity. The area under the curve (AUC) of Vsub/MLA2 >4.16 (0.80 [95% confidence interval: 0.75 to 0.85]) was better than that of MLA (change in [Δ]AUC: 0.069; p < 0.001), %APV (ΔAUC: 0.096; p = 0.017), and diameter stenosis of QCA (ΔAUC: 0.080; p = 0.037) and was comparable to that of CT-FFR (AUC 0.77; ΔAUC: 0.035; p = 0.304). CONCLUSIONS: Vsub is an independent determinant of an FFR ≤0.80. The mathematical index of Vsub/MLA2 >4.16 assessed by coronary CTA shows better diagnostic performance for the detection of ischemia-producing lesions than CT-derived MLA alone or %APV and QCA parameters and was comparable to that of on-site CT-FFR.