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1. Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction

   https://doi.org/10.3389/fdgth.2023.1279644  

   Kalita, Kanak; Ganesh, Narayanan; Jayalakshmi, Sambandam; Chohan, Jasgurpreet Singh; Mallik, Saurav; Qin, Hong (November 2023, Frontiers in Digital Health)

   The global rise in heart disease necessitates precise prediction tools to assess individual risk levels. This paper introduces a novel Multi-Objective Artificial Bee Colony Optimized Hybrid Deep Belief Network and XGBoost (HDBN-XG) algorithm, enhancing coronary heart disease prediction accuracy. Key physiological data, including Electrocardiogram (ECG) readings and blood volume measurements, are analyzed. The HDBN-XG algorithm assesses data quality, normalizes using z-score values, extracts features via the Computational Rough Set method, and constructs feature subsets using the Multi-Objective Artificial Bee Colony approach. Our findings indicate that the HDBN-XG algorithm achieves an accuracy of 99%, precision of 95%, specificity of 98%, sensitivity of 97%, and F1-measure of 96%, outperforming existing classifiers. This paper contributes to predictive analytics by offering a data-driven approach to healthcare, providing insights to mitigate the global impact of coronary heart disease. 

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2. Evaluation of intracoronary hemodynamics identifies perturbations in vorticity

   https://doi.org/10.3389/fsysb.2022.930396  

   Vardhan, Madhurima; Gounley, John; Chen, S James; Nair, Priya; Wei, Wei; Hegele, Luiz; Kusner, Jonathan; Kahn, Andrew M; Frakes, David; Leopold, Jane A; et al (November 2022, Frontiers in Systems Biology)

   Background and objective:Coronary artery disease (CAD) is highly prevalent and associated with adverse events. Challenges have emerged in the treatment of intermediate coronary artery stenoses. These lesions are often interrogated with fractional flow reserve (FFR) testing to determine if a stenosis is likely to be causative for ischemia in a cardiac territory. This invasive test requires insertion of a pressure wire into a coronary vessel. Recently computational fluid dynamics (CFD) has been used to noninvasively assess fractional flow reserve in vessels reconstructed from medical imaging data. However, many of these simulations are unable to provide additional information about intravascular hemodynamics, including velocity, endothelial shear stress (ESS), and vorticity. We hypothesized that vorticity, which has demonstrated utility in the assessment of ventricular and aortic diseases, would also be an important hemodynamic factor in CAD. Methods:Three-dimensional (3D), patient-specific coronary artery geometries that included all vessels >1 mm in diameter were created from angiography data obtained from 10 patients who underwent diagnostic angiography and FFR testing (n = 9). A massively parallel CFD solver (HARVEY) was used to calculate coronary hemodynamic parameters including pressure, velocity, ESS, and vorticity. These simulations were validated by comparing velocity flow fields from simulation to both velocities derived fromin vitroparticle image velocimetry and to invasively acquired pressure wire-based data from clinical testing. Results:There was strong agreement between findings from CFD simulations and particle image velocimetry experimental testing (p< 0.01). CFD-FFR was also highly correlated with invasively measured FFR (ρ= 0.77,p= 0.01) with an average error of 5.9 ± 0.1%. CFD-FFR also had a strong inverse correlation with the vorticity (ρ= -0.86,p= 0.001). Simulations to determine the effect of the coronary stenosis on intravascular hemodynamics demonstrated significant differences in velocity and vorticity (bothp< 0.05). Further evaluation of an angiographically normal appearing non-FFR coronary vessel in patients with CAD also demonstrated differences in vorticity when compared with FFR vessels (p< 0.05). Conclusion:The use of highly accurate 3D CFD-derived intravascular hemodynamics provides additional information beyond pressure measurements that can be used to calculate FFR. Vorticity is one parameter that is modified by a coronary stenosis and appears to be abnormal in angiographically normal vessels in patients with CAD, highlighting a possible use-case in preventative screening for early coronary disease. 

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3. Prediction of coronary artery calcium scoring from surface electrocardiogram in atherosclerotic cardiovascular disease: a pilot study

   https://doi.org/10.1093/ehjdh/ztaa008  

   Farjo, Peter D; Yanamala, Naveena; Kagiyama, Nobuyuki; Patel, Heenaben B; Casaclang-Verzosa, Grace; Nezarat, Negin; Budoff, Matthew J; Sengupta, Partho P (November 2020, European Heart Journal - Digital Health)

   null (Ed.)

   Abstract Aims Coronary artery calcium (CAC) scoring is an established tool for cardiovascular risk stratification. However, the lack of widespread availability and concerns about radiation exposure have limited the universal clinical utilization of CAC. In this study, we sought to explore whether machine learning (ML) approaches can aid cardiovascular risk stratification by predicting guideline recommended CAC score categories from clinical features and surface electrocardiograms. Methods and results In this substudy of a prospective, multicentre trial, a total of 534 subjects referred for CAC scores and electrocardiographic data were split into 80% training and 20% testing sets. Two binary outcome ML logistic regression models were developed for prediction of CAC scores equal to 0 and ≥400. Both CAC = 0 and CAC ≥400 models yielded values for the area under the curve, sensitivity, specificity, and accuracy of 84%, 92%, 70%, and 75%, and 87%, 91%, 75%, and 81%, respectively. We further tested the CAC ≥400 model to risk stratify a cohort of 87 subjects referred for invasive coronary angiography. Using an intermediate or higher pretest probability (≥15%) to predict CAC ≥400, the model predicted the presence of significant coronary artery stenosis (P = 0.025), the need for revascularization (P < 0.001), notably bypass surgery (P = 0.021), and major adverse cardiovascular events (P = 0.023) during a median follow-up period of 2 years. Conclusion ML techniques can extract information from electrocardiographic data and clinical variables to predict CAC score categories and similarly risk-stratify patients with suspected coronary artery disease. 

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4. Multi-Series CT Image Super-Resolution by using Transfer Generative Adversarial Network

   Xiao, Yao; Arreola, Manuel M.; Barreto, Izabella; Bolch, Wesley E.; Fox, W. Christopher; Peters, Keith; Rajderkar, Dhanashree A.; Rees, John H.; Fang, Ruogu (June 2020, Society for Imaging Informatics in Medicine (SIIM) Annual Meeting)

   Introduction Multi-series CT (MSCT) scans, including non-contrast CT (NCCT), CT Perfusion (CTP), and CT Angiography (CTA), are widely used in acute stroke imaging. While each scan has its advantage in disease diagnosis, the varying image resolution of different series hinders the ability of the radiologist to discern subtle suspicious findings. Besides, higher image quality requires high radiation doses, leading to increases in health risks such as cataract formation and cancer induction. Thus, it is highly crucial to develop an approach to improve MSCT resolution and to lower radiation exposure. Hypothesis MSCT imaging of the same patient is highly correlated in structural features, the transferring and integration of the shared and complementary information from different series are beneficial for achieving high image quality. Methods We propose TL-GAN, a learning-based method by using Transfer Learning (TL) and Generative Adversarial Network (GAN) to reconstruct high-quality diagnostic images. Our TL-GAN method is evaluated on 4,382 images collected from nine patients’ MSCT scans, including 415 NCCT slices, 3,696 CTP slices, and 271 CTA slices. We randomly split the nine patients into a training set (4 patients), a validation set (2 patients), and a testing set (3 patients). In preprocessing, we remove the background and skull and visualize in brain window. The low-resolution images (1/4 of the original spatial size) are simulated by bicubic down-sampling. For training without TL, we train different series individually, and for with TL, we follow the scanning sequence (NCCT, CTP, and CTA) by finetuning. Results The performance of TL-GAN is evaluated by the peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) index on 184 NCCT, 882 CTP, and 107 CTA test images. Figure 1 provides both visual (a-c) and quantity (d-f) comparisons. Through TL-GAN, there is a significant improvement with TL than without TL (training from scratch) for NCCT, CTP, and CTA images, respectively. These significances of performance improvement are evaluated by one-tailed paired t-tests (p < 0.05). We enlarge the regions of interest for detail visual comparisons. Further, we evaluate the CTP performance by calculating the perfusion maps, including cerebral blood flow (CBF) and cerebral blood volume (CBV). The visual comparison of the perfusion maps in Figure 2 demonstrate that TL-GAN is beneficial for achieving high diagnostic image quality, which are comparable to the ground truth images for both CBF and CBV maps. Conclusion Utilizing TL-GAN can effectively improve the image resolution for MSCT, provides radiologists more image details for suspicious findings, which is a practical solution for MSCT image quality enhancement. 

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5. Analysis identifying minimal governing parameters for clinically accurate in silico fractional flow reserve

   https://doi.org/10.3389/fmedt.2022.1034801  

   Tanade, Cyrus; Chen, S James; Leopold, Jane A; Randles, Amanda (December 2022, Frontiers in Medical Technology)

   BackgroundPersonalized hemodynamic models can accurately compute fractional flow reserve (FFR) from coronary angiograms and clinical measurements (FFR ${}_{\mathrm{baseline}}$), but obtaining patient-specific data could be challenging and sometimes not feasible. Understanding which measurements need to be patient-tuned vs. patient-generalized would inform models with minimal inputs that could expedite data collection and simulation pipelines. AimsTo determine the minimum set of patient-specific inputs to compute FFR using invasive measurement of FFR (FFR ${}_{\mathrm{invasive}}$) as gold standard. Materials and MethodsPersonalized coronary geometries ( $N=50$) were derived from patient coronary angiograms. A computational fluid dynamics framework, FFR ${}_{\mathrm{baseline}}$, was parameterized with patient-specific inputs: coronary geometry, stenosis geometry, mean arterial pressure, cardiac output, heart rate, hematocrit, and distal pressure location. FFR ${}_{\mathrm{baseline}}$was validated against FFR ${}_{\mathrm{invasive}}$and used as the baseline to elucidate the impact of uncertainty on personalized inputs through global uncertainty analysis. FFR ${}_{\mathrm{streamlined}}$was created by only incorporating the most sensitive inputs and FFR ${}_{\text{semi-streamlined}}$additionally included patient-specific distal location. ResultsFFR ${}_{\mathrm{baseline}}$was validated against FFR ${}_{\mathrm{invasive}}$via correlation ( $r=0.714$, $p<0.001$), agreement (mean difference: $0.01\pm 0.09$), and diagnostic performance (sensitivity: 89.5%, specificity: 93.6%, PPV: 89.5%, NPV: 93.6%, AUC: 0.95). FFR ${}_{\text{semi-streamlined}}$provided identical diagnostic performance with FFR ${}_{\mathrm{baseline}}$. Compared to FFR ${}_{\mathrm{baseline}}$vs. FFR ${}_{\mathrm{invasive}}$, FFR ${}_{\mathrm{streamlined}}$vs. FFR ${}_{\mathrm{invasive}}$had decreased correlation ( $r=0.64$, $p<0.001$), improved agreement (mean difference: $0.01\pm 0.08$), and comparable diagnostic performance (sensitivity: 79.0%, specificity: 90.3%, PPV: 83.3%, NPV: 87.5%, AUC: 0.90). ConclusionStreamlined models could match the diagnostic performance of the baseline with a full gamut of patient-specific measurements. Capturing coronary hemodynamics depended most on accurate geometry reconstruction and cardiac output measurement. 

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